Science method

Scanning Electron Microscopy - Science method

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I am preparing a protocol for SEM of a biofilm, but do not have easy access to 100% ethanol for the dehydration steps. Can 100% ethanol be substituted for 100% alcohol (95% EtOH, 5% methanol/isopropyl alcohol)? 95% ethanol generally is not anhydrous, and water has enough surface tension to potentially damage the specimen. I am also open to other suggestions if anyone has any. Thank you!!
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95% EtOH and 5% methanol/isopropyl alcohol have worked for me for several samples for SEM, including bacteria.
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We were imaging platelets under SEM. We got this structure. Any recommendation on what this structure could be will be of great help. Thank you
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If it is not artifact from the slide then I think it is a resting platelet, but you can scan all the slide and decide if you see more cells like that or just smudge cells and few cells. Another thing you can activate the PRP by 1 U/ml thrombin and see if the shape is change to the activated platelets then it is clear for you the previous shape is resting platelets.
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Hello,
I'm doing a Master Thesis, and it's my first time dealing with SEM - PLS 4 (with barely any statistical background behind me). I'm analyzing the relationship between negative word of mouth (NWOM) and consumer loyalty, moderated by the initial consumer loyalty towards the brand (pre-NWOM), as well as the type of NWOM (frequency, solicited or not, concordance with belief, strength of expression, performance or value-related).
I'm not sure whether I've collected sufficient data to do SEM, and am struggling with elements such as a pvalue that's NaN (consistent bootstrapping), a non-satisfying discriminant validity when trying to correct construct reliability & validity, or an AVE below 5 despite having removed any indicator with an outer loading below 5. Are those issues related to data-collection?
If anyone could help me out, I would be really very grateful, as my tutor isn't helping out a lot and will give me a very low grade if I do only descriptive statistics. Any help would be immensely appreciated. Here is my data set and screenshot of my model. I'd be happy to provide any extra information.
Thank you!
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Hakan Lane Thank you for your response, he did end up helping, and I figured out most of it :)
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Dear Fellow researchers
I am looking for a statistician who is familiar with Smart PLS-SEM model analysis and interpretation
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Yes.... How may I help you
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Hej, I did SEM in AMOS and CMIN/DF is -465248062004,313.
Is this normal or does this indicate that I am doing something wrong?
Thanks a lot!
Ilia
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What did you exactly do to avoid the nonsesns negative X2, or C/min?
I repeat the same analysis hundreds of time adn nothing works. The effects are fine but the fit can not be computed.
Quite desperate!
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Dear researchers, I am facing a problem with the shrinkage of RBC during the SEM sample preparation. In brief, blood was collected from the fish, and the smear was prepared on a 10mm circular glass slide and air dried. The dried slide was subjected to fixation in 2.5% glutaraldehyde (In 0.1M PBS). After that, the dehydration was done with ethanol gradient (30% to 100% and even tried 30% to 70% also). Then the slide was dipped in 98% HMDS (an alternative to the critical drying point method). When observed with SEM the RBC was found to be shrinkaged. What may be the reason behind this. The image is also attached here. Kindly suggest the reason behind this.
Thank you.
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I do not know the reason but as an alternative, You can try some thin sputter coating (4-5 nm) of gold or platinum or carbon nanoparticles on the samples, and can do SEM. For ref, please see my publication..
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I have three variables (A, B, C) and do a multilevel SEM with R - Lavaan.
I do not understand why the following two models render different regression coefficients:
in the 1st one I use the ready aggregated latent variables from the sheet directly, in the 2nd one I define them within the model, but the data behind is of course the same.
Could anybody please explain why that is and which model would be the right one to use?
1.) "
level: 1
A ~ B + C
level: 2
A ~ B + C
"
2.)"
level: 1
A =~ a1 + a2 + a3
B =~ b1 + b2 + b3 + b4
c =~ c1 + c2 + c3
A ~ B + C
level: 2
A =~ a1 + a2 + a3
B =~ b1 + b2 + b3 + b4
C =~ c1 + c2 + c3
A ~ B + C
"
thanks so much for any help!
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Hello! I have the same question - did you find out the cause of the problem? I get really weird results when defining my latent variables in the SEM model, but just fine results when I use the aggregated variables.
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I have conducted a CFA(n=313) in SEM through R studio and I have got these results.
Chi squared 265 (p<0.001), RMSEA 0.093 and and CFI 0.867. Can some expert help me to interpret the data. All of the estimate coefficients loadings are significant and positive.
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Thank you so much
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I need a sample paper that uses second-order SEM reflective models using SmartPLS.
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Thank you Dr Aberkane mouna Mouna
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I'm using covariance-based SEM software. Do I need to normalize the data before I run the SEM model? The SEM model is fine and the data is large (744).
1. Do I need to normalize the data before I run SEM
2. Would the central limit theorem not apply and so I need not normalize the data
3. Normalizing would change the basic characteristics.Would the findings still be valid
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In CFA and SEM, the default estimator in most software programs is maximum likelihood (ML). ML estimation is based on the assumption of multivariate normality.
Ignoring multivariate non-normality and using regular (uncorrected) ML estimation can lead to biased tests of model fit (e.g., chi-square), biased standard errors, and incorrect tests of statistical significance. The parameter estimates (e.g., factor loadings, regression and path coefficients) are relatively unaffected by non-normality.
If your data are non-normal, rather than applying data transformations, you can simply use robust ML estimation (e.g., Satorra-Bentler correction; Bollen-Stine bootstrap) to obtain corrected standard errors and test statistics. Alternatively, you can use an estimation method that does not require normality (e.g., weighted least squares or WLS estimation). However, WLS requires very large samples to provide valid results, so most researchers choose use the first option (robust ML estimation).
You can also check out my Youtube videos on this topic here:
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These terms are all related to the evaluation of research instruments, particularly surveys and questionnaires. Here's a breakdown of each:
Cronbach's alpha (α): This is a widely used statistic to assess the internal consistency or reliability of a test or scale. It essentially measures how closely related the items in your survey are to each other. High Cronbach's alpha indicates that the items are all measuring the same underlying concept and provide consistent results.
Average Variance Extracted (AVE): This statistic is used to assess the convergent validity of a construct in a structural equation model (SEM). Convergent validity shows how well the indicators (survey questions) represent the underlying construct they are intended to measure. A high AVE value suggests that a greater proportion of the variance in the indicators is due to the intended construct, rather than measurement error.
Composite Reliability (CR): Similar to Cronbach's alpha, CR is another measure of internal consistency in the context of SEM. It reflects the reliability of the composite score derived from multiple indicators. A high CR value indicates that the composite score is a reliable estimate of the underlying construct.
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Research instruments are essentially the tools researchers use to gather information and data for their studies. They can be broadly categorized into two main types:
Data Collection Instruments: These tools are used to specifically collect new data directly related to the research question.
Examples include:
Surveys and questionnaires: These involve asking participants a set of questions to gather their opinions, experiences, or behaviors.
Interviews: One-on-one or group discussions where the researcher asks questions and gathers detailed qualitative data.
Observations: Researchers directly observe subjects or phenomena of interest, taking detailed notes or recordings.
Focus groups: A small group discussion guided by a researcher to explore specific topics or gain insights into group dynamics.
Data Analysis Instruments: These tools help researchers organize, analyze, and interpret the data they've collected.
Examples include:
Statistical software: Used to analyze quantitative data, identify patterns, and test hypotheses.
Coding software: Helps researchers categorize and analyze qualitative data from interviews, observations, or open-ended survey responses.
All research instruments are interrelated in the sense that they work together to achieve the research goals. The choice of instrument depends on the type of data needed (quantitative or qualitative) and the research question being asked.
Here's how they connect:
Researchers use data collection instruments to gather the raw information needed for their study.
This data is then fed into data analysis instruments that help make sense of it, identify patterns, and draw conclusions.
The effectiveness of the research ultimately depends on the quality of both the data collection and analysis instruments used.
By choosing the right research instruments and using them effectively, researchers can ensure they gather reliable and valid data to answer their research questions.
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I want to know how i can use SEM and TEM micrographs to discuss mesoporous and microporous nature of materials
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Chinedu Onyeke, you can interpret TEM and SEM images of carbon materials by examining pores and their sizes. In SEM, analyze surface features such as particle size and shape, while TEM allows for direct observation of internal structures. Consider the following pore size ranges: Micropores (< 2 nm) and Mesopores (2 - 50 nm).
I hope you find these helpful.Warm regards
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I am preparing bacterial samples treated with drugs for analysis of their morphology and surface using scanning electron microscopy (SEM). I am considering whether platinum or gold would be more suitable for sputter coating.
