jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Martin Dornheim | Lars Baetcke | Etsuo Akiba | J.R. Ares | Tom Autrey | Jussara Barale | Marcello Baricco | Kriston Brooks | Nikolaos Chalkiadakis | Véronique Charbonnier | Steven T. Christensen | José M. Bellosta von Colbe | Mattia Costamagna | Erika Michela Dematteis | J.F. Fernandez | Thomas Gennett | David M. Grant | Tae Wook Heo | Michael Hirscher | Katherine E. Hurst | Mykhaylo Lototskyy | Oliver Metz | Paola Rizzi | Kouji Sakaki | Sabrina Sartori | Emmanuel Stamatakis | Alastair Stuart | A. K. Stubos | Gavin S. Walker | C. J. Webb | Brandon M. Wood | V.A. Yartys | Emmanuel Zoulias
Source: Progress in Energy (article, 2022-06-28)
Field: Materials Science — Materials Chemistry

Research and development of hydrogen carrier based solutions for hydrogen compression and storage Abstract Industrial and public interest in hydrogen technologies has risen strongly recently, as hydrogen is the ideal means for medium to long term energy storage, transport and usage in combination with renewable and green energy supply. In a future energy system, the production, storage and usage of green hydrogen is a key technology. Hydrogen is and will in future be even more used for industrial production processes as a reduction agent or for the production of synthetic hydrocarbons, especially in the chemical industry and in refineries. Under certain conditions material based systems for hydrogen storage and compression offer advantages over the classical systems based on gaseous or liquid hydrogen. This includes in particular lower maintenance costs, higher reliability and safety. Hydrogen storage is possible at pressures and temperatures much closer to ambient conditions. Hydrogen compression is possible without any moving parts and only by using waste heat. In this paper, we summarize the newest developments of hydrogen carriers for storage and compression and in addition, give an overview of the different research activities in this field. Hydrogen storage ; Energy carrier ; Hydrogen ; Hydrogen production ; Compressed hydrogen ; Hydrogen technologies ; Process engineering ; Renewable energy ; Environmental science ; Hydrogen economy ; Liquid hydrogen ; Waste management ; Materials science ; Computer science ; Chemistry ; Engineering ; Electrical engineering ; Organic chemistry Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Fuel Cells and Related Materials
Authors: Eric Kumi‐Barimah | Artitsupa Boontan | D.P. Steenson | Gin Jose
Source: Scientific Reports (article, 2022-07-06)
Field: Materials Science — Polymers and Plastics

Infrared optical properties modulation of VO2 thin film fabricated by ultrafast pulsed laser deposition for thermochromic smart window applications Over the years, vanadium dioxide, (VO<sub>2</sub>(M1)), has been extensively utilised to fabricate thermochromic thin films with the focus on using external stimuli, such as heat, to modulate the visible through near-infrared transmittance for energy efficiency of buildings and indoor comfort. It is thus valuable to extend the study of thermochromic materials into the mid-infrared (MIR) wavelengths for applications such as smart radiative devices. On top of this, there are numerous challenges with synthesising pure VO<sub>2</sub> (M1) thin films, as most fabrication techniques require the post-annealing of a deposited thin film to convert amorphous VO<sub>2</sub> into a crystalline phase. Here, we present a direct method to fabricate thicker VO<sub>2</sub>(M1) thin films onto hot silica substrates (at substrate temperatures of 400 °C and 700 °C) from vanadium pentoxide (V<sub>2</sub>O<sub>5</sub>) precursor material. A high repetition rate (10 kHz) femtosecond laser is used to deposit the V<sub>2</sub>O<sub>5</sub> leading to the formation of VO<sub>2</sub> (M1) without any post-annealing steps. Surface morphology, structural properties, and UV-visible optical properties, including optical band gap and complex refractive index, as a function of the substrate temperature, were studied and reported below. The transmission electron microscopic (TEM) and X-ray diffraction studies confirm that VO<sub>2</sub> (M1) thin films deposited at 700 °C are dominated by a highly texturized polycrystalline monoclinic crystalline structure. The thermochromic characteristics in the mid-infrared (MIR) at a wavelength range of 2.5-5.0 μm are presented using temperature-dependent transmittance measurements. The first-order phase transition from metal-to-semiconductor and the hysteresis bandwidth of the transition were confirmed to be 64.4 °C and 12.6 °C respectively, for a sample fabricated at 700 °C. Thermo-optical emissivity properties indicate that these VO<sub>2</sub> (M1) thin films fabricated with femtosecond laser deposition have strong potential for both radiative thermal management or control via active energy-saving windows for buildings, and satellites and spacecraft. Materials science ; Thin film ; Optoelectronics ; Thermochromism ; Transmittance ; Annealing (glass) ; Amorphous solid ; Electrochromism ; Pulsed laser deposition ; Infrared ; Band gap ; Monoclinic crystal system ; Optics ; Nanotechnology ; Composite material ; Crystal structure ; Chemistry Transition Metal Oxide Nanomaterials ; Ga2O3 and related materials ; ZnO doping and properties
Authors: Jeffrey A. Bennett | Milad Abolhasani
Source: Current Opinion in Chemical Engineering (article, 2022-05-16)
Field: Materials Science — Materials Chemistry

