jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Seunguk Song | Aram Yoon | Sora Jang | Jason Lynch | Jihoon Yang | Juwon Han | Myeonggi Choe | Young Ho Jin | Cindy Yueli Chen | Yeryun Cheon | Jinsung Kwak | Changwook Jeong | Hyeonsik Cheong | Deep Jariwala | Zonghoon Lee | Soon‐Yong Kwon
Source: Nature Communications (article, 2023-08-07)
Field: Materials Science — Materials Chemistry

Fabrication of p-type 2D single-crystalline transistor arrays with Fermi-level-tuned van der Waals semimetal electrodes High-performance p-type two-dimensional (2D) transistors are fundamental for 2D nanoelectronics. However, the lack of a reliable method for creating high-quality, large-scale p-type 2D semiconductors and a suitable metallization process represents important challenges that need to be addressed for future developments of the field. Here, we report the fabrication of scalable p-type 2D single-crystalline 2H-MoTe<sub>2</sub> transistor arrays with Fermi-level-tuned 1T'-phase semimetal contact electrodes. By transforming polycrystalline 1T'-MoTe<sub>2</sub> to 2H polymorph via abnormal grain growth, we fabricated 4-inch 2H-MoTe<sub>2</sub> wafers with ultra-large single-crystalline domains and spatially-controlled single-crystalline arrays at a low temperature (~500 °C). Furthermore, we demonstrate on-chip transistors by lithographic patterning and layer-by-layer integration of 1T' semimetals and 2H semiconductors. Work function modulation of 1T'-MoTe<sub>2</sub> electrodes was achieved by depositing 3D metal (Au) pads, resulting in minimal contact resistance (~0.7 kΩ·μm) and near-zero Schottky barrier height (~14 meV) of the junction interface, and leading to high on-state current (~7.8 μA/μm) and on/off current ratio (~10<sup>5</sup>) in the 2H-MoTe<sub>2</sub> transistors. Materials science ; Optoelectronics ; Schottky barrier ; Fabrication ; Field-effect transistor ; Transistor ; Fermi level ; Semimetal ; Semiconductor ; Nanotechnology ; Nanoelectronics ; Silicon ; Electrical engineering ; Physics ; Voltage 2D Materials and Applications ; Advanced Thermoelectric Materials and Devices ; MXene and MAX Phase Materials
Authors: Estelle Deschamps | Valentina Calabrese | Isabelle Schmitz‐Afonso | Marie Hubert‐Roux | Denis Castagnos | Carlos Afonso
Source: Molecules (review, 2023-02-22)
Field: Chemistry — Spectroscopy

