jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jordan Stone | Mira Khair | Steven J. Cavazos | Elizabeth S. Sooby
Source: Frontiers in Nuclear Engineering (article, 2024-01-12)
Field: Materials Science — Materials Chemistry

High-temperature thermal imaging to inform the arc-melt synthesis of nuclear materials The advancement of nuclear energy technology necessitates the development of novel materials and synthesis methods to produce materials which enable new fuel cycles. Alongside the maturation of R&amp;amp;D scale technologies to produce these materials, there is an ongoing effort to develop in situ monitoring capabilities to reduce the time to the discovery and development of these fuels. Monitoring data can be leveraged in artificial intelligence platforms to detect phenomena which lead to varied macro- and microstructural features which impact the application and performance of samples synthesized. The present study presents early-stage findings of the implementation of high-temperature, high-frame-rate infrared thermal imaging to monitor the arc-melt synthesis of novel fuels and compounds relevant to advanced nuclear reactors. The study illustrates both the challenges and opportunities of this methodology, highlighting the importance of internal standards while determining emissivity and transmission values as well as visualizing volatilization during melt synthesis. Emissivity ; Process engineering ; Volatilisation ; Materials science ; Scale (ratio) ; Systems engineering ; Computer science ; Environmental science ; Engineering ; Chemistry Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Physics and Applications
Authors: Taimin Yang | David G. Waterman | Z. Chu | James Beilsten‐Edmands | Zhehao Huang | Xiaodong Zou
Source: Research Square (preprint, 2024-11-05)
Field: Materials Science — Materials Chemistry

Serial chemical crystallography for autonomous quantitative phase analysis in an electron microscope Electron microscope ; Phase (matter) ; Microscope ; Crystallography ; Materials science ; Analytical Chemistry (journal) ; Computer science ; Chemistry ; Optics ; Physics ; Chromatography Crystallization and Solubility Studies ; Enzyme Structure and Function ; X-ray Diffraction in Crystallography
Authors: Hemant Verma | Jyoti Singh | Sarita Passey
Source: RASAYAN Journal of Chemistry (article, 2024-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

PYTHON PROGRAM FOR STRUCTURE AND MOLECULAR FORMULA OF ORGANIC COMPOUNDS: AN ALTERNATIVE THEORETICAL APPROACH A chemist always desires the automation of a chemistry laboratory as it reduces their workload by minimizing the time, energy, and effort. Automation when coupled with artificial intelligence is the need in today’s era as it gives more flexibility and power to the chemist. Using computer programs for a large number of such operations would further ease out these tasks as the desired objectives may be achieved with the click of a mouse. In this research article, the Python program has been used to arrive at the empirical and molecular formulas of some compounds. Python code has also been written for some compounds and their 2D and 3D structures drawn henceforth. With the introduction of Python language at the undergraduate level, this paper would allow chemistry students to make use of Python in organic chemistry as well. As a result, the chemistry students will be able to use the Python program for verification and classroom-based structure determination of various organic compounds. The students thus gain access and become equipped with computational skills that will be helpful in the modern world and can utilize the Python code as an alternative theoretical approach for these operations. While Python provides valuable computational tools, integrating thematic chemistry and new methodologies is crucial for advancing structural analysis. This approach enhances the depth and applicability of theoretical models beyond basic calculations. This paper is a perfect amalgamation of computer programming with organic chemistry and is an attempt to equip the chemists to do the above-mentioned operations theoretically from their desks. Python (programming language) ; Computer science ; Computational chemistry ; Statistical physics ; Chemistry ; Programming language ; Physics Various Chemistry Research Topics
Authors: Atsushi Kitada | T. Otsuki | T. Kanno | Norio Takenaka
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