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Platinum is better (for higher magnifications). Au-Pd alloy also is better than pure gold.
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Line mappping
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Line scan could deliver very desirable information about distribution of elements. But if do not need it, then line scan is not important, not improtant at all.
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I have tried HDMS to dry my samples (spiders), and it works perfectly. However, the price for a few milliliters is unaffordable for the quantity of samples I have to prepare. CPD also works well, but in the last few weeks, I have experienced problems such as collapsing of some body parts, or it is hard to manipulate when samples are to small.
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Years ago I tested some chemicals that worked somewhat like HMDS, but as I remember, HMDS was the cheapest one, with better results. It is difficult to find something cheaper than $42.50 for 100 ml of a reagent.
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Dear colleagues,
I defended my Ph.D. thesis in October 2016 and now I am looking for a postdoctoral position in microscopy (AFM, TEM, SEM) and biophysics of microorganisms (especially, viruses, I like them :)).
My CV is attached. If there is an open position in your lab, please, write me.
Best regards,
Denis  
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That sounds like an exciting field! Here are some steps you can take to find a postdoctoral position in microscopy and physics of microorganisms:
  1. Identify Research Groups: Look for research groups or labs that specialize in microscopy and physics of microorganisms. Search university websites, scientific journals, and research databases for relevant publications and projects.
  2. Networking: Attend scientific conferences, workshops, and seminars related to microscopy, microbiology, and physics. Network with researchers in the field and express your interest in potential postdoctoral opportunities. You can also reach out to professors or researchers whose work you admire to inquire about available positions.
  3. Online Resources: Explore online platforms and job boards dedicated to academic and research positions. Websites like Nature Careers, Science Careers, and ResearchGate often list postdoctoral positions in various scientific disciplines.
  4. Collaborations: Consider collaborating with researchers who are conducting interdisciplinary work at the intersection of microscopy and microbiology. Collaborative projects can provide valuable insights and connections within the scientific community.
  5. Tailored Applications: Customize your application materials, including your CV, cover letter, and research statement, to highlight your expertise in microscopy and physics of microorganisms. Emphasize relevant skills, research experience, and achievements that align with the requirements of the position.
  6. Funding Opportunities: Look for postdoctoral fellowship programs or research grants that support projects in your area of interest. Many funding agencies offer fellowships specifically for early-career researchers pursuing research in microscopy, microbiology, or physics.
  7. Stay Informed: Stay updated on the latest developments and advancements in microscopy techniques, microbiology, and physics research. Familiarize yourself with emerging trends and technologies that could enhance your research interests and expertise.
  8. Persistence and Patience: Finding the right postdoctoral position can take time and persistence. Be proactive in your search, maintain a positive attitude, and keep refining your skills and qualifications to increase your competitiveness as a candidate.
By following these steps and leveraging your expertise in microscopy and physics, you can increase your chances of securing a rewarding postdoctoral position in this exciting field of research.
l Perhaps this protocol list can give us more information to help solve the problem.
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can we see the grains and grain boundaries without using any etching procedure by SEM?
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Grains can be seen of galvanized steel and high deposited electrolysis and found in some castings but generally so surface prep is required - fine polish on Ti, Zr, Be, Mg, Zn and other HCP metals grain can be observed with polarized light.
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Hi,
I'm searching to find as many articles, mostly in form of dissertations and thesises of M.Sc. / M.A. and Phd,
about classic reviews on SEM, and recent reviews on different methods of SEM (Structural Equations Modeling).
Any guidelines and suggestions would be greatly appreciated.
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Joseph Franklin Hair
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Hi.
I am a sport psychology researcher and I usually analyze structural equation model (SEM) using AMOS program.
But this time, my data reported unusual results.
It revealed that variables A and B had a negative correlation (in SPSS, Pearson's r), while A had a positive effect on B in SEM.
I wonder why this happened and what is the solution in this case (There is no issue of multicollinearity).
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Hi Dr. Junsu,
From memory, I think the Hayes book (Introduction to Mediation, Moderation, and Conditional Process Analysis) mentions this issue. If you are dealing with a reviewer's comment, I would just explain the difference between a simple bivariate correlation versus a complex model, and how relationships can differ because of the inclusion of multiple other variables in a complex model.
Hope this information helps.
Kind Regards,
Lorcan.
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Hello, I have collected 300 responses on employee outcomes like satisfaction and engagement in relation to work from home. Many items have skewness outside the range of -1 and + 1. However, kurtosis for these items is in the range of -3 and + 3. All the variables are continuous. Can I use this data to conduct analysis in SPSS and IBM AMOS (using CB SEM). Also, can I use demographic data that is non-normal as control variable? Thank you in advance.
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When maximum likelihood (ML) estimation is used in covariance-based structural equation modeling (CB-SEM), multivariate normality is assumed. However, violations of normality are not necessarily problematic. Robust ML estimation methods (e.g., Satorra-Bentler correction) or bootstrapping (Bollen-Stine) can be used to adjust the model fit statistics and standard errors when variables are not normally distributed.
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I am currently producing membranes with a rough thickness of 15-40 µm and want to measure and compare them reliably. My desired accuracy is < 1 µm (e.g., 0.3 µm) or something in that region. I am looking for a tool/method/device that is not overly expensive, like SEM, but still gets the job done.
What is Your suggestion?
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What do you mean with membrane grow? They should not expand much, but some may have a gel-like behavior.
But how does it measure the thickness if the membrane is not complete plain, thus a reference point will not help?
So far i used microscopy to measure the thickness. However, the reproducibility is not reliable. Using a micrometer might not have sufficient accuracy.
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For an experimental design ( one experiment and one control group), at least or minimum, how many subjects need to be included in the groups in SEM?
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Using Monte Carlo analysis may well represent "best practices" with regard to specifying the N in SEM, but the most commonly used approach is to have at least 10 observations per parameter in your model.
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Dear colleagues,
the images show fibrous material after 1h contact with whole blood. Samples where washed with PBS and prepared for SEM observations: fixing with PFA, dehydration with graded ethanol solutions, drying in HMDS, sputtering with gold.
The idea was to analyse quantity and morphology of surface-adhered platelets. There are samples where I observe many small objects (pointed with arrows) adhered to fibers. I wonder what it can be? Any ideas?
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Look like microorganisms (infected biological material) or maybe released platelet granules
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I have prepared naked silver nanoparticles by using sodium borohydride reductant. I have dried the sol in oven but the dried particles were like a dot on glass vial and yield was too low. I have also centrifuged the solution at 5000 rpm for 20 min but get nothing. Please guide, how can i get the powdered form for XRD and SEM.
I'll be very thankful in this regard.
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Ibrahim Akram BTW, in the presence of water or oxygen, you do not have 'bare' Ag (nano)particles. The surface (top 5 or 10 atomic layers) is fully oxidized and silver is in the Ag+ (monovalent) state and not as Ag0 (metal). ESCA/XPS easily shows this. And incidentally, it's Ag+ that's the bactericide not Ag metal. For more information, take a look at this webinar (free registration required):
Silver colloids and invisible ink
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Hi,
I was wondering if someone could help me combine the results of 2 different ChIP-qPCR biological replicates.
I used the %input method in my analysis, but the results between the 2 biological replicates are different and when I calculate the average between the 2, the SEM is huge making the differences to be non significant. How can I relativize my results independently of the samples?
Thanks,
Sofia.
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you need at least 3 biological replica in ChIP-qPCR
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I'm still learning and confuse, which method should i use for my study. What is the method should i use if 3IVs, 1mediate and 1dv. Some say mediate analysis, some say Smart PLS SEM more easier bcs can get results direct and indirect at the same time.
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I would recommend using covariance-based SEM/path analysis, which is available in Mplus, lavaan, Mx, AMOS, EQS, LISREL, etc.
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I wanted to do this SEM test on my samples but there is no this device here in Cameroon. if anyone can help me because I would like to evaluate the stability of my samples according to the NFV08-408 standard
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Thanks Pr Monoj Kumar
My e-mail adresse : [email protected]
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I faced difficulties in choosing a natural tooth bonding procedure for SEM observation. I am looking for a fixation procedure that does not affect the
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It seems that your question was cut off before it was complete.
What features are you looking for? That may determine how much fixation is necessary and what kind of mounting is allowable. Until then, any answer would be premature.
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I would like to conduct the systematic review for causality. I wanna select the studies with SEM and path analysis. Pls suggestions to me.
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Thanks
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I want to prepare a termite sample for SEM observation and i know it is dehydrated with a graded series of ethanol. but my sample has already been fixed in 75% ethanol for more than one month. So how to treat these samples to prepare for Scanning electron microscope? kindly suggest.