Advances in Ultra-High-Resolution Mass Spectrometry for Pharmaceutical Analysis Pharmaceutical analysis refers to an area of analytical chemistry that deals with active compounds either by themselves (drug substance) or when formulated with excipients (drug product). In a less simplistic way, it can be defined as a complex science involving various disciplines, e.g., drug development, pharmacokinetics, drug metabolism, tissue distribution studies, and environmental contamination analyses. As such, the pharmaceutical analysis covers drug development to its impact on health and the environment. Moreover, due to the need for safe and effective medications, the pharmaceutical industry is one of the most heavily regulated sectors of the global economy. For this reason, powerful analytical instrumentation and efficient methods are required. In the last decades, mass spectrometry has been increasingly used in pharmaceutical analysis both for research aims and routine quality controls. Among different instrumental setups, ultra-high-resolution mass spectrometry with Fourier transform instruments, i.e., Fourier transform ion cyclotron resonance (FTICR) and Orbitrap, gives access to valuable molecular information for pharmaceutical analysis. In fact, thanks to their high resolving power, mass accuracy, and dynamic range, reliable molecular formula assignments or trace analysis in complex mixtures can be obtained. This review summarizes the principles of the two main types of Fourier transform mass spectrometers, and it highlights applications, developments, and future perspectives in pharmaceutical analysis. Fourier transform ion cyclotron resonance ; Orbitrap ; Mass spectrometry ; Pharmaceutical drug ; Pharmaceutical industry ; Biochemical engineering ; Pharmaceutical sciences ; Drug discovery ; Chemistry ; Drug development ; Analytical technique ; Computer science ; Nanotechnology ; Process engineering ; Analytical Chemistry (journal) ; Drug ; Chromatography ; Engineering ; Materials science ; Medicine ; Pharmacology ; Organic chemistry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Hao Ma | Sen Yan | Xinyu Lu | Yi‐Fan Bao | Jia Liu | Langxing Liao | Kun Dai | Maofeng Cao | Xiaojiao Zhao | Hao Yan | Hailong Wang | Xiaohui Peng | Ningyu Chen | Hui-Shu Feng | Lilin Zhu | Guangbao Yao | Chunhai Fan | De‐Yin Wu | Binju Wang | Xiang Wang | Bin Ren
Source: Science Advances (article, 2023-11-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Rapidly determining the 3D structure of proteins by surface-enhanced Raman spectroscopy Despite great advances in protein structure analysis, label-free and ultrasensitive methods to obtain the natural and dynamic three-dimensional (3D) structures are still urgently needed. Surface-enhanced Raman spectroscopy (SERS) can be a good candidate, whereas the complexity originated from the interactions between the protein and the gradient surface electric field makes it extremely challenging to determine the protein structure. Here, we propose a deciphering strategy for accurate determination of 3D protein structure from experimental SERS spectra in seconds by simply summing SERS spectra of isolated amino acids in electric fields of different strength with their orientations in protein. The 3D protein structure can be reconstructed by comparing the experimental spectra obtained in a well-defined gap-mode SERS configuration with the simulated spectra. The gradient electric field endows SERS with a unique advantage to section biomolecules with atomic precision, which makes SERS a competent tool for monitoring biomolecular events under physiological conditions. Biomolecule ; Raman spectroscopy ; Electric field ; Surface-enhanced Raman spectroscopy ; Protein structure ; Spectroscopy ; Nanotechnology ; Biological system ; Materials science ; Chemistry ; Raman scattering ; Physics ; Optics ; Biology ; Biochemistry Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Wenhui Huang | Shenglong Ding | Xi-Yuan Zhao | Kai Li | Haitao Guo | Mingzhu Zhang | Qi Gu
Source: Materials Today Bio (review, 2023-05-02)
Field: Materials Science — Biomaterials

Collagen for neural tissue engineering: Materials, strategies, and challenges Neural tissue engineering (NTE) has made remarkable strides in recent years and holds great promise for treating several devastating neurological disorders. Selecting optimal scaffolding material is crucial for NET design strategies that enable neural and non-neural cell differentiation and axonal growth. Collagen is extensively employed in NTE applications due to the inherent resistance of the nervous system against regeneration, functionalized with neurotrophic factors, antagonists of neural growth inhibitors, and other neural growth-promoting agents. Recent advancements in integrating collagen with manufacturing strategies, such as scaffolding, electrospinning, and 3D bioprinting, provide localized trophic support, guide cell alignment, and protect neural cells from immune activity. This review categorises and analyses collagen-based processing techniques investigated for neural-specific applications, highlighting their strengths and weaknesses in repair, regeneration, and recovery. We also evaluate the potential prospects and challenges of using collagen-based biomaterials in NTE. Overall, this review offers a comprehensive and systematic framework for the rational evaluation and applications of collagen in NTE. Scaffold ; Neural tissue engineering ; Regeneration (biology) ; Tissue engineering ; Neural cell ; Neuroscience ; Nanotechnology ; Computer science ; Biochemical engineering ; Materials science ; Cell ; Engineering ; Biomedical engineering ; Biology ; Cell biology Electrospun Nanofibers in Biomedical Applications ; Nerve injury and regeneration ; Tissue Engineering and Regenerative Medicine
Authors: So Takamoto | Daisuke Okanohara | Qing‐Jie Li | Ju Li
Source: Journal of Materiomics (article, 2023-01-19)
Field: Materials Science — Materials Chemistry

