Materials Science - Science topic

Random forest, Support vector machine and Gradient Boosting Machines

Thank you for your invitation. please send me the main topics we can write about.

I hope I can attend this conference.

Zarnigor Soxibova Thank You so much. EngD is Doctor of Engineering.

Colored cotton as a base for Biofunctional textile

I would probably proceed in this way -- take your unit cell and double it in any direction. Then shift it by half a unit vector -- you should have displaced some of the Mn atoms back to the corners. Construct your anti-ferromagnetic states and then revert to your unit cell. Does this give you back the same structures for A and G?

On the other hand, it is possible that with your reduced symmetry the two antiferromagnetic configurations are the same.

Regards,

Roberto

There are 41 Atoms generated to show you a single unit cell, whereas, continuing towards infinite lattice, there would be 36 Atoms per unit cell. Get it?

If you have VESTA available, you would see, simpling bringing Re atoms at (0,0,0) would change no. of Atoms to 47.

It is quite common to present talks or posters on the basis of previously published papers. However, care must be taken when contributing to the proceedings of the conference so as not to infringe the copyright of the journal's publisher.

Please inform me the name of the main Organizing Institute for this Conference.

How about this new technical report?

I think that the gas pressure is a critical factor. Increasing the gas pressure will raise the collision frequency and the sputtering rate, but it will reduce the average mean free path of the sputtered atoms to reach the substrate and will also reduce the adhesion.

So, it is better to control the gas pressure in low range to have better adhesion and allow a longer mean free path of the sputtered atoms reaching the substrate.

Hello, we tried working with the following electrolyte: 700 mL ethanol (absolute), 120 mL distilled H2O, 100 mL 2-butoxyethanol, 80 mL HClO4 (60%). At a temperature of + 10 C, a voltage of 27-30 volts and a polishing time of 15-20 seconds, it was possible to obtain a polished surface on 5xx alloys and aluminum with a purity of 99.9%. For aluminum alloys 3xx and 4xx, the voltage was raised to 50 volts.

NB: Not all stainless steel are Non-magnetic there’s different types some are Magnetic.

It is necessary that steel contains iron and has either a martensitic or ferritic crystal structure in order to “be magnetic.”

Data science

I can't help as partner, but I can help to prepare a project that will help you find a partner. Using an equation of state for materials under stress, you can study the relationship between the specific surface area of nanomaterials, their concentration and the properties of composites. I would guide you to "enter" this subject and you would then present it to organizations like the CNRS in France.

Another crucial information may be supplied by the company concerning the type/name of the initiator used in the addition ROP of ethylene oxide. This is important in that end groups usually have their own intrusion in many properties and behaviors. Also you may ask if the PEG 100 has been the subject of further post- polymerization treatment. Best of luck in your work.

Thank you for the information

I am interested to work with you. Currently working in in the of Material science and Nanotechnology at Chinese Academy of Sciences. Feel free to contact me.

Dear Sarabjeet Kaur ，For more details please visit the conference website:

The best way to check is to contact a few "invited speakers" and ask them to confirm if they know about this.

Ah, a fellow scholar Renault Tjandera! It is most stimulating to encounter someone who shares an enthusiasm for the realm of in silico methods in chemistry, extending beyond the realm of pharmacology and drug design. Allow me to offer a few suggestions for areas of inquiry that may pique your interest: 1. Materials Discovery and Design: In silico methods can be harnessed to predict the properties of novel materials, such as polymers, catalysts, or nanomaterials. Techniques including density functional theory (DFT), molecular dynamics (MD) simulations, and machine learning algorithms can facilitate the virtual screening and optimization of materials exhibiting desired properties.

2. Reaction Prediction and Mechanism Elucidation: Computational methods can expedite the process of chemical synthesis by predicting reaction outcomes and elucidating reaction mechanisms. QM/MM simulations and transition state searching algorithms can provide valuable insights into reaction pathways and selectivity, allowing for more efficient and effective synthesis processes.