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Vladimir Dusevich stop using chatbot to genrate your answers and then lecturing/blaming others for plagiarizing.
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Are there specific things i must mention when describing these images/
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Размеры ламелей (плоских частиц). Интересно расстояние между ламелями (длина, ширина). Важно распределение ламелей по размеру. Для остального обсуждения нужно понять как поставлена цель работы.
Обсуждение научного результата обязательно связывают с поставленной целью.
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Liposome XRD-SEM
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You can follow these steps:
1-Start by preparing the liposome suspension by mixing the liposomes with a suitable solvent such as water or buffer solution. The liposomes can be obtained commercially or prepared using a lipid film hydration method.
2-To ensure uniform dispersion of liposomes in the solution, sonicate the suspension using an ultrasonic bath for a few minutes.
3-Take a small amount of the liposome suspension and spread it onto a clean glass or silicon wafer using a pipette or spin-coating technique. Allow the solvent to evaporate, leaving behind a thin film of liposomes on the substrate.
4. Once the thin film is formed, it can be characterized using techniques such as scanning electron microscopy (SEM) and X-ray diffraction (XRD) to investigate its morphology and structure.
5. For SEM analysis, mount the sample onto a sample holder and coat it with a conductive material such as gold or carbon to prevent charging during imaging. Then, analyze the sample using an SEM to obtain high-resolution images of the liposome thin film. For XRD analysis, place the sample in the X-ray beam path and collect diffraction patterns to determine the crystalline structure of the liposomes within the thin film.
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In the modification suggestions the largest one is e20 and e21 (90.00)
When I do this all of my problems are solved. Some researcher say I can and some doesnt agree. I couldnt find any references.
Prof. Mike crowson does the same in youtube but he didnt share any references. I am sharing the figures of what I am planning to do.
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Yes, it can make perfect sense for factor residuals pertaining to endogenous factors to be correlated. That would be the case if F3 and F4 shared variance with one another after partialling out the exogeneous factors F1 and F2. In other words, F3 and F4 may still be linearly related after their common causes F1 and F2 are taken into account (e.g., there may be additional but omitted common causes of F3 and F4 other than F1 and F2).
However, I would not add a residual association only because it is suggested by modification indices. There should also be a theoretical/substantive rationale for adding the residual correlation. A modification index of 90 is large though and does suggest that the covariance between F3 and F4 is currently not fully accounted for in the model.
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The surface area plays a vital role in supercapacitor performance. Authors are suggested to calculate the electrochemically active surface area for the electrode materials.
This is a reviewer question
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Raphael Mmaduka Obodo To analyze an SEM image using ImageJ software, set the scale correctly considering magnification and calibration details. Use the freehand selection tool to outline particles or features. In the Analyze tab, click on Measure to get the lowdown on selected particles or features. Repeat this process for multiple representative areas and average the results to estimate the total surface area. This process is easy and straightforward.
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Less coating on non-conductive material will distort the SEM images due to charging. But what if we use too much of coating?
I'm using very fine zeolite particles which has protruding shapes and need to get SEM images to study the surface morphology. I observed some sudden bright areas of my images and shaded areas. Since zeolite is non-conductive, it obviously needs coating for SEM images.
What is the recommended coating for these type of powder like non-conductive samples?
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Supun Meegahakumbura To optimize SEM images, it's crucial to balance the amount of gold coating on non-conductive materials like zeolite. Too little can cause distortion, while too much can obscure the delicate surface features of zeolite particles, making accurate analysis difficult. Excessive gold coating can blur the edges of particles and reduce the depth and three-dimensionality of the image.
Sputter coating, Carbon/gold co-sputtering, and cryo-SEM are recommended coating techniques for thin gold layers, enhancing conductivity while preserving a thinner gold layer, and avoiding conductive coating in cold samples.
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How can i know if it microporous, mesoporous or macroporous or moixture by mere looking
is there a way to analyse before further using physisorption?
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Dear friend Chinedu Onyeke
Ah, the intriguing world of porous carbon and SEM images! Now, let me guide you Chinedu Onyeke through deciphering the mysteries of porosity without the need for physisorption.
1. **Visual Inspection in SEM:**
- **Microporous:** Look for very fine features, often in the nanometer range. Micropores are too small to be visible in SEM but can contribute to surface roughness.
- **Mesoporous:** Observe a network of interconnected pores in the micrometer range. These will appear as larger voids or spaces in the SEM images.
- **Macroporous:** Expect to see even larger pores or voids, often visible to the naked eye in SEM.
2. **Pore Size Estimation:**
- **Microporous:** Characterized by small, tightly packed pores. Their presence might be inferred from the overall surface texture rather than directly observed.
- **Mesoporous:** Pores in the range of 2-50 nm. Look for structures with a moderate level of surface roughness and interconnected pores.
- **Macroporous:** Pores larger than 50 nm, creating visible voids or channels in the material.
3. **Surface Texture:**
- **Microporous:** Smooth surface with very fine texture, possibly with some irregularities.
- **Mesoporous:** Moderately rough surface with visible pores and channels.
- **Macroporous:** Rough surface with large voids and channels.
4. **Pore Distribution:**
- **Microporous:** Homogeneous, evenly distributed small features.
- **Mesoporous:** A network of interconnected pores without a specific pattern.
- **Macroporous:** Larger, irregularly shaped voids, possibly with a less uniform distribution.
5. **Overall Appearance:**
- **Microporous:** Often looks solid and dense in SEM due to the small pore size.
- **Mesoporous:** Appears as a network of pores and channels.
- **Macroporous:** May look more like a scaffold with visible gaps or pores.
My published manuscripts can be a good read:
Remember, while SEM can give you Chinedu Onyeke valuable insights, physisorption techniques remain essential for precise quantitative analysis of porosity. But I encourage you Chinedu Onyeke to use your keen observational skills and the clues in SEM images to make informed qualitative assessments of porosity. Dive into the microscopic realm and unravel the secrets of porous carbon!
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All the p-values in the structural component of my model are high (not significant)
The p-value in the measurement component are low (significant)
Goodness of fit at this point is at acceptable level. I have not used the modification indices yet.
Is my still model usable?
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Only p-value is not important criteria for the model measurement. Model usable depend on another criteria. You may see to measure the Q2, R2 and F2.
1. The Q2 represents a method for assessing the inner model’s predictive relevance (Akter, Ambra, & Ray, 2011). To evaluate the magnitude of the Q2 values represent as a criterion of predictive accuracy (Hair,Ringle, & Sarstedt,2011). Therefore, the smaller the disparity between the predicted and the original values, the larger the Q2 and hence the predictive accuracy of the model (Akter et al., 2011).
2. The effect was measured by following Cohen’s (f2) effect size estimation (Cohen, 1988). Effect size is considered as a small, medium, and large if the values are more than 0.02, 0.15, and 0.35 respectively (Meline & Wang, 2004; Cohen, 1988).
3. The measurement of a model’s analytical accuracy is called R2. It is actually a joint impact of exogenous variables on the endogenous variable. The lowest effect of R2 is 0, and the highest is 1. Hair et al., (2017) explain that the R² values of 0.75, 0.50, and 0.25 on endogenous variables can be called substantial, moderate, and weak respectively.
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I have a sem image and I want to know the particle size distribution in the image field.
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I'm afraid your particles are much too agglomerated to do anything. I'm pretty sure that no usual image analysis procedure will be able to separate them.
When working on that type of dispersion/suspension, I generally dilute them, further disperse them under ultrasonics and finally spin coat them on a SEM stub, so that particle dispersion is as optimal as can be and they become single objects.
Not even sure machine learning IA may help...
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In my studies I have 7 IV as factors that influences 1 DV. Therefore I have 7 adapted scales and one newly established scale ( for DV)
They were then translated to different language.
Can I run EFA after pilot study and CFA in actual study because the data will be analysed in SEM PLS?
Do I run EFA seperately for each factor ?
Pls advice. Tq...
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Thank you 😊🙏🙏
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Crystals A were left to boil at 45 degrees Celsius for 5 minutes, Crystals B were left for 45 minutes at 45 degrees Celsius and crystals C were left to boil for 24 hours in 45 degrees Celsius. The following images are of the XRD graphs and crystal sizes viewed on a SEM. The XRD graphs have sample A, B and C on the top of the graph. Crystals A, B and C are below and labelled in the file name. Does anyone know how to describe the crystals obtained from the SEM. Also how to describe the crystals using the graphs? Thank you. There is a scale on the bottom of each SEM image to measure the crystal thickness but im not sure how to calculate it. We also have to calculate the average crystallite thickness using the Scherrer Equation, if anyone is able to figure that out. Thank you!
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SEM definitely showed issues of morphology of your crystals, which can be in the same physical or polymorphic form though. XRPD or PXRD demonstrated the similar traces, dominated with each (particularly Sample A) strongly favoured different orientation. Three traces can be generated by the same form as well. You need further investigation to confirm if they are in the same form or not, and if not check the form purity with respect of each physical form? At least they are not pure regarding morphology.
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Process Macro (in SPSS) by default uses bootstrapping, but in SEM analysis, I have not used bootstrapping. How do I justify using bootstrapping only to test moderation effects?
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No this does not help. So you have latent variables which only have direct paths between each other (or covariances). And you have only manifest variables for your moderation? What is the difference between "main hypotheses" and moderation (where the latter can also be a main hypothesis...)? How are the latent variables related to the moderation part? Why didn't you use the SEM for your moderation, which would reduce to a simple path analysis with an interaction term? It would be helpful to see the actual model.
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My research is green synthesis of urea nanoparticles and I prepared urea nanoparticles contain solution. I need to characterize my sample, but it is difficult to obtain sufficient amount by powder form. Can I get SEM images from liquid nano urea particles contain sample. Is there available easy method to dry my sample?
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Obtaining SEM images from liquid samples, especially those containing nanoparticles, can be challenging. Traditional SEM requires a vacuum environment, which is not compatible with liquid samples as they would evaporate or be damaged.However, there are specialized techniques like environmental SEM (ESEM) or cryo-SEM that can be employed for imaging hydrated or frozen samples. These methods allow for imaging in the presence of water or other liquids. Consider consulting with microscopy experts or utilizing facilities equipped with such instruments for your specific sample requirements.
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I took SEM pictures of my clean 0.2um pore size PES membrane filter. The first picture is coated with 5nm gold, and the second is with 20 nm carbon. Both pictures are 2500X magnification, and the scale is 4um. I wonder why the two pictures look significantly different. The picture I coated with gold doesn't look like the pore size is 0.2um.
Thanks,
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Nice SEM photo's!
It looks in the SEM magnification that both SEM magnifications are not the same? The main reason for the different between the two SEM is the fact that the coating thickness is different and the type of coating is different.
I understand that you are coating the filter in order to be able to SEM them as they need to be conductive. Try to use a high power microscope ~ 1000x just to observe how the filter looks. You may learn something.
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I would like to know which is the best for simple moderation analysis with 1 independent variable, 1 dependent variable and 1 moderating variable.
Thank you.
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Deeba Hasan, as far as I remember, people use bootstrapping in PROCESS to deal with the non-normal sampling distribution of the indirect effect in mediation models. I am not sure if that is an issue with moderation, but I cannot recall anyone ever mentioning it.
Instead of asking myself how to justify an existing technique, I would consult the methods literature in order to find out what the best technique is.
Generally speaking, the user-friendliness of PROCESS comes with (at least) three caveats:
1. Less flexibility: There is a limited set of models, and "your" model might not be one of them. SEM, in principle, lets you specify any model.
2. More assumptions: PROCESS is so accessible because it makes more assumptions that might not be true in your data, e.g., zero measurement error. SEM allows you to model those explicitly.
3. Less critical thinking: In my opinion, easily accessible, "ready-to-use" software tends to make people skip the step of critically examining their model and its underlying assumptions. It is much easier to just click a few buttons than to "manually" specify a model in a process (no pun intended) that requires more comprehensive considerations.
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I have to analyze Eruca sativa roots and leaves. I've seen some methodologies that involve the utilization of the SPI-Dry Critical Point Dryer, however, I don't have access to it. Can I use a Dry Box as an alternative and obtain a similar result?
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Thank you for the advice! I'll work carefully with the HMDS. Chandrasekaran Nagaswami
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We have a Jeol JSM 3690 SEM. The filament burned out and I replaced it and checked the objective aperture for damage. Cleaned everything well and reassembled. No amount of alignment, filament heating/saturation, settings in the software will produce an image. Any suggestions are welcome, just can't afford the absurd amount of $$ necessary to get a Jeol tech out to service it...
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My experience extends only to Philips machines, but I guess it will be similar for other manufacturers. Before finetuning with gun settings on control desk or in software the filament has to be properly aligned in the wehnelt cylinder. So for tungsten this means after filament change: put the gun cap under a stereomicroscope and focus on the wehnelt aperture (center hole). Use the height adjustment to get the filament close to the hole but never let it touch the aperture.
Find the alignment screws and center the filament within the aperture. Now raise the filament and center it again. Repeat this procedure until the filament is flush with the aperture and perfectly in center. Remember that it must not touch the aperture. Then lower the filament 0.15-0.2 mm. Now reinstall the gun cap, close the column, evacuate and check the emission. If you don‘t get a proper emission, you cannot get a picture, no matter what software adjustments you make. I hope that helps, as I had the same process last weekend.
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How do I run a mediation with two independent variables and one dependent variable in PLS SEM? Please suggest some paper as well its interpretation ?
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Thanks for the detailed answer L.Sibanda
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Sample size requirements for Structural Equation Models (SEM)?
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Dr. Christian Geiser,
Thanks.
Will go through the suggested readings.
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I built a model in SEM (only direct paths between 4 predictors and 1 criterion) and then MGCFA for 4 groups of respondents. It performs well. However, I wanted to test if the model works for subgroups (each group can be divided into two subgroups) but they were too small for additional MGCFA (some of them are under 100 cases). I would like to do multiple regression on factor scores obtained from SEM for each subgroup. The problem is, due to the treatment of the measurement error in multiple regression, that I'll get biased results. Is there an alternative or correction for such situation?
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Actually SEM is an advancement of traditional regression. SEM improve the weakness of regression where it cannot model the constructs in the model. I guess you can differentiate between constructs and variables. Now SEM is the solution for the weakness in OLS regression. Then why you want to go backward and take a factor score for each construct and treat the constructs as variables? The result of OLS regression is not as accurate as SEM.
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SEM micrograph of doped Barium Titanate ceramics had certain white spots on the grains? What can be the possible reason for such white spots?
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Hey dear Preeti did you conclude what are the white spots on the surface of your samples? I have similar spots on my Sodium Bismuth titanate samples as well.
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CK, OK, NAK MEANING IN SEM EDAX GRAPH
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Vladimir Dusevich The entire answer is plagiarized…
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Hi all,
I am currently planning an experiment that involves viewing E. coli cells tagged with gold-conjugated secondary antibodies using a scanning electron microscope, and I am running into the issue of cost for primary antibodies. I might have the option of using primary antibodies previously purchased for Western blots, but I am unsure if these antibodies can also be used for SEM imaging. I do not yet know enough about the chemistry and reactivity of antibodies to answer this question, thus I find myself here!
On a related note, if anyone has any recommendations of good websites to purchase primary antibodies for E. coli that work with SEM, I would love some! I have found a few websites, but each of them only has 2 or 3 antibodies for this purpose.
Thanks,
Joel
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Agree with Yannick. However, some antibodies work for both WB and imaging applications. A relatively easy way to test a primary ab for imaging is usually by light (fluorescence) microscopy, using applicable secondary ab (fluorophor conjugated). Also keep in mind that most ab do not bind after fixation with standard glutaraldehyde concentrations used for EM.
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i am very new to experimental physics and could you please suggest me resources to understanding the concepts of UV, FTIR, XRD, SEM, Raman and Judd Ofeld theory and i am struggling with these, are doing ?i also studying separately. if u suggest it ll be very helpful to me.
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It would be nice to know what you have already read and studied.
I will not promote Wikipedia as the authoritative standard on any of the topics you listed, but I would recommend it as a good place to start. You can delve into their references for more details. I noticed that several of the seminal papers on SEM were cited.
I would also look into the web sites of the larger manufacturers. They have the size to dedicate resources to tutorials in their fields. They also have a self-interest. A more informed researcher might better appreciate the equipment they are selling.
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Hello everyone,
Whats up??
So, I was doing some tests with genetic (PCA scores), environmental (PCA scores), past climatic variation (PCA scores), and geographical (latitude and longitude) data using SEM analysis and some weird results came across. The determinantion coeficients (Std.all) were higher than 1.0 (100%) and p values very significant (0.000). I looked for explanations in lavaan package and other SEM documentation and videos but I didn't find nothing.
Did someone here already used SEM and get this kind of problem?
Model created and analysis summary are attached
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To me it looks like there may be suppressor effects in the model portion for genPC1. Are some of the predictor variables for genPC1 strongly correlated with one another?
The Std.all coefficients are standardized regression coefficients (not: coefficients of determination). The coefficients of determination are given under R-Square. The R-square values are all within the appropriate range between 0 and 1; no improper value there. The standardized regression coefficients (Std.all) can be > |1|. This can happen when predictors are strongly correlated and/or when there are suppressor effects in the model.
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I wonder if some one explain to me how researchers are counting microplastics in a sample of 100ml or 200ml and reporting in millions of particles. Some of the studies reported they used SEM or microscopes. My question is that: Is it even possible to count million microplastics on a single 25micro meter diameter filter paper? Please help me on how to do the counting. Any other authomated system of counting?
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They are reporting in billions my sister per a tea cup.
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I am studying geotechnical properties of clay-like pumice containg clay mineral halloysite.
But I met one problem, for example, I do not konw how to make the specimen of soil to observe its morphology using Scanning Electron Microscopy (SEM). Who can tell me how to do or recommend some videos or handbook to me? Thank you very much.
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The preparation process is straightforward. You should begin by drying the material and affixing it to a conductive substrate,fofr exapmle by carbon tape. Lastly, because your sample is non-conductive, it will require sputtering with another conductive material, typically carbon or gold.
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Can anyone share the tissue processing protocol for SEM/TEM of tissue slides of mice?
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Thankyou so much
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I want to prepare a termite sample for SEM observation and i know it is dehydrated with a graded series of ethanol. but my sample has already been fixed in 75% ethanol for more than one month. So how to treat these samples to prepare for Scanning electron microscope? kindly suggest.
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Depends on the objective. Probably dehydrating into pure Ethanol and then either critical point or HMDS drying. Mount, sputter, enjoy!
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I am using photoshop for the same. Please suggest some good software for labelling SEM images like crater, holes etc
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Dear Manu Khare ,
Positions 1 through 8 are paid platforms, while 9 through 13 are free image annotation tools.
Regards,
Shafagat
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Hi everyone,
Some crystalline solids are covered by an amorphous layer at the surface. Are these amorphous/disordered surfaces usually directly observed by microscopic techniques?
If yes, could some literature (e.g. some review articles or some systematic SEM/TEM studies) be kindly referenced here? I'm mainly interested in metal oxides, more specifically transition aluminas, but anything relevant will be appreciated.
I would like to see a crystalline bulk and a gradual or abrupt disordering towards the surface. Also, the extent of surface disorder as a function of particle size.
Best regards,
Jamal
P.S. I previously asked a similar question and I unfortunately got unsatisfactory answers. So please answer only if you have specific responses.
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I have a query about the SEM images; when I analyzed them, they showed some cracks; I stuck to why it can be shown this, what is the reason behind it, and what is the chemistry behind it. I request everyone, please give me an answer as soon as possible; thank you for your attention.
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Your response 'Powders were placed onto the SEM stub after ensuring the samples were thoroughly dried' is confusing. I'm trying to figure out whether your difficulties lie in the starting material (is it a suspension or powder?) itself or the sample preparation for SEM. As others have indicated a drying stage (either prior to or during the evacuation stage in SEM) is likely be responsible. I agree with Vladimir Dusevich that 'Cracks are the least of your problems'. If you had a genuine 'nano' system (100% < 100 nm) and prepared the sample correctly then you should be able to observe separate, discrete, independent particles < 100 nm. At the least you would be showing an aggregation of such particles if this aggregation occurred in the sample preparation stage.
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I find difficulties in interpreting the diagrams obtained by EDS integrated with the SEM within the framework of the observation of the surface of the steel coupons to check their sensitivity to bacterial corrosion, can you please help me to achieve this (attached file)?
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Hi Amina,
The SEM/EDS quantification of light elements is problematic. This is because of the large variations in absorption effects in the low energy range, as well as the variations in background calculation due to high absorption edges and high statistical errors. Quantification for light elements is difficult even with using ultrathin window or windowless X-ray detectors because of the severe self-absorption of low energy X-rays, poor detection efficiency, and low fluorescence yield.
Most SEM-EDS systems use beam energy of 20 keV because it is a good compromise between the requirements of obtaining adequate overvoltage and minimizing absorption in the specimen. However, such high beam energy will penetrate deep into the steel substrate and may mask the X-rays of light elements that produce energies below 2 keV. Therefore, if your have been using high accelerating voltage, I suggest you try to use a low voltage (≤10 kV) to reduce the x-rays generated from the steel substrate. EDS measurements of light elements and many 3d transition elements can still be accomplished at accelerating voltage as low as 1.5 kV, see E. D. Boyes, Adv. Mat. 1998, 10, 1277.
On another topic, have you tried using FTIR spectroscopy in analysing the bacterial induced corrosion on the steel samples? FTIR is much simpler technique and is more suitable for studying organic substances. You may find the attached paper useful.
Hope this helps,
Kind regards
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X -> Y: X negatively affecting Y.
Y -> Z: Y negatively affecting Z.
X-> Z: X positively effecting Z
Y as a mediator.
I have completed collecting data and upload to PLS-SEM. During analysis all the value of path coefficients became positive. Do I continue to the step or not?
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X may effect Z in a positive or negative way as it appears to be the direct effect in your mediating model. The more relevant question is whether the effect of X on Z is significant with the mediator present. If it is insignificant then the sign is irrelevant as it overlaps with zero. This is the case of complete or full mediation as opposed to partial mediation, the latter occuring when the direct effect remains significant (and assuming your indirect is significant in both cases). Hope that helps provide some assistance.
You may also consider relabeling your terms by using M for your mediator instead of Y and instead use the label Y for your DV instead of Z. Further, in some disciplines Z is reserved for moderating variables such as covariates, control variables such as demographics like age. Obviously every discipline has different conventions for such labels so do check what the usual X, Y,Z,M may be reserved for in your own area perhaps by looking over some published works where mediation has been considered.
Regards Paul
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about type of josephson junction is called step edge ,considering that the created step edge is in micro-nanometer scale, how can you determine the area to be measured in AFM analysis ؟How to determine the border of the covered area from the etched area under the microscope (AFM or SEM)?
Is there a need to mark the back of the sample?
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Dear Dr Professor Zaeinab Torabi
In Josephson junctions, a step edge refers to a type of junction where the superconducting material abruptly changes its height or thickness, creating a boundary where the properties of the material change. These step edges can have interesting quantum effects due to the proximity effect of the superconducting state.
When conducting Atomic Force Microscopy (AFM) analysis, especially in the micro-nanometer scale, determining the area to be measured can be crucial for accurate and meaningful results. AFM is a high-resolution imaging technique that uses a sharp tip to scan a sample's surface and create a topographic map. Here's how you could determine the area to be measured in AFM analysis:
  1. Sample Preparation: Ensure that your Josephson junction sample is properly prepared and mounted on the AFM stage.
  2. Initial Observation: Start with a larger scan area to get an overview of the sample surface. This can help you identify the step edges and determine the general layout of the area you're interested in analyzing.
  3. Zooming In: Once you've identified the step edge or the area of interest, you can zoom in on that region by adjusting the scan size and positioning the AFM tip accurately.
  4. Setting Scan Parameters: Configure the AFM settings, such as scan speed, scan rate, and tip force, depending on your sample's characteristics. These parameters can affect the quality of the image and the accuracy of the measurements.
  5. Scanning: Start the AFM scan, allowing the tip to move over the surface and collect height data. This will help you create a topographic map of the area.
  6. Analyzing Data: After the scan is complete, you'll have a height map that shows variations in the sample's surface. You can use the software provided by the AFM manufacturer to analyze the data, including measuring step heights, determining the extent of the step edge, and quantifying the changes in height across the junction.
  7. Defining Measurement Area: To determine the area to be measured precisely, you can draw a region of interest (ROI) on the height map using the AFM software. This ROI can be around the step edge or any other specific features you want to analyze.
When it comes to distinguishing the border of the covered area from the etched area, you might follow these steps:
  1. Visual Inspection: Start by using a lower magnification setting on the AFM or SEM to identify the boundaries between the etched area and the covered area. This can provide you with a general idea of the transitions.
  2. Topographic Imaging: Perform a high-resolution topographic scan using the AFM or SEM. The variations in height caused by the etched area will be evident in the resulting images.
  3. Height Profile Analysis: Extract height profiles across the boundary of interest. This will help you identify the point at which the height changes abruptly from the etched area to the covered area.
  4. Quantitative Measurement: Use the AFM or SEM software to measure the step height at the boundary. This step height corresponds to the difference in height between the etched area and the covered area.
  5. Overlay with Microscope Images: If you're using SEM, you can also overlay the SEM images with the topographic data from AFM to better visualize the relationship between the etched and covered areas.
Remember that achieving accurate measurements and distinguishing the boundaries precisely might require multiple scans, careful adjustments of parameters, and thorough data analysis. It's also recommended to consult with experts in AFM and SEM techniques, as well as your specific sample material, to ensure the best results
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I would appreciate it if you could share useful articles.