A Computational Approach to Glyme-Based Room Temperature Aluminum Electrodeposition Baths A computational data-driven approach is applied to the design of liquid electrolyte materials. A case study is conducted targeting glyme-based room temperature aluminum (Al) electroplating solutions, where how to achieve higher plating speed has been an open issue. The determination of stable Al-Cl-glyme complex structures by density functional theory (DFT) calculations enables the modeling for molecular dynamics (MD) simulations of the bulk electrolytes, namely aluminum chloride (AlCl 3 )-diglyme (G2)-cosolvent system. It is shown that a tridentate coordination of diglyme (G2) to [AlCl 2 ] + is robust against desolvation and one-electron reduction, suggesting that the diffusion coefficients of the [AlCl 2 ] + cationic complex is an indication of faster plating. Additionally, the calculated diffusion coefficients correlate weakly with the relative permittivity data of cosolvents in their pure state, but strongly with viscosity data. This relationship can be used to design Al plating solutions. Figure 1 Aluminium ; Materials science ; Chemical engineering ; Metallurgy ; Engineering Anodic Oxide Films and Nanostructures
Authors: Daisuke AOKI | Shigetaka Tsubouchi
Source: Kobunshi (article, 2024-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Compound Discovery AI Chemicals Informatics In recent years, material manufacturers have been promoting efficient and effective material development using data science and informatics. Such technologies can contribute to develop new functional materials while reducing environmental impact, such as waste and energy consumption. This article introduces “Chemicals Informatics (CI),” which uses artificial intelligence (AI) technology to search for compounds with promising properties, and compares it with Materials Informatics (MI). The CI extracts promising compounds, and the MI optimizes the combination ratios. The CI is equipped with three unique AI technologies: natural language processing (NLP), new compound generation, and compound search. These AI technologies can realize comprehensive and high-speed compound searches from the vast amount of compound data publicly available in the world. By applying the compound search technology, it is also possible to search for the optimal combination of different types of compounds. As a specific example, we show a search for additives that achieves both the strength and degradability of bio-based polylactic acid. Informatics ; Data science ; Computer science ; Engineering History and advancements in chemistry ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Wissam A. Saidi
Source:  (article, 2024-10-06)
Field: Materials Science — Materials Chemistry

Revolutionizing Materials Design: The Intersection of Quantum Mechanics and Data Modeling The field of materials design is currently experiencing a notable evolution, driven by the convergence of sophisticated computational methodologies based on first principles and data-driven modeling approaches. I will review our recent endeavors employing AI/ML to expedite first-principles simulations and mitigate traditional methods&#39; temporal and spatial limitations. Central to our efforts is developing and utilizing ML interatomic potentials (MLPs) across a diverse spectrum of materials. We show that MLPs serve as invaluable tools for navigating the complexities of the simulations, such as understanding the behavior of MgO at extreme environments of ~1 terapascal and temperatures &gt;10,000 Kelvin. Moreover, we show that MLPs can provide precise details of the intricate dynamics governing the oxidation processes of binary alloy systems due to the competition between surface segregation and reconstruction tendencies. In summation, advancements in MLPs open the door to fresh possibilities in material modeling and, ultimately, discovery. Intersection (aeronautics) ; Computer science ; Engineering ; Aerospace engineering Machine Learning in Materials Science ; Nanotechnology research and applications
Authors: Pengju Yu | Yanwei Dai | Fei Qin
Source:  (article, 2024-08-07)
Field: Materials Science — Ceramics and Composites

A Machine Learning Framework to Predict the Thermal Fatigue Lifetime of SiC Module with Sintered Silver Layer Sintered nano-silver is a widely used interconnect material in the third generation of power semiconductors, and is often used for service in extremely high-temperature environments due to its advantages of high melting temperature, making the reliability of interconnection more and more important for the overall reliability of the third-generation power semiconductor device. Different from traditional fatigue life prediction methods, a thermal fatigue lifetime dataset is collected based on Coffin-Manson thermal fatigue model and finite element simulations in this paper. A data-driven method based on the Support Vector Regression (SVR) model for predicting fatigue lifetime of sintered nano-silver layer is developed. The analysis includes identifying the key factors influencing interconnect reliability, and determining the main factors affecting the thermal fatigue lifetime of SiC modules. The results show that increasing the thickness of the sintered nano-silver interconnecting layer can enhance the lifetime of the interconnect structure, while elastic modulus and chip thickness negatively impact the lifetime of the sintered silver layer. The prediction accuracy and stability of the proposed data-driven SVR model are discussed and studied. Layer (electronics) ; Thermal ; Thermal fatigue ; Materials science ; Computer science ; Composite material ; Physics Advanced ceramic materials synthesis ; Advanced machining processes and optimization ; Aluminum Alloys Composites Properties
Authors: Ramesh Chand Tiwari
Source: International Journal of Advanced Chemistry Research (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Nano-scale metal oxide clusters: Synthesis, properties, and applications in environmental remediation Nano-scale metal oxide clusters (NMOCs) have emerged as a critical class of materials with remarkable physicochemical properties, enabling a wide spectrum of environmental remediation technologies. Their high surface-area-to-volume ratio, tunable band gaps, and reactive surfaces make them ideal candidates for catalytic degradation, adsorption, sensing, and photocatalysis of environmental pollutants. This review explores the synthesis routes and mechanisms of NMOCs, investigates their structural and functional characteristics, and critically examines their role in removing heavy metals, dyes, organic compounds, and pathogens from contaminated environments. Particular attention is paid to TiO₂, ZnO, Fe₃O₄, and CeO₂-based clusters, among others. Data-driven analysis compares removal efficiencies, reusability, cost implications, and degradation pathways. The review concludes by discussing limitations, regulatory challenges, and future directions for large-scale integration of NMOCs in environmental engineering systems. Environmental remediation ; Oxide ; Photocatalysis ; Environmental degradation ; Degradation (telecommunications) ; Catalysis Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques ; TiO2 Photocatalysis and Solar Cells
Authors: Postiaux, Annaëlle | Denis, Julien | Richel, Aurore
Source: Open Repository and Bibliography (University of Liège) (article, 2024-04-12)
Field: Materials Science — Biomaterials