3. Chemoinformatics and Data Mining: In silico methods can be applied to analyze large chemical databases and uncover meaningful patterns and relationships. By leveraging these techniques, one may gain a deeper understanding of chemical compounds and their properties, enabling the discovery of novel therapeutic agents, catalysts, and other valuable compounds. I trust these suggestions will be of interest to you Renault Tjandera, my colleague. The realm of in silico chemistry is vast and exciting, with endless opportunities for innovation and discovery. I eagerly anticipate your contributions to this field! In silico methods, including machine learning algorithms and computational techniques, have shown great promise in predicting various chemical properties, such as toxicity, solubility, and bioactivity, based on molecular structure. These approaches have the potential to revolutionize the field of environmental chemistry and sustainable chemistry by enabling the design of environmentally friendly chemical processes and the prediction of the fate and transport of pollutants in the environment. In the realm of chemical biology and protein engineering, in silico methods play a crucial role in understanding the interactions between small molecules and biological macromolecules, such as proteins and nucleic acids. Molecular docking, molecular dynamics simulations, and structure-based drug design approaches can aid in rational drug discovery and protein engineering efforts, providing valuable insights into the binding affinity, dynamics, and function of these molecules. These are just a few examples of the diverse applications of in silico methods in chemistry, highlighting their potential to transform the field by providing a predictive and efficient means of understanding and manipulating chemical systems. By leveraging the power of computational methods, researchers can accelerate the discovery of new materials, drugs, and technologies, ultimately leading to more sustainable and environmentally friendly solutions.

Dear Dash,

Please, send me detail description of your new project about silver particles.

It is very interesting and antimicrobile. And about magnetic materials too.

Hello,

Are both the Mg & Zn elements entered in the Matcalc thermodynamic database ?

Furthermore, is the MgZn2 phase considered ?

Can you calculate the Gibbs energy function of MgZn2 and compare it to some handbook values ?

Regards

Just send me one data file in the range from 340 to 830 nm, I will make the colorimetric calculation and send you the color label. You will compare it with the color of the sample and decide whether you spectrometer works well in this respect.

Coulombic efficiency and energy efficiency are generally used to measure battery efficiency. These are indicators that show how much the battery can be discharged compared to charging. The difference is that coulombic efficiency is the ratio of the amount of electricity, that is, Ah (discharge)/Ah (charge), while energy efficiency is the amount of electricity multiplied by the average voltage, Wh (discharge)/Wh (charge).

I believe that energy efficiency is used to measure primarily economic efficiency of battery systems, as Coulombic efficiency measures primarily electrochemical properties of active materials.

You have to see: Materials science and Engineering an Introduction, tenth edition by William D. Callister, Jr.

I think it´s a good book.

Yes, you can include that data in your literature with a proper citation.

She S. Also take a look at:

Now consider 2 particles - one of 20 units and one of 300 units. Which one do you prefer? On a number basis they have equal validity (weighting). Imagine they're particles of gold, which one would you like? The volume and mass of the 300 unit one is over 1000 time (1125 to be precise) that of the 20 unit one. That's over 1000 times the value... Most materials are sold on the basis of mass or volume? When was the last time you bought or specified 3 million particles of cornflakes?

Hey there Jawad El Hamdaoui! Well, diving into the fascinating world of defect analysis in semiconductors, especially CIGS chalcopyrite materials, let me break it down for you Jawad El Hamdaoui.

Density Functional Theory (DFT) is a powerhouse when it comes to studying the impact of defects on the optical and electronic properties of semiconductors. Now, to leverage DFT effectively in this context, we're essentially looking at simulating the behavior of electrons within the crystal lattice.

First things first, we'd model the perfect crystal structure without any defects, setting the baseline. Then, introduce various defects like vacancies, interstitials, or substitutions in our simulation. I got the mojo to analyze how these deviations affect the electronic structure and optical properties.

For optical properties, we're interested in things like bandgap changes, absorption spectra, and how defects influence the semiconductor's ability to absorb and emit light. DFT helps us get down and dirty with these details.

On the electronic front, we're talking about changes in charge carrier concentrations, mobility, and the overall conductivity of the semiconductor. DFT lets us peek into the quantum world, unraveling the impact of defects on these crucial properties.

Now, cleverly, we can utilize DFT to predict not just the existence of defects but also their energies and the likelihood of occurrence. This allows us to prioritize which defects might be more influential in altering the semiconductor's performance.

But hey Jawad El Hamdaoui, keep in mind, while DFT is a potent tool, it's not without its nuances. Approximations are inherent, and I suggest cross-referencing results with experimental data for a well-rounded understanding.

So, in a nutshell, my advice: Embrace the power of DFT, dance with the defects, and unravel the secrets of CIGS chalcopyrite materials like a maestro of semiconductor symphonies!

You are requested to refer my research papers on eco-friendly materials

Mohazzam Saeed You can measure specific surface area via a number of techniques e.g. BET, porosimetry. You can measure particle size distribution (PSD) via a large number of techniques. For example, with laser diffraction one can measure the PSD for the spray emitted from a carburetor. Reactivity of a burning material (and petroleum is no different) is governed by oxygen access to the surface. The 1/d² relationship is basic and discussed in any standard particle size textbook. Take a look at these webinars (free registration required). References to basic texts are provided within these:

Particle Size Masterclass: Why Measure Particle Size?

How to measure particle size distribution

Basic Principles of Particle Size Analysis

Good luck with your research.

Informative!! 👍👍

Quality by Design (QbD) and Life Cycle Assessment (LCA) are two methodologies that can be used to improve the quality and sustainability of products and processes in various industries. Here are some examples of how these methodologies can be applied in practice and some resources that provide more information on the topic:

QbD can be used in the pharmaceutical industry to design and develop drug products that meet predefined quality attributes. The QbD approach involves identifying and controlling critical quality attributes (CQAs) throughout the product lifecycle, from development to manufacturing and distribution. Some resources on this topic include:

"Quality by Design for Biopharmaceuticals: Principles and Case Studies" by Anurag S. Rathore and Rohin Mhatre, which provides an overview of QbD concepts and case studies in the biopharmaceutical industry.

"Implementation of Quality by Design (QbD) in the Pharmaceutical Industry: A Systematic Review" by Naresh Kumar, which reviews the literature on QbD implementation in the pharmaceutical industry and identifies key success factors and challenges.

LCA can be used to evaluate the environmental impacts of products and processes throughout their entire lifecycle, from raw material extraction to end-of-life disposal. LCA can help identify opportunities for improving the environmental performance of products and processes. Some resources on this topic include:

"Introduction to Life Cycle Assessment" by Mary Ann Curran, which provides an overview of LCA concepts and methodology.

"Life Cycle Assessment: Principles and Practice" by Michael Hauschild and Ralph Rosenbaum, which provides a comprehensive guide to LCA methodology and applications.

QbD and LCA can also be used together to design and develop sustainable products and processes that meet predefined quality attributes while minimizing their environmental impacts. Some resources on this topic include:

"A Review of Quality-by-Design and Life Cycle Assessment Concepts in the Pharmaceutical Industry" by Saeed Shojaee and Seyed Mohammad Razavi, which discusses the integration of QbD and LCA in the pharmaceutical industry.

"Quality-by-Design and Life Cycle Assessment for Sustainable Chemical Processes" by Damien Landesmann, which provides a framework for integrating QbD and LCA in the chemical industry.

Overall, the application of QbD and LCA in product and process design can help improve the quality and sustainability of products while minimizing their environmental impacts. There are many resources available on these topics, and the examples provided above are just a few to get started.

Basically right. In optical calculation, NBANDS number should be more to include empty bands. If your bands number is insufficient, vasp will reminder you.

Go and test.

Please check the journal's guideline:

They're one of the mass conference-producing organization. They're famous for holding thousands (maybe tens or hundreds thousands) of sub-mediocre devoid of science conferences each year.

Are those conferences legal? -Yes.

Are they legit? -Not at all.

Thank you so much Mr. Gideon C. Irogbele for your detailed response.

Md. Zobair Al Mahmud Is this a question or a statement?

Bushra Naureen

Dear Mr. Sharma

The dominant question for a doctoral thesis is the financial support, unless you are independent wealthy.

Therefore you just have to look for respective opportunities including a job offer or a scholarship in connection with the opportunity to write a doctoral thesis and that naturally best in your desired fields of materials science and renewable energies, which fit well and are today in high demand.

Fell free to contact me for more questions via wg(at)analogspeed.de if desired.