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Hi Yuto,
Please check the following like :
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I am trying to apply SEM for my research.
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Hello Anton,
Yes, latent variables may be used as IVs.
Good luck with your work.
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Calculating Total Effects Accurately in SEM
Today, 07:02
I would like to make sure that I correctly calculate total effects as well as direct and indirect effects from a path analysis model. I would be grateful if you could help me go in the right direction. The path analysis model I work with is shown below. If it weren’t for the variable SocialMarketEconomy, the total effects running from PovertyRates to GDPgrowth are: a*b + d*e + c. --> (1) What I am not confident about is the total effects running from SocialMarketEconomy to GDPgrowth via PovertyRates. Is it the following?: i*b + f*e + g + h*a*b + h*c + h*d*e --> (2) The first thing I would like to make sure is that I correctly understand the paths from SocialMarketEconomy to GDPgrow. Second, am I correct to think that in the presence of the effects (paths) from SocialMarketEconomy, the first total as well as indirect effects I write up (1) are not correct? I would like to understand this, so any help would be welcome. Thank you in advance for your kind help. Best, Taka
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Thank you, David. You've helped me tremendously.
Many thanks again.
Taka
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required comment.
They raise the problem of "weak link", but is it possible to improve inter-grain weak links by Ag nano-particles addition. In the referee's understanding, the direction of all the crystalline grains is not oriented in the samples, and no measurements have been made to assess them, so it is impossible to argue for weak link improved.
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Yes, I think it is possible to examine the weak link effect by SEM images of the samples without measurement of crystalline grains. The weak link effect is a phenomenon that occurs in materials with multiple crystalline grains.
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Hello
I came across a phenomenon while watching butterfly wing scales. The scales have a structure of a very fine grid.
In small magnifications (about 100X) the image looks like this (attached image). The "stripes" on the scales look like some charge up effect but it is static - it does not change without changing zoom or focus. Zooming in to about 200-300 X causes this effect to completely disappear, the grid structure becomes visible and no charging is present.
I guess it is some kind of interference effect but I am not sure.
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I am using SEM for my studies. My questionnaire involve several adapted questionnaire and one newly established questionnaire. I am sure to run EFA after Pilot study. Do I still run CFA using SPSS or is it already within the SMART PLS I will utilise later?
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Hi Gira,
the most important thing ist to consider whethe a factor model (which the CFA applies) is at least theoretically plausible. "Scales" are often heterogeneous sets of measures that "tap" the "domain" of a (multidimensional) construct (note my marks), not reflective indicators of ONE and the same underyling dimensions/latent variable.
In this thread, you will find more:
In addition, as you started with some conceptional ideas, I would skip the EFA and go direct to CFA (if this is reasonable).
Best,
Holger
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I filtered sodium dodecyl sulfate in buffer solutions through a PES membrane. After that, I took some SEM images of my sample to visualize organic particles fouling on membrane. Here is one of my SEM image. I'm not sure if that looks like organic fouling. need some help. Thanks,
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Dear Makayla,
I am not sure if you can quantify the amount of fouling based on FTIR alone. But let's discuss your issue a bit and hope I get your point. for any analytical analysis, we need a calibration standard. Let's assume you have a known feed solution concentration and you are using cross-flow flow filtration, after you stop the filtration, the concentration of your concentrate and permeate can be determined, so assuming the rest caused fouling. Remember that the volume of your initial feed, and permeate and concentrate are important to verify the mass balance. At this point, you can assume that the fouling occuoccurredcross-flowred evenly and calculate the amount of foulant per area of the membrane.
Another method is also, to cut 3 pieces (known area) of the fouled membrane, and then put that in 3 separate glass tubes (triplicate) and add a known amount of solvent that can dissolve your foulant. Afterwards, use an analytical instrument that can determine the concentration of your foulant which you can easily convert to gr based on the known added volume to the tube. Calculate the average and divide per area.
Last but not least, you can start with different known concentrations of sodium dodecyl sulfate in buffer solutions and do the same procedure that I explained and further also use FTIR and connect the concentration to the intensity or area under specific peaks if the peak is specific to your foulant and not the membrane. Now you can have a calibration curve that connects the ftir and concentration of foulant per area of the membrane. Just remember the time of filtration and pressure, temperature, flow velocity, (turbulent flow), pH and other important factors should be constant.
In the case of real-time monitoring of fouling using Raman spectroscopy, you can read the article of my colleague and maybe get some hints.
In one of my papers, I was also trying to identify the amount of vanillin adsorbed on the membrane surface after my experiment and I explained the methods there, which is similar to what I have learned from my colleague in the abovementioned article.
Hope my explanation is clear and I got your point correctly.
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I have generated a peristimulus time histogram from some neuronal electrophysiology data and I am trying to compare the AUCs for several data. When performing AUC, GraphPad does not generate SEM or SD values. I have seen several tutorials on YouTube where they are automatically calculated.
Thank you for your assistance.
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Hi,
Here's a was to calculating SD and SEM for AUC values in Prism:
  1. Perform AUC analysis for each peristimulus time histogram individually.
  2. Enter the AUC values from each analysis into a new table.
  3. Use the "Descriptive Statistics" analysis under the "Analyze" tab to calculate SD and SEM.
Hope this helps.
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I am currently working with a scanning electron microscope (SEM) for my research, and I am interested in understanding the significance of depth of field in SEM imaging. From what I have learned, the depth of field in SEM can affect the clarity of images, especially when dealing with thicker or three-dimensional specimens. Can anyone share their experiences or insights on how the depth of field impacts SEM imaging and how it compares to optical microscopy? Are there any specific strategies or techniques that can be used to optimize depth of field in SEM? I would greatly appreciate any advice or suggestions from fellow researchers who have worked with SEM and encountered similar challenges. Thank you in advance for your input!
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Greater depth of field helps to keep in focus different areas (of different height) of field of view simultaneously. Another name of depth of field is depth of focus. You can increase it by increasing working distance (with some loss of resolution) or by installing a smaller aperture of final lens (with some improvement of resolution by also increasing noise).
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How to prepare yeast sample for SEM
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I had worked with yeast (in colonies) just once, so not a lot of experience, but you have no other recommendations yet...
Use standard protocol for cell/bacteria colonies, but increase time for each step significantly, to at least 1 hour. First washes in pure alcohol and in pure resin keep overnight. May be it's an overkill, but it worked for me.
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Currently, I am trying to determine the fiber diameter in ImageJ using DiameterJ Plugin. Although I created segmented images using the diameterJ plugin, after analysis DiameterJ does not generate an excel file containing radius calculations. I attempted multiple methods, including downloading the software from multiple locations. It still contains errors. Therefore, it would be helpful if anyone who has effectively utilized the diameterJ plugin in ImageJ could share that software. Or any recommendations?
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Hello I encountered the same issue with DiameterJ plugin. However I was able to find a alternative plugin to measure the fiber diameter. Here is a step by step video tutorial. it uses the GIFT plugin in imageJ. Hope you find this useful https://www.youtube.com/watch?v=GjQ3V7uH-Dc
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Is it possible to obtain SEM images of the surface of the electrically insulating thin film, which is 200 microns thick and has a pale yellow color, without detaching it from the 4 mm thick glass substrate?
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Hello Dhaiwat
To avoid the charging of the surface, it is necessary to produce a conductive surface by evaporation or sputtering (see @Insic). My recommendation would be some nm Au. The thickness of this metallic layer must be lower than the reach (penetration depth) of secondary electrons (SEM-mode). Look for penetration depth of electrons with energies < 50 eV. In the case of thicker layers you observe only backscattered electrons (BSE-mode).
Don't forget to ground the surface (Connect the surface layer with zero Potential).
Yours
Rüdiger Mitdank
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In order to perform SEM or PLS SEM one needs to do CFA first and then mediation analysis, whereas in Barron & Kenny, there is no CFA involved, why?
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Mediation analysis is a statistical technique that examines how a third variable, called a mediator, influences the relationship between an independent variable and a dependent variable. Barron & Kenny approach and PLS SEM mediation analysis are two methods for conducting mediation analysis, but they have some differences. Barron & Kenny approach is based on multiple regression models and requires several assumptions, such as linearity, normality, and homoscedasticity. PLS SEM mediation analysis is based on structural equation modeling and does not require these assumptions. PLS SEM mediation analysis can also handle multiple mediators, complex causal structures, and latent variables, while Barron & Kenny approach is limited to simple mediation models with observed variables.
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In a few articles, I have come across images that are similar to SEM cross-sectional images but taken with AFM, but the article does not mention the details. Can you help me to measure in this way?