Autonomous chemical science and engineering enabled by self-driving laboratories Computer science ; Resource (disambiguation) ; Systems engineering ; Artificial intelligence ; Control engineering ; Biochemical engineering ; Engineering Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Advanced biosensing and bioanalysis techniques
Authors: Fuzhan Rahmanian | Jackson Flowers | Dan Guevarra | Matthias H. Richter | Maximilian Fichtner | Phillip Donnely | John M. Gregoire | Helge S. Stein
Source: Advanced Materials Interfaces (article, 2022-01-06)
Field: Materials Science — Materials Chemistry

Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration Abstract Materials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high‐throughput characterization, automatic analysis, and machine learning. Within a MAP, one or multiple autonomous feedback loops may aim to optimize materials for certain functional properties or to generate new insights. The scope of a given experiment campaign is defined by the range of experiment and analysis actions that are integrated into the experiment framework. Herein, the authors present a method for integrating many actions within a hierarchical experimental laboratory automation and orchestration (HELAO) framework. They demonstrate the capability of orchestrating distributed research instruments that can incorporate data from experiments, simulations, and databases. HELAO interfaces laboratory hardware and software distributed across several computers and operating systems for executing experiments, data analysis, provenance tracking, and autonomous planning. Parallelization is an effective approach for accelerating knowledge generation provided that multiple instruments can be effectively coordinated, which the authors demonstrate with parallel electrochemistry experiments orchestrated by HELAO. Efficient implementation of autonomous research strategies requires device sharing, asynchronous multithreading, and full integration of data management in experimental orchestration, which to the best of the authors’ knowledge, is demonstrated for the first time herein. Orchestration ; Computer science ; Modular design ; Distributed computing ; Automation ; Asynchronous communication ; Interface (matter) ; Software ; Throughput ; Scope (computer science) ; Modularity (biology) ; Implementation ; Systems engineering ; Computer architecture ; Software engineering ; Operating system ; Engineering Machine Learning in Materials Science ; Scientific Computing and Data Management ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Eunjae Shim | Joshua Kammeraad | Ziping Xu | Ambuj Tewari | Tim Cernak | Paul M. Zimmerman
Source: Chemical Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Predicting reaction conditions from limited data through active transfer learning Transfer and active learning have the potential to accelerate the development of new chemical reactions, using prior data and new experiments to inform models that adapt to the target area of interest. This article shows how specifically tuned machine learning models, based on random forest classifiers, can expand the applicability of Pd-catalyzed cross-coupling reactions to types of nucleophiles unknown to the model. First, model transfer is shown to be effective when reaction mechanisms and substrates are closely related, even when models are trained on relatively small numbers of data points. Then, a model simplification scheme is tested and found to provide comparative predictivity on reactions of new nucleophiles that include unseen reagent combinations. Lastly, for a challenging target where model transfer only provides a modest benefit over random selection, an active transfer learning strategy is introduced to improve model predictions. Simple models, composed of a small number of decision trees with limited depths, are crucial for securing generalizability, interpretability, and performance of active transfer learning. Transfer of learning ; Active learning (machine learning) ; Transfer (computing) ; Computer science ; Chemistry ; Artificial intelligence Machine Learning in Materials Science ; Machine Learning and Algorithms ; Computational Drug Discovery Methods
Authors: Jiao Chen | Jun Luo | M. Hu | Jun Zhou | Cheng Zhi Huang | Hui Liu
Source: Advanced Functional Materials (article, 2022-10-31)
Field: Materials Science — Materials Chemistry