Towards universal neural network interatomic potential • Universal interatomic potential for modeling chemically and structurally complex systems. • Tensorial message passing in equivariant graph convolutional network. • Iterative construction of a diverse and broad training dataset. • User-friendly atomistic simulation platform Matlantis™ powered by universal interatomic potential for 72 elements. Interatomic potential ; Graph ; Computer science ; Convolutional neural network ; Theoretical computer science ; Computational science ; Statistical physics ; Molecular dynamics ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Wail Al Zoubi | Bassem Assfour | A.W. Allaf | Stefano Leoni | Jee‐Hyun Kang | Young Gun Ko
Source: Journal of Energy Chemistry (article, 2023-03-09)
Field: Chemistry — Organic Chemistry

Experimental and theoretical investigation of high-entropy-alloy/support as a catalyst for reduction reactions Catalysis ; Materials science ; Alloy ; Porosity ; Metal ; Oxide ; Chemical engineering ; Selectivity ; Nanoparticle ; Phase (matter) ; Nanotechnology ; Chemistry ; Metallurgy ; Organic chemistry ; Composite material Nanomaterials for catalytic reactions ; Electrocatalysts for Energy Conversion ; Copper-based nanomaterials and applications
Authors: Yang Shi | Wenxian Zhang | Yi Xue | Jingjing Zhang
Source: Chemosensors (article, 2023-04-06)
Field: Chemistry — Spectroscopy

Fluorescent Sensors for Detecting and Imaging Metal Ions in Biological Systems: Recent Advances and Future Perspectives Metal ions play a crucial role in many biochemical processes, and when in a state of scarcity or surplus, they can lead to various diseases. Therefore, the development of a selective, sensitive, cost-effective, and fast-responding sensor to detect metal ions is critical for in vitro medical diagnostics. In recent years, fluorescent sensors have been extensively investigated as potent kits for the effective assessment of metal ions in living systems due to their high sensitivity, selectivity, ability to perform real-time, non-invasive monitoring, and versatility. This review is an overview of recent advances in fluorescent sensors for the detection and imaging of metal ions in biosystems from 2018 to date. Specifically, we discuss their application in detecting essential metal ions and non-essential metal ions for in vitro diagnostics, living cell imaging, and in vivo imaging. Finally, we summarize remaining challenges and offer a future outlook on the above topics. Nanotechnology ; Metal ions in aqueous solution ; Fluorescence ; Live cell imaging ; Ion ; Computer science ; Chemistry ; Materials science ; Biochemical engineering ; Physics ; Cell ; Engineering ; Biochemistry ; Optics Molecular Sensors and Ion Detection ; Electrochemical Analysis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Huaxin Gong | Shucheng Chen | Jeffrey B.‐H. Tok | Zhenan Bao
Source: Matter (article, 2023-07-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

An emerging class of carbon materials: Synthesis and applications of carbon flowers Carbon fibers ; Adsorption ; Nanotechnology ; Materials science ; Characterization (materials science) ; Porous medium ; Porosity ; Chemistry ; Composite material ; Organic chemistry ; Composite number Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced battery technologies research
Authors: Seonghwan Kim | Charles M. Schroeder | Nicholas E. Jackson
Source: ACS Polymers Au (article, 2023-03-29)
Field: Materials Science — Materials Chemistry