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You have to do a sample preparation.
In our lab, we do an ion-slice on the cross-section. Then the topography is reduced to a minima. Then, e.g. different materials of the cross-section can be detected.
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Hello, I am making some PDMS films that are about <10 mm thick. The PDMS solution consists of a 1:0.1:0.5 PDMS (polydimethylsiloxane) base to PDMS (polydimethylsiloxane) initiator to Hexamethyldisiloxane (HMDSO) ratio. The HMDSO is used to dilute the PDMS base to curing agent solution to allow for better wetting of the template it is being cast over.
Once the solution is applied to the template, the samples are kept at 60 °C for 2.5 h to cure and to evaporate any residual hexamethyldisiloxane (HMDSO). Following this, the sacrificial template is removed via acetonitrile bath followed by IPA bath. I'm planning to then leave the sample in the fume hood overnight for drying.
Should overnight fume hood exposure be enough time to ensure the PDMS is dry enough for SEM viewing or are there are other typical sample prep drying steps taken to prepare samples like PDMS or other polymers before SEM scanning that I should consider?
Thanks in advance.
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Thank you
Allan Philippe
!
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I did SEM in AMOS and it suggest to connect an error variable and observed variable to enhance the model fit. I was wondering if these variables can be connected as well as two error variables.
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Structural equation modeling (SEM) is a method that allows researchers to test complex hypotheses about the relationships among observed and latent variables (Kline, 2015). Observed variables are those that can be measured directly, such as test scores or survey responses. Latent variables are those that cannot be measured directly, such as intelligence or academic achievement. SEM involves specifying a model that represents the causal structure of the variables and estimating the parameters of the model using data. One of the advantages of SEM is that it can provide information about the fit of the model to the data, as well as the modification indices that suggest how the model can be improved (Byrne, 2016).
One of the questions that may arise when using SEM is whether it is appropriate to connect an observed variable and an error variable if the output suggests it in modification indices. An error variable is a latent variable that represents the measurement error or unexplained variance of an observed variable. Connecting an observed variable and an error variable means adding a path between them in the model, which implies that there is a direct effect of one variable on another. This may not make sense theoretically or conceptually, as error variables are usually assumed to be independent of other variables in the model. Therefore, connecting an observed variable and an error variable may not be a good practice in SEM, unless there is a strong justification for doing so based on substantive knowledge or previous research (Brown, 2015).
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Looking for a book recommendation about latent modelling in R. Specifically interested in CFA and SEM.
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There are several books that cover latent modeling in R. Here are some recommendations:
  • Latent Variable Modeling with R by E. Raykov and A. Marcoulides: This book provides a comprehensive introduction to latent variable modeling using R. It covers both exploratory and confirmatory factor analysis (CFA) as well as structural equation modeling (SEM).
  • Latent Variable Modeling Using R: A Step-by-Step Guide by T. Beaujean: This book is designed for readers who are new to latent variable modeling and R. It provides a step-by-step guide to conducting LVMs using the lavaan package.
  • Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide by J. Fitzmaurice, N. Laird, and J. Ware: This book provides an introduction to longitudinal data analysis using R. It covers both traditional methods (e.g., repeated measures ANOVA) as well as more advanced methods (e.g., mixed-effects models).
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Please can someone recommend the most appropriate SEM software for handling Panel data and at the same time moderation and mediation situation with the antecedent relevant diagnostic test and/or robustness check required for time series data. User friendliness for a beginner can equally be taken into consideration. 
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There are several software packages that can be used for Structural Equation Modelling (SEM) with panel data. Some of the most popular ones include:
  • Mplus: Mplus is a commercial software package that is widely used for SEM analysis. It is particularly well-suited for longitudinal data analysis, including panel data.
  • R: R is a free and open-source software environment for statistical computing and graphics. It has several packages that can be used for SEM analysis, including the lavaan package.
  • Stata: Stata is a commercial software package that is widely used in social science research. It has several modules that can be used for SEM analysis, including the sem module.
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Can I use SEM when I have data for only one country (so it is time series data with more than one variable), as it is not panel data now? If I can use it, do I need to take care of something special that is not in the panel data?
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Yes, you can use Structural Equation Modelling (SEM) when you have time series data with more than one variable. SEM is a statistical technique that allows you to test complex relationships between variables. It is often used in social science research to test theories about how different variables are related to each other. Here are some articles that discuss this topic:
  • SEM Time Series Modeling · R Views - RStudio
  • Structural Equation Modeling With Many Variables: A Systematic Review of Issues and Developments
  • A State-Space Approach for Structural Equation Model with Time Series and Cross-Sectional Data
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Detection of biofim formation using Scanning Electron Microscope
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Salsabil
As Kaushik explains, there are other substitute fixatives, but none of them are quit as complete, or as life-like as glutaraldehyde. All will likely demonstrate either some level oh morphological alteration, or shrinkage, or swelling. It is unfortunate, but glutaraldehyde is recommended under normal conditions, or a double fixative to include Osmium Tetroxide *(highly toxic to the touch).
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Hello everyone, I am working on the elaboration of macroporous materials for bone reconstruction. I mainly characterized the porosity using SEM observation. For the moment I use ImageJ to analyze the porous structure (size, shape, orientation,…) but I haven’t found an efficient method to automate the image processing. I wonder if someone know an accurate automatic and efficient method or software than can make me save time ?
Thank you in advance,
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Recently our lab purchased Porometric by ThermoFisher. In my opinion it is very powerfull tool because pores counting process is fully automated and for good results you just need contrast picture and enter pore size. But I suppose that ImageJ is enough for your research you need only learn from Manual about all options
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I am doing SEM and linear regression on same data. But the results of one or two variable (multiple variable) differs in significance and impact.
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In an SEM with latent variables, you account for measurement error (random noise) in the observed variables related to the exogeneous (independent, predictor) variables, whereas in standard OLS regression you don't. This alone may explain the differences.
Also, an OLS regression model is saturated (reproduces the observed data perfectly), whereas SEMs with latent variables tend to be overidentified. Overidentification means there can be misfit between your model and data. This also (or in addition) could explain discrepancies between your SEM and regression results.
Another issue is statistical power, which could differ between OLS regression and SEM, depending on various factors. Differences in power may explain differences regarding tests of significance between regression and SEM.
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Respected members,
I want to know how to report such result in research where second order relationship comes into existence.
For eg
Variable A, B and C ; all are individual and separate constructs (First order) as per previous studies.
But in my survey, post checking validity parameters in CFA using AVE, MSV , ASV etc values , it is found that Variable A and Variable B are making second order constructs, then how to justify this second order relationship with theory if Previously no such relationship is established.
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can second order be changed to first order in sem
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Hi,
I want to test a moderated mediation model with two dependent variables.
However, my sample size is too small to include many latent variables.
Can I use path analysis (SEM without latent variables) to test this model?
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the small sample size is indeed a concern while going for SEM. Nonetheless, you can once run the model in SmartPLS
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What analysis, SEM or TEM, is better for MOF identification? Unfortunately, I only have one choice.
I have the crystal size average with XRD, and therefore, I think SEM is better.
please help me.
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Hi Saied Saeid Saei dehkordi,
If you are talking about Metal-organic framework analysis then TEM as it differentiates phase by density gradient. Law density (Organic) bright, high density (metal) dark.
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I am currently doing my undergraduate study, and I am using UTAUT2 to evaluate factors that can affect adoption of a new application we made. I have 30 sample size, all obtain using a purposive sampling only. I want to use SEM by performing PLS but it seems that I needed a bigger sample size, what other statistical models can I use?
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UTAUT2 (Unified Theory of Acceptance and Use of Technology 2) is an extension of the original UTAUT model and incorporates additional constructs to explain user acceptance and adoption of technology. When analyzing UTAUT2 data, various statistical models can be employed depending on the research objectives and the nature of the data. Here are some commonly used statistical models for analyzing UTAUT2 data:
  1. Multiple Regression Analysis: Multiple regression is a common statistical technique used to analyze UTAUT2 data. It allows you to assess the relationships between the predictor variables (UTAUT2 constructs) and the outcome variable (user acceptance and use of technology) while controlling for other variables.
  2. Structural Equation Modeling (SEM): SEM is a powerful statistical technique that estimates and tests complex relationships between latent variables. UTAUT2 constructs can be treated as latent variables, and their relationships can be assessed using SEM. This approach provides information on the direct and indirect effects of the constructs on user acceptance and use of technology.
  3. Partial Least Squares Structural Equation Modeling (PLS-SEM): PLS-SEM is an alternative approach to SEM that is often used when the sample size is relatively small or the data violate the assumptions of traditional SEM. PLS-SEM is suitable for analyzing UTAUT2 data when the focus is on predicting the outcome variable rather than estimating population parameters.