Food by-products: opportunities and applications ​ Postiaux Annaëlle ​ The by-products of the agri-food industry represent a wide range of underutilized organic matrices. Among these by-products, whose nature and quantity vary depending on geographical regions, production processes, and other factors (including seasonal ones), we find residues from the processing of cereals (straw, etc.), pseudo-cereals (e.g., quinoa straw), fruit and vegetable processing industries (peels, seeds, etc.), oilseed and proteinaceous material conversion industries, etc. These by-products are characterized by different chemical compositions, the understanding of which is a prerequisite to better target relevant opportunities for valorization from a techno-economic perspective. In this regard, this presentation illustrates which agri-food by-products are most suitable for valorization in the field of materials (specifically as food packaging). Various conversion techniques and processes are illustrated, along with applications (bio-sourced biodegradable, non-biodegradable, edible packaging, active and intelligent packaging). An analysis of recommendations proposed by the scientific community is presented, as well as a discussion of the importance of artificial intelligence in this research field. Food processing ; Production (economics) ; Food industry ; Food products ; Presentation (obstetrics) ; Field (mathematics) Nanocomposite Films for Food Packaging ; Food Chemistry and Fat Analysis ; Food composition and properties
Authors: Richel, Aurore
Source: Open Repository and Bibliography (University of Liège) (article, 2024-01-25)
Field: Materials Science — Biomaterials

Food by-products, opportunities to create new packaging The by-products of the agri-food industry represent a wide range of underutilized organic matrices. Among these by-products, whose nature and quantity vary depending on geographical regions, production processes, and other factors (including seasonal ones), we find residues from the processing of cereals (straw, etc.), pseudo-cereals (e.g., quinoa straw), fruit and vegetable processing industries (peels, seeds, etc.), oilseed and proteinaceous material conversion industries, etc. These by-products are characterized by different chemical compositions, the understanding of which is a prerequisite to better target relevant opportunities for valorization from a techno-economic perspective. In this regard, this presentation illustrates which agri-food by-products are most suitable for valorization in the field of materials (specifically as food packaging). Various conversion techniques and processes are illustrated, along with applications (bio-sourced biodegradable, non-biodegradable, edible packaging, active and intelligent packaging). An analysis of recommendations proposed by the scientific community is presented, as well as a discussion of the importance of artificial intelligence in this research field. Food processing ; Production (economics) ; Food industry ; Presentation (obstetrics) ; Food products ; Field (mathematics) Nanocomposite Films for Food Packaging ; Consumer Packaging Perceptions and Trends ; Food Chemistry and Fat Analysis
Authors: Kejie Zhao
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Chemistry — Electrochemistry

(Invited) Dynamics and Heterogeneity of Particle Network in Composite Electrodes We use a data-driven approach to assess the heterogeneous electrochemistry and mechanics in composite cathodes. We visualize the morphological defects at multi-scales ranging from the macroscopic composite, particle ensembles, to individual single particles. Particle fracture and interfacial debonding are identified in a large set of tomographic data. The mechanical damage of active particles is highly heterogeneous. The difference originates from the polarization of the electrolyte potential, various local conducting environments, and thus the non-uniform distribution of the activation energy for the charge transfer reaction. We model the kinetics of intergranular fracture and interfacial degradation to assess the heterogeneous mechanical damage in composite electrodes using microstructure-informed mechanics modeling. We quantify the influence of the mechanical damage on the metrics of battery performance. More interestingly, the interfacial failure reconstructs the conductive network and redistribute the electrochemical activities that render a dynamic nature of electrochemistry and mechanics evolving over time in the composite electrodes. Composite number ; Particle (ecology) ; Dynamics (music) ; Particle dynamics ; Materials science ; Computer science ; Statistical physics ; Composite material ; Physics ; Biology ; Molecular dynamics ; Acoustics ; Ecology Electrochemical Analysis and Applications
Authors: Xiaoming Zhang | Chen Du | Yin-Jian Song | Ji‐Dong Liu | Zheng‐Bai Zhao | Jiangang Gao | Xiang‐Yun Du
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Organic Chemistry