Controlled Synthesis of Multicolor Carbon Dots Assisted by Machine Learning Abstract Carbon dots (CDs) have received extensive attention and applications in recent years due to their remarkable characteristics of tunable emission wavelength, high stability, and a variety of synthetic raw materials. Since the formation process and photoluminescence properties of CDs are affected by multiple factors, the luminescence regulation of CDs has always been a troublesome problem. Furthermore, it is still a lack of appropriate approaches to reveal the hidden rules between the synthesis conditions and the luminescence properties of CDs. Inspired by machine learning (ML) applications in molecular and materials science, herein, a data‐driven ML strategy is proposed to multi‐dimensionally investigate the correlation between reaction parameters and the photoluminescence properties of CDs. Meanwhile, it is demonstrated that reaction parameters and solvent properties have different influences on the fluorescence properties of CDs, and the intelligently optimizing synthesis route of CDs is achieved using ML algorithms. CDs with excellent luminescent properties screened by ML are further applied to high‐capacity colorful information encryption. This study provides an efficient ML‐assisted strategy to guide the synthesis of multicolor CDs, helping researchers to quickly and easily obtain CDs according to experimental requirements. Photoluminescence ; Materials science ; Luminescence ; Nanotechnology ; Fluorescence ; Carbon fibers ; Process (computing) ; Solvent ; Computer science ; Optoelectronics ; Organic chemistry Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Xiuyang Lü | Zhizhong Xie | Xuanjun Wu | Mengmeng Li | Weiquan Cai
Source: Chemical Engineering Science (article, 2022-06-17)
Field: Chemistry — Inorganic Chemistry

Hydrogen storage metal-organic framework classification models based on crystal graph convolutional neural networks Hydrogen storage ; Convolutional neural network ; Computer science ; Metal-organic framework ; Artificial neural network ; Graph ; Adsorption ; Artificial intelligence ; Hydrogen ; Algorithm ; Machine learning ; Chemistry ; Theoretical computer science ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning in Materials Science
Authors: Igor I. Baskin | A. E. Épshtein | Yair Ein‐Eli
Source: Journal of Molecular Liquids (article, 2022-01-29)
Field: Materials Science — Materials Chemistry

Benchmarking machine learning methods for modeling physical properties of ionic liquids Quantitative structure–activity relationship ; Artificial neural network ; Computer science ; Benchmarking ; Artificial intelligence ; Machine learning ; Transformer ; Ranking (information retrieval) ; Biological system ; Voltage Machine Learning in Materials Science ; Ionic liquids properties and applications ; Computational Drug Discovery Methods
Authors: Yi-Lun Liao | Tess Smidt
Source: arXiv (Cornell University) (preprint, 2022-06-23)
Field: Materials Science — Materials Chemistry

Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs Despite their widespread success in various domains, Transformer networks have yet to perform well across datasets in the domain of 3D atomistic graphs such as molecules even when 3D-related inductive biases like translational invariance and rotational equivariance are considered. In this paper, we demonstrate that Transformers can generalize well to 3D atomistic graphs and present Equiformer, a graph neural network leveraging the strength of Transformer architectures and incorporating SE(3)/E(3)-equivariant features based on irreducible representations (irreps). First, we propose a simple and effective architecture by only replacing original operations in Transformers with their equivariant counterparts and including tensor products. Using equivariant operations enables encoding equivariant information in channels of irreps features without complicating graph structures. With minimal modifications to Transformers, this architecture has already achieved strong empirical results. Second, we propose a novel attention mechanism called equivariant graph attention, which improves upon typical attention in Transformers through replacing dot product attention with multi-layer perceptron attention and including non-linear message passing. With these two innovations, Equiformer achieves competitive results to previous models on QM9, MD17 and OC20 datasets. Equivariant map ; Transformer ; Computer science ; Architecture ; Graph ; Theoretical computer science ; Topology (electrical circuits) ; Artificial intelligence ; Mathematics ; Pure mathematics ; Engineering ; Combinatorics ; Electrical engineering ; Voltage Machine Learning in Materials Science ; Advanced Graph Neural Networks
Authors: Tianwei He | Alain R. Puente Santiago | Shiyu Xia | Md Ariful Ahsan | Guobao Xu | Rafael Luque
Source: Advanced Energy Materials (article, 2022-04-22)
Field: Materials Science — Materials Chemistry

Experimental and Theoretical Advances on Single Atom and Atomic Cluster‐Decorated Low‐Dimensional Platforms towards Superior Electrocatalysts Abstract The fundamental relationship between structure and properties, which is called “structure‐property”, plays a vital role in the rational designing of high‐performance catalysts for diverse electrocatalytic applications. Low‐dimensional (LD) nanomaterials, including 0D, 1D, 2D materials, combined with low‐nuclearity metal atoms, ranging from single atoms to subnanometer clusters, are currently emerging as rising star nanoarchitectures for heterogeneous catalysis due to their well‐defined active sites and unbeatable metal utilization efficiencies. In this work, a comprehensive experimental and theoretical review is provided on the recent development of single atom and atomic cluster‐decorated LD platforms towards some typical clean energy reactions, such as water‐splitting, nitrogen fixation, and carbon dioxide reduction reactions. The upmost attractive structural properties, advanced characterization techniques, and theoretical principles of these low‐nuclearity electrocatalysts as well as their applications in key electrochemical energy devices are also elegantly discussed. Materials science ; Nanotechnology ; Cluster (spacecraft) ; Nanomaterials ; Catalysis ; Atom (system on chip) ; Electrochemistry ; Water splitting ; Physical chemistry ; Computer science ; Electrode ; Photocatalysis ; Chemistry Nanocluster Synthesis and Applications ; Ammonia Synthesis and Nitrogen Reduction ; Advanced Photocatalysis Techniques
Authors: Yi Wang | Jun Chen | Yaxu Zhong | Soojin Jeong | Ruipeng Li | Xingchen Ye
Source: Journal of the American Chemical Society (article, 2022-07-21)
Field: Materials Science — Materials Chemistry