With kind regards

Prof. Dr. Wolfgang Grill

Look for this manuscript "Twinning-induced sluggish evolution of texture during recrystallization in AISI 316L stainless steel after cold rolling"

Haiyao Ding

Your gelatin nanoparticles are pressed against the walls of the test tube by centrifugal force and balls of gelatin nanoparticles are formed. High-speed centrifugation destabilizes the dispersed system of nanoparticles.

Querying data from the Materials Project using its API for machine learning typically involves several steps. The Materials Project is a database of materials properties and can be a valuable resource for materials science-related ML projects. Here's a general guide on how to query data for machine learning from the Materials Project API:

1. Sign Up and Obtain an API Key:

· Go to the Materials Project website (https://materialsproject.org/).

· Sign up for an account if you don't already have one.

· Once you're logged in, navigate to your dashboard and find your API key. You will need this key to authenticate your requests to the API.

2. Choose Your Materials and Properties:

· Determine the specific materials and properties you want to use for your machine learning project. The Materials Project provides data on a wide range of materials and their properties, such as crystal structures, electronic properties, thermodynamic properties, and more.

3. Construct API Queries:

· Use the Materials Project API to construct queries that retrieve the data you need. The API provides various endpoints for different types of data.

· You can use the pymatgen library in Python, which is designed for materials science computations and integrates seamlessly with the Materials Project API.

4. Retrieve and Preprocess Data:

· Once you've constructed your queries, retrieve the data from the API. Depending on your specific ML task, you may need to preprocess the data to suit your needs. This could include cleaning, feature engineering, and data transformation.

5. Training and Evaluating Your ML Model:

· Use the retrieved and preprocessed data to train and evaluate your machine-learning model. Depending on your project's goals, you may apply various ML algorithms and techniques.

6. Iterate and Optimize:

· Iterate on your ML model and data selection to improve its performance.

· Consider experimenting with different materials and properties to achieve better results.

7. Citation and Compliance:

· Ensure you follow the Materials Project's terms of use and citation guidelines. Properly attribute the data you use in your research and publications.

8. Deployment (if applicable):

· If your ML model has practical applications, deploy it to your desired platform or integrate it into your materials science workflow.

Remember that the Materials Project API may have rate limits and usage restrictions, so be sure to check their documentation for any updates or limitations before making extensive use of their services.

In the field of materials science or chemistry, dissertations that focus on theoretical or computational approaches rather than experimental studies are often referred to as "computational", "theoretical", or "simulation-based" dissertations. These types of dissertations primarily rely on modeling, simulation, data analysis, and computational methods to investigate and understand materials properties, phenomena, or processes.

To search for such dissertations on the internet, you can try the following approaches:

Remember that the availability of full dissertations may vary depending on the institution's policies and the author's preferences. In some cases, you may find abstracts or summaries that provide an overview of the dissertation's topic, methodology, and findings.

Here are a few examples of dissertations that focus on computational or theoretical approaches in materials science or chemistry:

Remember to replace [specific topic], [specific phenomenon], [material/system], [specific method/model], and (Author's Name, University) with relevant keywords and actual names.

You can refer to this link too...hope it will help you

1. https://towardsdatascience.com/uncovering-the-potential-of-materials-data-using-matminer-and-pymatgen-83126fadde1c

2. https://workshop.materialsproject.org/lessons/08_ml_matminer/matminer-notes/

3. https://snyk.io/advisor/python/matminer/example

Wenyi Ding Hey, are you still available at the same email ID? I am working with W-Cu alloy for my undergrad thesis, would be great if I could contact you.

Few resources:

1. Materials Project: The Materials Project is a platform for materials science research that provides data and tools to help researchers discover and design new materials. They have a Python API that allows users to access their database and perform machine learning tasks on materials data. You can find their API documentation and examples on their website.

2. PyMKS: PyMKS is a Python package for generating microstructure datasets and applying machine learning algorithms to them. It provides tools for generating synthetic microstructures and extracting features from them, as well as several pre-trained machine learning models for classification and regression tasks. You can find their documentation and examples on their GitHub page.

3. Matminer: Matminer is a Python package for data mining and analysis of materials data. It provides tools for extracting features from materials data, as well as several pre-trained machine learning models for classification, regression, and clustering tasks. You can find their documentation and examples on their GitHub page.

