Materials Engineering Research

Başlatan Karabasan, Tem 03, 2019, 06:34 ÖS

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Tem 03, 2019, 06:34 ÖS Last Edit: Tem 03, 2019, 06:36 ÖS by Karabasan

Nanotechnology enables engineers to weld previously un-weldable aluminum alloy

An aluminum alloy developed in the 1940s has long held promise for use in automobile manufacturing, except for one key obstacle. Although it's nearly as strong as steel and just one-third the weight, it is almost impossible to weld together using the technique commonly used to assemble body panels or engine parts.

That's because when the alloy is heated during welding, its molecular structure creates an uneven flow of its constituent elements -- aluminum, zinc, magnesium and copper -- which results in cracks along the weld.

Now, engineers at the UCLA Samueli School of Engineering have developed a way to weld the alloy, known as AA 7075. The solution: infusing titanium carbide nanoparticles -- particles so small that they're measured in units equal to one billionth of a meter -- into AA 7075 welding wires, which are used as the filler material between the pieces being joined. A paper describing the advance was published in Nature Communications.

Using the new approach, the researchers produced welded joints with a tensile strength up to 392 megapascals. (By comparison, an aluminum alloy known as AA 6061 that is widely used in aircraft and automobile parts, has a tensile strength of 186 megapascals in welded joints.) And according to the study, post-welding heat treatments, could further increase the strength of AA 7075 joints, up to 551 megapascals, which is comparable to steel.

Because it's strong but light, AA 7075 can help increase a vehicle's fuel and battery efficiency, so it's already often used to form airplane fuselages and wings, where the material is generally joined by bolts or rivets rather than welded. The alloy also has been used for products that don't require joining, such as smartphone frames and rock-climbing carabiners.

But the alloy's resistance to welding, specifically, to the type of welding used in automobile manufacturing, has prevented it from being widely adopted.

"The new technique is just a simple twist, but it could allow widespread use of this high-strength aluminum alloy in mass-produced products like cars or bicycles, where parts are often assembled together," said Xiaochun Li, UCLA's Raytheon Professor of Manufacturing and the study's principal investigator. "Companies could use the same processes and equipment they already have to incorporate this super-strong aluminum alloy into their manufacturing processes, and their products could be lighter and more energy efficient, while still retaining their strength."

The researchers already are working with a bicycle manufacturer on prototype bike frames that would use the alloy; and the new study suggests that nanoparticle-infused filler wires could also make it easier to join other hard-to-weld metals and metal alloys. Mesajı Paylaş
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Cold welding gibi bir method neden kullanılamıyordu Alüminyumda acaba? Biraz bakındım da bulamadım. Mesajı Paylaş


Cold welding de hot welding de onun için. Mesajı Paylaş


Ben mi anlamıyorum, yoksa bu bayağı önemli bir buluş değil mi? Mesajı Paylaş


Army hydrogen-generation discovery may spur new industry

Army officials announced the exclusive licensing of a new technology designed to harvest hydrogen from an aluminum alloy powder and any fluid that contains water.

"This is on-demand hydrogen production," said Dr. Anit Giri, a materials scientist at the U.S. Army CCDC Army Research Laboratory at Aberdeen Proving Ground, Maryland. "Utilizing hydrogen, you can generate power on-demand, which is very important for the Soldier."

Army researchers discovered a structurally-stable, aluminum-based nanogalvanic alloy powder in 2017, which reacts with water or any water-based liquid to produce on-demand hydrogen for power generation without a catalyst.

The laboratory filed for a patent and posted a Federal Register Notice in June 2018 inviting companies to submit ideas on how best to commercialize the technology.

"Based on ongoing interest generated by this notice, the Army is selecting the most appropriate partners and collaborators with an intent to build a manufacturing base," said Jason Craley, from the lab's Technology Transfer Office.

Although the 2018 notice had a set timeline for responding, Craley said the door is not closed on considering new licenses and partnerships for purposes not already covered by H2 Power. Interested parties may still contact the laboratory, he said.

"The initial notice generated a lot of interest," Giri said. "One company has already been given the exclusive license for a particular use, but several other companies are in negotiations."

The company that received the first rights is H2 Power, LLC of Chicago. According to the agreement, the license grants the right to use the patent in automotive and transportation power generation applications related to "2/3/4/6 wheeled vehicles, such as motorcycles, all sizes of cars, minivans, vans, SUV, pick-up trucks, panel trucks other light and medium trucks up to 26,000 pounds and any size bus."

H2 Power may also use the patent for power generation applications via "generators and micro-grid equipment that generates 15 kilowatts and above."

"Imagine a squad of future Soldiers on a long range patrol far from base with dead batteries and a desperate need to fire up their radio," said Dr. Kris Darling, a prominent materials scientist at CCDC ARL. "One of the Soldiers reaches for a metal tablet and drops it into a container and adds water or some fluid that contains water such as urine, immediately the tablet dissolves and hydrogen is released into a fuel cell, providing instant power for the radio."

The powder has many advantages, Darling said, such as:

• Energy and power source
• Stable alloy powder
• Environmentally friendly
• Scalable hydrogen production
• Easily transportable
• Feed stock for additive manufacturing

"This material is unique," Darling said. "It was discovered just a few years ago, so a manufacturing base doesn't exist. That's why we're going to work directly with the people licensing this technology -- so they can build the infrastructure and gain the manufacturing science and engineering to be able to rapidly scale this."

Having the manufacturing base is important, he said because it opens up the technology for commercial applications as well as DOD applications.

"It's important because the Army can benefit from this product being produced," Darling said.

Giri said he is already in close contact with H2 Power representatives.

"They are very excited about the potential applications," Giri said. "We're looking forward to working with them to make this technology available to the U.S. Army." Mesajı Paylaş
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AI techniques used to improve battery health and safety

Researchers have designed a machine learning method that can predict battery health with 10x higher accuracy than current industry standard, which could aid in the development of safer and more reliable batteries for electric vehicles and consumer electronics.

The researchers, from Cambridge and Newcastle Universities, have designed a new way to monitor batteries by sending electrical pulses into them and measuring the response. The measurements are then processed by a machine learning algorithm to predict the battery's health and useful lifespan. Their method is non-invasive and is a simple add-on to any existing battery system. The results are reported in the journal Nature Communications.

Predicting the state of health and the remaining useful lifespan of lithium-ion batteries is one of the big problems limiting widespread adoption of electric vehicles: it's also a familiar annoyance to mobile phone users. Over time, battery performance degrades via a complex network of subtle chemical processes. Individually, each of these processes doesn't have much of an effect on battery performance, but collectively they can severely shorten a battery's performance and lifespan.

Current methods for predicting battery health are based on tracking the current and voltage during battery charging and discharging. This misses important features that indicate battery health. Tracking the many processes that are happening within the battery requires new ways of probing batteries in action, as well as new algorithms that can detect subtle signals as they are charged and discharged.

"Safety and reliability are the most important design criteria as we develop batteries that can pack a lot of energy in a small space," said Dr Alpha Lee from Cambridge's Cavendish Laboratory, who co-led the research. "By improving the software that monitors charging and discharging, and using data-driven software to control the charging process, I believe we can power a big improvement in battery performance."

The researchers designed a way to monitor batteries by sending electrical pulses into it and measuring its response. A machine learning model is then used to discover specific features in the electrical response that are the tell-tale sign of battery aging. The researchers performed over 20,000 experimental measurements to train the model, the largest dataset of its kind. Importantly, the model learns how to distinguish important signals from irrelevant noise. Their method is non-invasive and is a simple add-on to any existing battery systems.

The researchers also showed that the machine learning model can be interpreted to give hints about the physical mechanism of degradation. The model can inform which electrical signals are most correlated with aging, which in turn allows them to design specific experiments to probe why and how batteries degrade.

"Machine learning complements and augments physical understanding," said co-first author Dr Yunwei Zhang, also from the Cavendish Laboratory. "The interpretable signals identified by our machine learning model are a starting point for future theoretical and experimental studies."

The researchers are now using their machine learning platform to understand degradation in different battery chemistries. They are also developing optimal battery charging protocols, powering by machine learning, to enable fast charging and minimise degradation.

This work was carried out with funding from the Faraday Institution. Dr Lee is also a Research Fellow at St Catharine's College. Mesajı Paylaş
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Sağda solda herkes uğraşıp duruyor ama bu kınuda en büyük proje Amerikan Enerji Bakanlığı destekli, Crabtree'nin JCESR'ı.

"Cey-Sizır" diye okuyorlar "Jül Sezar" gibi. :)

Bunlar asıl 200.000 kadar değişik kimyasalın modellemesini yapıyorlar. Bu çok büyük iş. Mesajı Paylaş


I will make public book sharing here.

Physics and Applications of Graphene(Published: April 19th 2011)
Edited by Sergey Mikhailov


1. Nano-Engineering of Graphene and Related Materials
By Zhiping Xu

2. Synthesis of Graphenes with Arc-Discharge Method
By Nan Li, Zhiyong Wang and Zujin Shi

3. Chemical Vapor Deposition of Graphene
By Congqin Miao, Churan Zheng, Owen Liang and Ya-Hong Xie

4. Epitaxial Graphene on SiC(0001): More Than Just Honeycombs
By Lian Li

5. Thermal Reduction of Graphene Oxide
By Seung Hun Huh

6. Graphene Etching on Well-Defined Solid Surfaces
By Toshio Ogino and Takahiro Tsukamoto

7. Transparent and Electrically Conductive Films from Chemically Derived Graphene
By Siegfried Eigler

8. Graphene-Based Nanocomposites
By Xin Wang and Sheng Chen

9. Graphene-Based Polymer Nanocomposites
By Horacio J. Salavagione, Gerardo Martínez and Gary Ellis

10. Functionalized Graphene Sheet / Polyurethane Nanocomposites
By Hyung-il Lee and Han Mo Jeong

11. Equilibrium Nucleation, Growth, and Thermal Stability of Graphene on Solids
By E.V.Rut'kov and N.R.Gall

12. Intercalation of Graphene Films on Metals with Atoms and Molecules
By E.V.Rut'kov and N.R.Gall

13. Electronic and Magnetic Properties of the Graphene- Ferromagnet Interfaces: Theory vs. Experiment
By Elena Voloshina and Yuriy Dedkov

14. Electronic Properties of Graphene Probed at the Nanoscale
By Filippo Giannazzo, Sushant Sonde and Vito Raineri

15. Scanning Transmission Electron Microscopy and Spectroscopy of Suspended Graphene
By Ursel Bangert, Mhairi Gass, Recep Zan and Cheng Ta Pan

16. Electrical Conductivity of Melt Compounded Functionalized Graphene Sheets Filled Polyethyleneterephthalate Composites
By Haobin Zhang, Shunlun He, Cao Chen, Wenge Zheng and Qing Yan

17. Non-Volatile Resistive Switching in Graphene Oxide Thin Films
By Fei Zhuge, Run-Wei Li, Congli He, Zhaoping Liu and Xufeng Zhou

18. Measuring Disorder in Graphene with Raman Spectroscopy
By Ado Jorio, Erlon H. Martins Ferreira, Luiz G. Cançado, Carlos A. Achete and Rodrigo B. Capaz

19. Superconductivity and Electron-Phonon Coupling in Graphite Intercalation Compunds
By Tonica Valla and Zhihui Pan

20. Graphene Transistors
By Kristóf Tahy, Tian Fang, Pei Zhao, Aniruddha Konar, Chuanxin Lian, Huili (Grace) Xing, Michelle Kelly and Debdeep Jena

21. Graphene Transistors and RF Applications
By Jeong-Sun Moon, Kurt Gaskill and Paul Campbell

22. Chemical and Biosensing Applications Based on Graphene Field-Effect Transistors
By Yasuhide Ohno, Kenzo Maehashi and Kazuhiko Matsumoto

23. Graphene-Supported Platinum and Platinum-Ruthenium Nanoparticles for Fuel Cell Applications
By Lifeng Dong, Qianqian Liu, Li Wang and Kezheng Chen

Graphene Simulation(Published: August 1st 2011)
Edited by Jian Ru Gong


1. DFT Calculation for Adatom Adsorption on Graphene
By Kengo Nakada and Akira Ishii

2. Structural and Electronic Properties of Graphene upon Molecular Adsorption: DFT Comparative Analysis
By Ali Zain Alzahrani

3. Computer Simulation of Radiation Defects in Graphene and Relative Structures
By Arkady Ilyin

4. Hydrogenation of Graphene and Hydrogen Diffusion Behavior on Graphene/Graphane Interface
By Zhimin Ao and Sean Li

5. Description of Adsorbed Phases on Carbon Surfaces: A Comparative Study of Several Graphene Models
By José L. Vicente and Alberto G. Albesa

6. Electronic States of Graphene-Based Ferromagnets
By Masashi Hatanaka

7. Nonlinear Transport Through Ultra Narrow Zigzag Graphene Naoribbons
By Hosein Cheraghchi

8. Field Emission from Graphene Nanosheets
By Takahiro Matsumoto, Tomonori Nakamura, Yoichiro Neo, Hidenori Mimura and Makoto Tomita

9. Theory of Defect Dynamics in Graphene
By L.L. Bonilla and A. Carpio

10. Symmetry and Lattice Dynamics
By Hui Tang, Bing-Shen Wang and Zhao-Bin Su

11. Universal Quantification of Chemical Bond Strength and Its Application to Low Dimensional Materials
By Bo Xu, Xiaoju Guo and Yongjun Tian

12. The Photoeffect on Graphene and Axion Detection by Graphene
By Miroslav Pardy

13. Planar Dirac Fermions in External Electromagnetic Fields
By Gabriela Murguía, Alfredo Raya and Ángel Sánchez

14. Nonlinear Plasmonics Near the Dirac Point in Negative-Zero-Positive Index Metamaterials-Optical Simulations of Electron in Graphene
By Ming Shen and Linxu Ruan

15. Zitterbewegung (Trembling Motion) of Electrons in Graphene
By Tomasz M. Rusin and Wlodek Zawadzki

16. Graphene and Cousin Systems
By L.B Drissi, E.H Saidi and M. Bousmina

17. Single-Particle States and Elementary Excitations in Graphene Bi-Wires: Minding the Substrate
By Cesar E.P. Villegas and Marcos R.S. Tavares

New Progress on Graphene Research(Published: March 27th 2013)
Edited by Jian Ru Gong


1. Electronic Tunneling in Graphene
By Dariush Jahani

2. Localised States of Fabry-Perot Type in Graphene Nano-Ribbons
By V. V. Zalipaev, D. M. Forrester, C. M. Linton and F. V. Kusmartsev

3. Electronic Properties of Deformed Graphene Nanoribbons
By Guo-Ping Tong

4. The Cherenkov Effect in Graphene-Like Structures
By Miroslav Pardy

5. Electronic and Vibrational Properties of Adsorbed and Embedded Graphene and Bigraphene with Defects
By Alexander Feher, Eugen Syrkin, Sergey Feodosyev, Igor Gospodarev, Elena Manzhelii, Alexander Kotlar and Kirill Kravchenko

6. Quantum Transport in Graphene Quantum Dots
By Hai-Ou Li, Tao Tu, Gang Cao, Lin-Jun Wang, Guang-Can Guo and Guo-Ping Guo

7. Advances in Resistive Switching Memories Based on Graphene Oxide
By Fei Zhuge, Bing Fu and Hongtao Cao

8. Surface Functionalization of Graphene with Polymers for Enhanced Properties
By Wenge Zheng, Bin Shen and Wentao Zhai

9. Graphene Nanowalls
By Mineo Hiramatsu, Hiroki Kondo and Masaru Hori Mesajı Paylaş
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