Structural Diversity in Dimension-Controlled Assemblies of Tetrahedral Gold Nanocrystals Polyhedron packings have fascinated humans for centuries and continue to inspire scientists of modern disciplines. Despite extensive computer simulations and a handful of experimental investigations, understanding of the phase behaviors of synthetic tetrahedra has remained fragmentary largely due to the lack of tetrahedral building blocks with tunable size and versatile surface chemistry. Here, we report the remarkable richness of and complexity in dimension-controlled assemblies of gold nanotetrahedra. By tailoring nanocrystal interactions from long-range repulsive to hard-particle-like or to systems with short-ranged directional attractions through control of surface ligands and assembly conditions, nearly a dozen of two-dimensional and three-dimensional superstructures including the cubic diamond and hexagonal diamond polymorphs are selectively assembled. We further demonstrate multiply twinned icosahedral supracrystals by drying aqueous gold nanotetrahedra on a hydrophobic substrate. This study expands the toolbox of the superstructure by design using tetrahedral building blocks and could spur future computational and experimental work on self-assembly and phase behavior of anisotropic colloidal particles with tunable interactions. Tetrahedron ; Nanotechnology ; Diamond ; Icosahedral symmetry ; Chemistry ; Nanocrystal ; Chemical physics ; Diamond cubic ; Phase (matter) ; Crystallography ; Materials science Pickering emulsions and particle stabilization ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanomaterials and Printing Technologies
Authors: Haonan Wang | Tianhua Xu | Yaoxin Fu | Ziyihui Wang | Mark S. Leeson | Junfeng Jiang | Tiegen Liu
Source: Biosensors (review, 2022-08-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Liquid Crystal Biosensors: Principles, Structure and Applications Liquid crystals (LCs) have been widely used as sensitive elements to construct LC biosensors based on the principle that specific bonding events between biomolecules can affect the orientation of LC molecules. On the basis of the sensing interface of LC molecules, LC biosensors can be classified into three types: LC-solid interface sensing platforms, LC-aqueous interface sensing platforms, and LC-droplet interface sensing platforms. In addition, as a signal amplification method, the combination of LCs and whispering gallery mode (WGM) optical microcavities can provide higher detection sensitivity due to the extremely high quality factor and the small mode volume of the WGM optical microcavity, which enhances the interaction between the light field and biotargets. In this review, we present an overview of the basic principles, the structure, and the applications of LC biosensors. We discuss the important properties of LC and the principle of LC biosensors. The different geometries of LCs in the biosensing systems as well as their applications in the biological detection are then described. The fabrication and the application of the LC-based WGM microcavity optofluidic sensor in the biological detection are also introduced. Finally, challenges and potential research opportunities in the development of LC-based biosensors are discussed. Biosensor ; Biomolecule ; Whispering-gallery wave ; Nanotechnology ; Interface (matter) ; Materials science ; Liquid crystal ; Optoelectronics ; Resonator Liquid Crystal Research Advancements ; Photonic and Optical Devices ; Semiconductor Lasers and Optical Devices
Authors: Chingrishon Kathing | G. S. S. Saini
Source: Recent Progress in Materials (review, 2022-12-30)
Field: Chemistry — Analytical Chemistry

A Review of Various Treatment Methods for the Removal of Dyes from Textile Effluent Wastewater generated by the textile industry has been a major environmental concern for a long. Production of fiber involves various steps and uses a lot of chemicals, dyes, and water. Therefore, the effluent produced from the textile industry needs proper purification before discharging into the water body. The current review summarizes various physical and chemical methods like ion exchange, coagulation-flocculation, membrane separation, membrane distillation, oxidation, ozonation, etc., for wastewater treatment. Along with this, adsorption methods, the various adsorbents used to purify wastewater, and the mechanism involved in adsorption have also been discussed. The biological method utilizes various microbes (bacteria, fungi, algae, and yeast) as a whole and the enzymes (laccase and azoreductase) secreted by them for wastewater treatment, which have been considered more feasible than physical and chemical methods. The adsorption and biological methods are better than other techniques due to their ability to degrade diverse classes of dye, less accumulation of harmless sludge, and cost-effective and safer approach for the disposal of textile effluent. While physical and chemical methods are expensive and generate toxic sludge, which is difficult to decompose. Effluent ; Wastewater ; Flocculation ; Waste management ; Pulp and paper industry ; Textile industry ; Adsorption ; Sewage treatment ; Chemistry ; Environmental science ; Chemical industry ; Environmental engineering ; Organic chemistry ; Engineering Dye analysis and toxicity ; Enzyme-mediated dye degradation