Facile Preparation of Self-Healing, Adhesive and Patterned Gels Via Frontal Polymerizations for Wearable Sensing and Actuating Applications Adhesive ; Self-healing ; Materials science ; Wearable computer ; Nanotechnology ; Computer science ; Embedded system ; Medicine Photopolymerization techniques and applications ; Advanced Polymer Synthesis and Characterization ; Additive Manufacturing and 3D Printing Technologies
Authors: Alis Pataquiva-Mateus
Source:  (article, 2024-06-06)
Field: Materials Science — Biomaterials

E-cellulose: symbiotic cultivation for the production of smart textiles in a framework of sustainable fashion and electronic art Technology has become ubiquitous, it is all around us, being a permanent part of our day to day. Along with the emergence of the Internet of Things (IoT) and other innovative technologies such as Augmented Reality (AR), Artificial Intelligence (AI), Blockchain, IoT-based smart wearables and apparel harmonize functionality, with design, electronic art, and science. On the other hand, the textile industry is considered the second most polluting industry in the world, making it necessary to rethink and replace ordinary products in great global demand produced from unsustainable processes with ones that contribute to the reduction of the greatest number of environmental problems. For thus, the synthesis and structuring at nanoscale of an extracellular renewable biopolymer is proposed. This bacterial cellulose (BC), obtained from a fermentative process that requires low amounts of water for its production, it has been demonstrated having an excellent potential in the slow fashion movement due to low hydric and carbon footprint, and diminishing of tree falling or use of non-sustainable fibers; as well as, into the textile industry due to its low-cost raw materials, mechanical properties, and versatility. Production (economics) ; Carbon footprint ; Textile industry ; Textile ; Computer science ; Structuring ; Wearable computer ; Business ; Materials science ; Ecology ; Embedded system Advanced Cellulose Research Studies
Authors: Krystal Vasquez
Source: C&EN Global Enterprise (article, 2024-11-11)
Field: Materials Science — Materials Chemistry

AI generates mainly White, male chemists Artificial intelligence image generators have developed a reputation for bias. For instance, Bloomberg reported that when prompted to generate images of different professions, the AI model Stable Diffusion mainly created White men , particularly in higher-paying occupations. Valeria Stepanova, a professor of chemistry at Viterbo University, wanted to know if this observation applied to her scientific discipline. “I’ve always been interested in how diversity is represented in my field,” Stepanova says. So she and her colleagues tested four AI image generators—Adobe Firefly, DALL-E 2, Craiyon, and DreamStudio—providing each with carefully engineered prompts pertaining to chemistry-specific occupations. The researchers then classified the gender and race of 200 AI-generated chemists, giving each an assignment of male or female and identifying each as African American, American Indian, Asian and Pacific Islander, Hispanic, or White. The researchers scanned every image for visible signs of disability. Stepanova and her team found that, together, the AI White (mutation) ; Chemistry ; Biochemistry Machine Learning in Materials Science
Authors: C EN editorial staff
Source: C&EN Global Enterprise (article, 2024-11-18)
Field: Materials Science — Materials Chemistry

New nuclear should power decarbonization It wouldn’t be that hard to cut greenhouse gas emissions if we could source abundant and affordable energy without releasing carbon dioxide. Nuclear power has the potential to deliver just that: a stable, large-scale energy source with few CO 2 emissions. Although renewable electricity such as wind and solar can also power electrolytic hydrogen production, nuclear has the unique ability to provide constant low-CO 2 electricity 24/7, heedless of sunlight and weather—a critical advantage for commercial-scale plants, which also run around the clock. Some readers may be heartened, then, that nuclear power has experienced a dramatic comeback in popularity over the past year or so. But there is a curious twist. Utilities are building and restarting nuclear power plants not to decarbonize but to fuel the energy demands of artificial intelligence systems and data centers. In September, Microsoft signed a contract with Constellation in which the software giant will pay Nuclear power ; Power (physics) ; Nuclear engineering ; Environmental science ; Engineering ; Physics ; Nuclear physics ; Thermodynamics Graphite, nuclear technology, radiation studies