  4. Bayesian Structural Equation Modeling (BSEM): BSEM is a statistical approach that incorporates prior information and uncertainty into the estimation of structural equation models. It allows researchers to specify and test more flexible models and provides a Bayesian framework for model estimation and comparison.
  5. Hierarchical Linear Modeling (HLM): HLM is a statistical technique used to analyze nested data or data with a hierarchical structure. If your UTAUT2 data includes multiple levels (e.g., users nested within organizations), HLM can be employed to examine how the UTAUT2 constructs influence user acceptance and use of technology at different levels.
These are some of the statistical models commonly used to analyze UTAUT2 data. The choice of model depends on factors such as the research objectives, sample size, data characteristics, and the complexity of the relationships being investigated.
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Hi,
I am conducting SEM. Is a sample size of 220 enough to estimate 50 parameters?
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There is no general way to answer this question because it depends on many factors such as the model structure, specific parameter values, and effect sizes. The best way to address this issue is to simulate your specific model for the given sample size using a Monte Carlo simulation. This allows you to study the accuracy of parameter estimates, standard errors, and test statistics as well as the rate of improper solutions and/or convergence problems.
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The data I collected for my research yielded a non-normal distribution.
I aim to test a hypothetical model using SEM, and AMOS is said to be better for confirmatory research. However, I don't want an inflated model (since the data are not normally distributed).
Accordingly, I have the following questions:
1. Is SmartPLS a good fit for conducting SEM and path analyses, and is that more accurate than Amos for the data that are not normally distributed?
2. Moreover, is it better to use VB-SEM?
I asked the second question because VB-SEM is said to be more flexible regarding non-normality.
I sincerely thank the researchers who will answer these questions.
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With the latest SmartPLS release, you can run both PLS-SEM and CB-SEM (like Amos): https://www.smartpls.com/downloads
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In order to utilize SEM (Scanning Electron Microscopy) technology, tissue samples typically undergo a fixation and then dehydration process. While creating the protocol for my thesis research, I have been trying to explore if there is any research for what these minimum or maximum time intervals are for each step during a graded series of ethanol, especially what the supporting research/evidence is.
I have found some typical protocols, but no luck in finding studies, equations, or reasoning that explain why the specified time intervals were utilized. For further context, I am creating a protocol for bovine and porcine tissue samples.
Any ideas, suggestions or recommendations would be incredibly appreciated! Thank you in advance for any help or assistance.
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Most of the electron microscopists have their own reasoning for their protocol and stick with it, I being one of them!
Anyway, coming to the point the duration of immersing the samples in different ethanol concentration varies according to the sample size.
*For a sample of say 3mm cube I would start with:
30% ethanol for 5-10 min,
50% " "
70% " 15-20 min
80% " 10-15
90% " "
95% " "
100%. " 20-30 min with three changes.
After this you can use HMDS or CPD for drying.
The above mentioned was passed on to me from previous generation.
* All the durations were only arbitrary.
I hope this helps.
There is no Xylene involved in SEM sample preparation.
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I am a university student and I've been doing EDX analysis on some kanthal heating wires and one of them appears to have a huge amount of carbon in it (one reading said 20% the other said 25%), which is very unexpected and I cannot explain it (in the report I'm writing that's due tomorrow, and I don't have time to repeat the EDX analysis), apart from as contamination from a fingerprint (as I was a bit clumsy while preparing the sample for the SEM), does anyone with experience of EDX think contamination could leave such a high amount of carbon on the sample?
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Typically, fingerprints contain other elements, like electrolytes, in addition to C. If I'm trying to determine if something might be a fingerprint, I also look for low levels of O, Na, and Cl. May also see K and S.
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Do you know a book on scanning electron microscopy of concrete/cement?
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It is not a book, but also try:
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It is difficult for me to correctly interpret the SEM photos of the concrete microstructure. Do you know any books about SEM image analysis?
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Certainly! Here are a few books that can help you with SEM image analysis and interpreting concrete microstructure:
1. "Scanning Electron Microscopy and X-Ray Microanalysis" by Joseph I. Goldstein, Dale E. Newbury, and David C. Joy: This comprehensive book provides a thorough introduction to SEM and X-ray microanalysis techniques. It covers sample preparation, instrument operation, image interpretation, and qualitative/quantitative analysis.
2. "Scanning Electron Microscopy of Cements and Concrete" edited by Karen Scrivener and Peter L. Pratt: This book focuses specifically on SEM analysis of cementitious materials, including concrete. It covers various aspects of sample preparation, SEM imaging techniques, and interpretation of microstructural features in cement-based materials.
3. "Introduction to Scanning Electron Microscopy" by Michael A. Wilson: This book provides a beginner-friendly introduction to SEM techniques and principles. While it does not specifically focus on concrete microstructure, it covers fundamental concepts that can be applied to SEM analysis in various fields.
4. "Quantitative Energy-Dispersive Electron Probe X-Ray Microanalysis (Astm Special Technical Publication// Stp)" by David C. Joy and Andre V. Selim: This book delves into quantitative analysis using energy-dispersive X-ray microanalysis (EDX) in conjunction with SEM. It explores the principles, techniques, and interpretation of results, which can be relevant for analyzing concrete microstructures.
These books should provide you with a solid foundation for understanding SEM image analysis and interpreting concrete microstructures. It's worth noting that SEM analysis can be complex, so it may be helpful to seek guidance from experts or researchers in the field for specific challenges you encounter.
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I used untreated cotton fabric,treated fabric with silk and silver mixture,silver nano particle, silk nano particle
Here, s4b(1,2,3) are untreated cotton fabric,s3b(1,2,3) are treated fabric with silk and silver mixture,s(21,22,23) are silver nanoparticle and s(11,12,13) are silk nanoparticle
Please anyone interpret the SEM analysis result
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how much sample size is needed for SEM & XRD analysis of Carbon fiber brush electrode, in MFC, only bristles can be used for these analyses, or the titanium wire can also be used?
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SEM and XRD are the best techniques for morphological and structural characterization, requiring a sample size of around a pea-size powder volume.
Thanks
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I am currently working with SEM for my paper. While checking my model fit I am facing a lot of problem. I am clicking the modifications indices option in calculate estimate, but while running model fit in plugins, it automatically clicked modifications indices and residual and showing error. I am attaching picture here. If you have any tricks please share me.
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Looking at your question, I believe you are facing issues related to model fit while using AMOS for SEM analysis. Here are some steps you can take to address these problems:
1. Check the model specification: SEM models are only as good as the assumptions and theoretical framework on which they are built. You should start by checking the model specification to ensure that it is valid and based on sound theoretical reasoning. This includes verifying the relationships between the variables in the model, specifying the measurement models, and ensuring that the model is properly identified.
2. Check the model fit indices: AMOS provides a number of model fit indices that can help you evaluate the adequacy of your model. These include chi-square, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR). Look for values of CFI and TLI greater than 0.90, and values of RMSEA and SRMR less than 0.08.
3. Examine the modification indices: Modification indices suggest where the model may be improved by adding paths or freeing parameters. However, it is important to exercise caution when making modifications to the model, as this can result in overfitting and reduced generalizability. If you do decide to make modifications, do so in a systematic and theoretically informed way.
4. Check for model misspecification: If the model fit indices suggest that the model is misspecified, it may be necessary to revise the model specification. This can involve adding or removing variables, re-specifying the measurement models, or modifying the relationships between the variables.
5. Seek help from an expert: SEM can be a complex and challenging technique, especially for those who are new to it. If you are having difficulty interpreting your results or addressing model fit problems, it may be helpful to seek assistance from an experienced SEM researcher or statistician.
I hope these suggestions are helpful in addressing the issues you are facing in your SEM analysis.
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Hello ResearchGate community,
Can anyone help me please with identifying the suitability of this model? Generally the ES of the relationship between latent variables should be at least moderate, even strong (especially the interrelated subscales of EIS in the center.
I have counted 64 (free) parameters... Does this seem correct? Is this model, at glance, sound?
It is estimated that we would get around 250 people for our sample, but I'm aiming to get at least 320, as according to Tabachnik and Fidell (2007) - usually 10 N per parameter is required BUT if all assumptions are satisfied (i.e. normality, linearity etc..) 5 N per parameter is permittable (hence my sample size minimum of 320, as opposed to 640).
Really appreciate any help with this... Been years since I even sniffed SEM.
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What does not appear sound about this model is that the EIS factor in the middle does not have but a single latent variable (NR-Self) as an indicator. I suspect that the model in this way would be underidentified without further constraints (or additional indicators of EIS). In general, you need at least two indicators per latent variable for identification.