Open Macromolecular Genome: Generative Design of Synthetically Accessible Polymers A grand challenge in polymer science lies in the predictive design of new polymeric materials with targeted functionality. However, <i>de novo</i> design of functional polymers is challenging due to the vast chemical space and an incomplete understanding of structure-property relations. Recent advances in deep generative modeling have facilitated the efficient exploration of molecular design space, but data sparsity in polymer science is a major obstacle hindering progress. In this work, we introduce a vast polymer database known as the Open Macromolecular Genome (OMG), which contains synthesizable polymer chemistries compatible with known polymerization reactions and commercially available reactants selected for synthetic feasibility. The OMG is used in concert with a synthetically aware generative model known as Molecule Chef to identify property-optimized constitutional repeating units, constituent reactants, and reaction pathways of polymers, thereby advancing polymer design into the realm of synthetic relevance. As a proof-of-principle demonstration, we show that polymers with targeted octanol-water solubilities are readily generated together with monomer reactant building blocks and associated polymerization reactions. Suggested reactants are further integrated with Reaxys polymerization data to provide hypothetical reaction conditions (e.g., temperature, catalysts, and solvents). Broadly, the OMG is a polymer design approach capable of enabling data-intensive generative models for synthetic polymer design. Overall, this work represents a significant advance, enabling the property targeted design of synthetic polymers subject to practical synthetic constraints. Polymer ; Chemical space ; Polymerization ; Computer science ; Synthetic biology ; Materials science ; Nanotechnology ; Chemistry ; Organic chemistry ; Bioinformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Birgir Guomundsson | Gunnar Lorna
Source: Journal of Computational Intelligence in Materials Science (article, 2023-05-06)
Field: Materials Science — Materials Chemistry

Automated Design Using Machine Learning in Materials Engineering - An Explicit Forecasts Every discipline of physics, including materials science, has been profoundly influenced by the arrival of algorithmic breakthroughs in the domain of machine learning. Many important advances have been made by combining materials data (computed and measured) with different machine learning approaches to solve difficult problems like, creating effectual and extrapolative surrogate prototypes for a wide variety of material parameters, down-selecting and screening novel candidate materials for particular application, and structuring novel approaches to accelerate and enhance atomistic and molecular simulations. Although current implementations have shown some of the promise of data-enabled pathways, it has become evident that success in this area will depend on our capacity to interpret, explain, and justify the results of a machine learning approach on the basis of our professional knowledge in the field. This article reviews the most important machine learning applications in materials engineering. In addition, we present a short overview of a number of methods that have proven useful in deriving physically relevant insights, design-centric knowledge, and causal links from materials engineering. Last but not least, we highlight some of the next prospects and obstacles that the materials community will encounter in this dynamic and fast developing industry. Structuring ; Variety (cybernetics) ; Implementation ; Computer science ; Field (mathematics) ; Machine learning ; Artificial intelligence ; Domain (mathematical analysis) ; Data science ; Management science ; Industrial engineering ; Systems engineering ; Software engineering ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Fuel Cells and Related Materials
Authors: Atwakyire Moses | Ding Chen | Peng Wan | Siyuan Wang
Source: Materials Today Communications (article, 2023-10-11)
Field: Materials Science — Biomaterials

Prediction of electrochemical corrosion behavior of magnesium alloy using machine learning methods Random forest ; Materials science ; Corrosion ; Decision tree ; Magnesium ; Machine learning ; Artificial intelligence ; Artificial neural network ; Feature selection ; Magnesium alloy ; Predictive modelling ; Computer science ; Metallurgy Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition
Authors: Panpan Zhou | Xuezhang Xiao | Xinyu Zhu | Yongpeng Chen | Weiming Lü | Mingyuan Piao | Ziming Cao | Miao Lu | Fang Fang | Zhinian Li | Lijun Jiang | Lixin Chen
Source: Energy storage materials (article, 2023-09-13)
Field: Materials Science — Materials Chemistry

Machine learning enabled customization of performance-oriented hydrogen storage materials for fuel cell systems Hydrogen storage ; Materials science ; Hydrogen ; Personalization ; Process engineering ; Randomness ; Computer science ; Composite material ; Engineering Hydrogen Storage and Materials ; Catalysis and Hydrodesulfurization Studies ; Hybrid Renewable Energy Systems
Authors: Yaolong Zhang | Bin Jiang
Source: Nature Communications (article, 2023-10-12)
Field: Materials Science — Materials Chemistry