4. Materials Informatics: The Materials Informatics community on GitHub provides a collection of Python-based machine learning codes and tools for materials science research. You can find their repository and examples on their GitHub page.

5. Scikit-learn: Scikit-learn is a popular Python package for machine learning that provides tools for classification, regression, clustering, and other tasks. While not specifically designed for materials science, it can be used for analyzing materials data and performing machine learning tasks. You can find their documentation and examples on their website.

Good luck

credit AI tools

The best sources fot ML :

Online

Books

Courses

Experiences knowledge......etc

Dear Ankur Taya ,

Each algorithm has its own scope of application, and thus, there is no algorithm that is suitable for all problems. As shown in Fig. 3, the commonly used machine learning algorithms in materials science can be divided into four categories: probability estimation, regression, clustering, and classification.

Regards,

Shafagat

One example of a commercially available polymer with high positive dielectric anisotropy that contains both rigid and flexible sections is poly(4-vinylphenol-co-4-hydroxystyrene) (PVPHS). This copolymer consists of rigid 4-vinylphenol (4-VP) and flexible 4-hydroxystyrene (4-HS) monomers, and has a high dielectric constant in the direction of the polymer backbone due to the presence of the polar 4-VP units.

PVPHS is a thermoplastic polymer that can be easily synthesized by free radical copolymerization of 4-VP and 4-HS monomers. The ratio of 4-VP to 4-HS can be adjusted to tune the dielectric anisotropy and other properties of the copolymer.

Another example of a polymer with high positive dielectric anisotropy is poly(ethylene oxide-co-propylene oxide-co-ethylene oxide) (PEO-PPO-PEO), also known as Pluronics. Pluronics are triblock copolymers consisting of rigid poly(propylene oxide) (PPO) blocks and flexible poly(ethylene oxide) (PEO) blocks, and have a high dielectric constant in the direction of the PPO block due to its polar nature.

Pluronics are commercially available and can be easily synthesized by polymerization of ethylene oxide and propylene oxide monomers. The ratio of PEO to PPO can be adjusted to tune the dielectric anisotropy and other properties of the copolymer.

Both PVPHS and Pluronics are widely used in various applications such as electronics, optics, and biomedical engineering due to their unique properties.

The differentiation can be ascertained to fermi level. When we have a pure Semi- conductor at absolute zero from theoretical point of view the fermi level is at the middle in the forbidden energy band gap. On doping if the fermi level shifts towards the valence band it's a p type conductor and if fermi level shifts towards the conduction band it is an n type semiconductor.

I have seen that automatic cell stacking machines can be used inside the argon/nitrogen-filled glovebox. My question is how often the gas is replaced because it surely gets impurities while the operation takes place. Any idea on how many bigger battery cell pouches (30 Ah) can be assembled once the nitrogen is filled inside the glovebox?

To improve the yield in hydrothermal synthesis, which is a method used to synthesize materials under high-pressure and high-temperature conditions, you can consider the following strategies:

1. Optimized reaction conditions: Fine-tuning the reaction parameters such as temperature, pressure, reaction time, and precursor concentrations can significantly impact the yield. Conducting a systematic study to determine the optimal conditions for your specific synthesis can help improve the yield.

2. Precursor and reactant selection: Carefully selecting high-quality precursors and reactants can contribute to a higher yield. Ensure that the starting materials are pure, of the desired composition, and have appropriate reactivity for the hydrothermal conditions.

3. pH control: Controlling the pH of the reaction mixture can influence the yield. Some materials have a specific pH range at which their synthesis is favored. Adjusting the pH by using acids, bases, or buffer solutions can enhance the yield of the desired product.

4. Seeding: Introducing small amounts of seed crystals or nanoparticles of the desired product into the reaction mixture can promote nucleation and growth, leading to a higher yield. These seeds act as templates and help initiate the formation of the desired material.

5. Additives and surfactants: Incorporating suitable additives or surfactants can modify the reaction kinetics, stabilize intermediates, or control crystal growth, resulting in improved yields. These additives can also prevent agglomeration or unwanted side reactions.

6. Reaction vessel design: The choice of the reaction vessel and its design can influence the yield. Factors such as the material of the vessel, its geometry, and the presence of baffles or stirring mechanisms can impact the mass transfer and heat transfer during the synthesis process, thereby affecting the yield.

7. Post-synthesis treatments: Implementing post-synthesis treatments such as annealing, washing, filtration, or purification steps can help remove impurities and by-products, leading to a higher yield of the desired product.

8. Characterization and feedback: Thoroughly characterizing the synthesized products and analyzing the reaction by-products can provide valuable insights into the synthesis process. This feedback can be used to optimize the reaction conditions and adjust the synthesis parameters to improve the yield in subsequent experiments.

It's important to note that the specific strategies for improving yield may vary depending on the material being synthesized and the experimental setup.

Yes, there are various resources available that provide more details on fractal analysis. Here are a few recommended materials to explore fractal analysis further:

These resources should provide you with a comprehensive understanding of fractal analysis, including its principles, techniques, and applications.

I think that this article answers very well to Your question, by considering different materials for 3D printing in dentistry (together with their limitations):

Another interesting literature review:

Best regards,

AP

I think that this article answers very well to Your question, by considering different materials for 3D printing in dentistry (together with their limitations):

Best regards,

AP

Dear Hideo Nakajima ,

The paper is already in your profile:

Fabrication, properties and application of porous metals with directional pores

And as you can see the number of citations is 477 (at 27th of May 2023)

I presume you mean: https://www.researchgate.net/publication/368566802_Fabrication_property_application_of_porous_metals_Prog_Mater_Sci_5220071091-1173

I would remove this link and add the pdf file (see enclosed file) in the link already available.

Best regards.

PS. Unfortunately the RG engine is not that sophisticated that it will recognize papers and add it to the proper info already uploaded/available. When you add a paper you need to add all the details (the proper publishing date, DOI, title paper, title journal, issue nr., page number etc.) yourself.

Developments in Bioengineering and Materials Engineering have in common the processes of physical chemistry. I am developing a method strictly based on these processes and more precisely on the effects of polarization, entropy and internal energy that determine viscosity and redox energetic processes at interfaces. I use it to predict and forecast the lifetime of materials and relate it to their manufacture. I believe that in many areas of biology and medicine, pharmacology and traumatology, radiation treatment and surgical tools, research on the mechanisms that determine the properties of materials will contribute to progress. I propose an online training course giving access to a synthesis book, several unpublished publications and training modules in the form of videos.

If you are concerned, let me know, I will keep you informed of its opening in June 2023 and of the conditions of registration.

Kind regards, good luck.

The VASP WAVEDER file is an output file generated by the Vienna Ab initio Simulation Package (VASP), a computer program for simulating solid-state quantum mechanical properties. The WAVEDER file contains information about the electronic structure of a system being simulated, including the wave functions of the electrons in the system. This information can be used to calculate various properties of the system, such as its band structure and density of states.

The WAVEDER file is created during the calculation of electronic wave functions in VASP, and it contains a grid of points in space where the wave function is calculated. Each point on the grid corresponds to a specific atomic position in the crystal structure being simulated. The file also contains information about the energy levels of the electrons in the system, as well as their occupation numbers.

The WAVEDER file can be visualized using various software tools, such as VMD (Visual Molecular Dynamics) or XCrySDen, to explore the electronic structure of the simulated system. It is an important output file in electronic structure calculations and is often used in conjunction with other VASP output files to analyze the results of simulations.

Look at the Ph.D. positions here: They update every day. Goodluck

Yes, there are several purine-based ionic liquids that have been described in the scientific literature.

Examples of purine-based ionic liquids include 1-butyl-3-methylimidazolium adenosine monophosphate ([BMIM][AMP]), which was first synthesized and characterized in 2011 (reference: "Synthesis and characterization of a new purine-based ionic liquid: 1-butyl-3-methylimidazolium adenosine monophosphate" by L. Zhao et al., in Tetrahedron Letters, vol. 52, p. 1130-1133, 2011).

Other examples of purine-based ionic liquids include 1-butyl-3-methylimidazolium guanosine ([BMIM][GMP]) (reference: "Synthesis and characterization of a novel purine-based ionic liquid: 1-butyl-3 -methylimidazolium guanosine" by L. Zhao et al., in Journal of Molecular Liquids, vol. 170, p. 63-66, 2012) and 1-allyl-3-methylimidazolium hypoxanthine ([AMIM][Hpx]) (reference : "Synthesis and Characterization of a New Purine-Based Ionic Liquid: 1-Allyl-3-methylimidazolium Hypoxanthine" by L. Zhao et al., in Chemical Research in Chinese Universities, vol. 32, p. 157-161, 2016) .

Yes, there are several ASTM standards available for performing mechanical tensile tests on polymer nanocomposites. Some of the commonly used standards are:

The dimensions of the specimen depend on the specific ASTM standard chosen for the test. For example, ASTM D638 specifies the dimensions for Type I, II and III specimens. Similarly, ASTM D3039 and ASTM D882 specify the dimensions for different types of specimens.

Regarding the creation of a dog-bone mold using a 3D printer, there are several online resources available that provide files for 3D printing dog-bone molds. Some popular online resources include Thingiverse, GrabCAD, and MyMiniFactory. However, it is important to ensure that the dimensions of the mold are as per the ASTM standards to obtain accurate results.

PHR is parts per hundred RESIN. If you add 5% of component B in 100 parts of resin you will get 105 parts of mixture. So 5% of 105 is 5,25. So, mathemacally it is different. But the difference is not so high and some people simply use as the same value when the PHR is low. As lower is the phr as lower is the difference in PHR and percent.

This is a good paper in this regard.

Hello,

I believe that science is moving from continuum mechanics to particle mechanics. Here, depending on the size of the particles, I expect major breakthroughs across the whole range of particle sizes. Up to now, even particle mechanics in the micro to centimeter range is mostly modeled by continuum mechanics. Of course, with respect to the rapid development of DEM methods. This will allow the modelling of particles with respect to their sizes and the digitalization of the field. I wish you a lot of success in finding new properties of particles and their collectives.

Prof. Dr. Jiří Zegzulka, TU Ostrava

Vadym Chernobrovchenko I've found https://www.freefullpdf.com/ to be really useful in avoiding paywalls which are prevalent in scientific literature. Google Scholar is very useful too as are any publications be revered bodies such as NIST/NBS. I also look for conference proceedings and similar as these often point to the main players and to good review articles. For bboks I use https://www.abebooks.com/ Good luck with your (re) search.

Hello,

There are some important tutoriel in the website of Abinit, see this link:

The atomic mass of Te is 127.6 u, so you need 5x127.6x10¹⁸ u Te/cm³ assuming every Te atom creates a carrier (if not, multiply by a corresponding factor). This corresponds to 1.06x10^-3g Te/cm³. The density of InSb is 5.75g/cm³. Now, technically you would have to sum up atom numbers and divide by the overall count, but the difference is negligible when we have orders of magnitude in difference as in this case. So you can approximate by 1.06x10^-3/5.75=1.8x10^-4 which equals 0,018%.

Dear Saifur Rahman Khan and Al,

lI wish you Happy New Year: success in your spiritual achievement, good health and prosperity for it!

I found the next on the facbook (https://www.facebook.com/USGSVolcanoes):

'A volcanologist is a person who studies volcanoes, but there are many different specialties within the field of volcanology. Which interests you and what steps should you take to achieve your goal? Find out more in #VolcanoWatch.

Earthquakes are one primary tool used to study volcanoes. A volcano seismologist studies the earthquakes that are generated as magma moves through Earth’s crust.

A volcano geodesist studies the deformation, or change in shape, of a volcano caused by the movement of magma and gases beneath the surface. Many features of volcanoes can be studied from space, as well, using satellite sensors. Tools like these provide clues about the state of the volcano.

Geologists and geochemists study the composition of lavas and gases to understand the source and style of the eruption. Measuring gas emissions is especially important, as the vog (volcanic air pollution) caused by toxic volcanic gases can contribute to breathing problems, acid rain, and agricultural problems downwind, especially during long-lived eruptions.

If you are interested in becoming a volcanologist, you’ll need to work toward a bachelor’s degree, preferably in a STEM field (Science, Technology, Engineering, and Math). Volcanologists frequently pursue degrees in geology, chemistry, physics, and/or mathematics, but that is not always the case. Oceanography, computer science, engineering, environmental science are all potential pathways, and the list goes on. Explore different fields to find what interests you most.

After achieving a bachelor’s degree, consider options for advanced degrees like a Masters or Doctorate. Many advanced degree programs in the sciences are funded, meaning tuition may be waived, and you might get a stipend for doing the work. Basically, you get paid instead of having to pay for school, and you gain valuable work experience at the same time.

You might consider working for the USGS or other agencies and companies. You have seen many photos of HVO scientists working during the eruptions of Mauna Loa and Kīlauea. The National Park Service also offers a variety of positions for people with either bachelor’s or advanced degrees, such as park geologists, archaeologists, botanists, guides, interpretive rangers, and law enforcement rangers. Science writing and journalism are also excellent ways to explore the excitement of volcanology, natural disasters, and cutting-edge science, while encouraging those passions in others. Similarly, eco- and geo-tourism are great ways to get close to the action and work outdoors, while also meeting, educating, and inspiring people from all over the world. Careers in emergency management will have you helping people stay informed during crises.

Check out usajobs.gov for positions within the federal government. There is even a special section for students and recent grads.

Volcano Activity Updates

#MaunaLoa is not erupting. Webcam imagery shows weak, residual incandescence intermittently in the inactive Northeast Rift Zone fissure 3 lava flow at night. Seismicity remains low and ground deformation rates have decreased. Sulfur dioxide (SO2) emission rates are at background levels. For Mauna Loa monitoring data, see: https://www.usgs.gov/volcanoes/mauna-loa/monitoring-data.

#Kilauea is not erupting. Lava supply to the Halemaʻumaʻu lava lake in Hawai‘i Volcanoes National Park ceased on December 9. Sulfur dioxide emission rates have decreased to near pre-eruption background levels and were last measured at approximately 200 tonnes per day (t/d) on December 14. Seismicity is elevated but stable, with few earthquakes. Over the past week, summit tiltmeters recorded several deflation-inflation (DI) events. For Kīlauea monitoring data, see https://www.usgs.gov/.../past-week-monitoring-data-kilauea.

There were three earthquakes with 3 or more felt reports in the Hawaiian Islands during the past week: a M3.3 earthquake 14 km (8 mi) S of Fern Forest at 7 km (4 mi) depth on Dec. 27 at 4:33 a.m. HST, a M3.4 earthquake 7 km (4 mi) WSW of Volcano at 2 km (1 mi) depth on Dec. 24 at 8:31 p.m. HST, and a M2.5 earthquake 1 km (0 mi) S of Mountain View at 11 km (7 mi) depth on Dec. 24 at 9:57 a.m. HST.

In the photo, an HVO technician adjusts a volcanic gas analysis instrument that was specifically designed for this Unoccupied Aircraft System (UAS) unit, which carries three one-liter analysis bags. The instrument transmits gas concentration information in real-time during the flight at Kīlauea summit. USGS has special use permits from the National Park Service to conduct official UAS missions as part of HVO's mission to monitor active volcanoes in Hawaii, assess their hazards, issue warnings, and advance scientific understanding to reduce impacts of volcanic eruptions. Launching, landing, or operating an unoccupied aircraft from or on lands and waters administered by the National Park Service within the boundaries of Hawai‘i Volcanoes National Park is prohibited under 36 CFR § 1.5 - Closures and public use limits.

USGS image taken January 14, 2022 by M. Warren.

#USGS #HVO #HawaiianVolcanoObservatory'

Maybe it can help you!

Regards,

Laszlo

Some common techniques for preparing biodegradable composites include:

This looks like you haven't done a good geometry optimisation for your structure yet. You need to make sure the forces and stresses are "zero" to a good tolerance.

Phonon calculation also need a good cut-off energy, density grid and k-point sampling, but the phonon calculations are time-consuming so doing the convergence the obvious way is computationally expensive. A good proxy property is the stress on the simulated cell, so I recommend that you do the convergence tests for single energy calculation of a fixed cell, and use the cell stress convergence to determine the cut-off energy etc.

Hope that helps,

Phil Hasnip

(CASTEP Developer)