Universal machine learning for the response of atomistic systems to external fields Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in current machine learning models. This work proposes a universal field-induced recursively embedded atom neural network (FIREANN) model, which integrates a pseudo field vector-dependent feature into atomic descriptors to represent system-field interactions with rigorous rotational equivariance. This "all-in-one" approach correlates various response properties like dipole moment and polarizability with the field-dependent potential energy in a single model, very suitable for spectroscopic and dynamics simulations in molecular and periodic systems in the presence of electric fields. Especially for periodic systems, we find that FIREANN can overcome the intrinsic multiple-value issue of the polarization by training atomic forces only. These results validate the universality and capability of the FIREANN method for efficient first-principles modeling of complicated systems in strong external fields. Polarizability ; Computer science ; Dipole ; Universality (dynamical systems) ; Artificial neural network ; Force field (fiction) ; Molecular dynamics ; Statistical physics ; Physics ; Artificial intelligence ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Lenz Fiedler | Normand A. Modine | Steve Schmerler | Dayton J. Vogel | Gabriel Popoola | Aidan P. Thompson | Sivasankaran Rajamanickam | Attila Cangi
Source: npj Computational Materials (article, 2023-06-27)
Field: Materials Science — Materials Chemistry

Predicting electronic structures at any length scale with machine learning Abstract The properties of electrons in matter are of fundamental importance. They give rise to virtually all material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets. Modeling and simulation of such diverse applications rely primarily on density functional theory (DFT), which has become the principal method for predicting the electronic structure of matter. While DFT calculations have proven to be very useful, their computational scaling limits them to small systems. We have developed a machine learning framework for predicting the electronic structure on any length scale. It shows up to three orders of magnitude speedup on systems where DFT is tractable and, more importantly, enables predictions on scales where DFT calculations are infeasible. Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances materials science to frontiers intractable with any current solutions. Bottleneck ; Computer science ; Density functional theory ; Scaling ; Scale (ratio) ; Electronic structure ; Statistical physics ; Range (aeronautics) ; Computational science ; Physics ; Aerospace engineering ; Quantum mechanics ; Geometry ; Mathematics ; Engineering Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Sachit Mishra | Rajat Srivastava | Atta Muhammad | Amit Amit | Eliodoro Chiavazzo | Matteo Fasano | Pietro Asinari
Source: Scientific Reports (article, 2023-04-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The impact of physicochemical features of carbon electrodes on the capacitive performance of supercapacitors: a machine learning approach Hybrid electric vehicles and portable electronic systems use supercapacitors for energy storage owing to their fast charging/discharging rates, long life cycle, and low maintenance. Specific capacitance is regarded as one of the most important performance-related characteristics of a supercapacitor's electrode. In the current study, Machine Learning (ML) algorithms were used to determine the impact of various physicochemical properties of carbon-based materials on the capacitive performance of electric double-layer capacitors. Published experimental datasets from 147 references (4899 data entries) were extracted and then used to train and test the ML models, to determine the relative importance of electrode material features on specific capacitance. These features include current density, pore volume, pore size, presence of defects, potential window, specific surface area, oxygen, and nitrogen content of the carbon-based electrode material. Additionally, categorical variables as the testing method, electrolyte, and carbon structure of the electrodes are considered as well. Among five applied regression models, an extreme gradient boosting model was found to best correlate those features with the capacitive performance, highlighting that the specific surface area, the presence of nitrogen doping, and the potential window are the most significant descriptors for the specific capacitance. These findings are summarized in a modular and open-source application for estimating the capacitance of supercapacitors given, as only inputs, the features of their carbon-based electrodes, the electrolyte and testing method. In perspective, this work introduces a new wide dataset of carbon electrodes for supercapacitors extracted from the experimental literature, also giving an instance of how electrochemical technology can benefit from ML models. Supercapacitor ; Capacitive sensing ; Electrode ; Carbon fibers ; Computer science ; Materials science ; Capacitance ; Chemistry ; Composite material ; Composite number Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials