jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Miharu Eguchi | Minsu Han | Yusuke Asakura | Jonathan P. Hill | Joel Henzie | Katsuhiko Ariga | Alan E. Rowan | Watcharop Chaikittisilp | Yusuke Yamauchi
Source: Angewandte Chemie International Edition (review, 2023-07-24)
Field: Materials Science — Materials Chemistry

Materials Space‐Tectonics: Atomic‐level Compositional and Spatial Control Methodologies for Synthesis of Future Materials Reactions occurring at surfaces and interfaces necessitate the creation of well-designed surface and interfacial structures. To achieve a combination of bulk material (i.e., framework) and void spaces, a meticulous process of "nano-architecting" of the available space is necessary. Conventional porous materials such as mesoporous silica, zeolites, and metal-organic frameworks lack advanced cooperative functionalities owing to their largely monotonous pore geometries and limited conductivities. To overcome these limitations and develop functional structures with surface-specific functions, the novel materials space-tectonics methodology has been proposed for future materials synthesis. This review summarizes recent examples of materials synthesis based on designing building blocks (i.e., tectons) and their hybridization, along with practical guidelines for implementing materials syntheses and state-of-the-art examples of practical applications. Lastly, the potential integration of materials space-tectonics with emerging technologies, such as materials informatics, is discussed. Nanotechnology ; Space (punctuation) ; Materials science ; Tectonics ; Computer science ; Process (computing) ; Mesoporous material ; Systems engineering ; Engineering ; Geology ; Chemistry Mesoporous Materials and Catalysis ; Catalytic Processes in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Venkateshkumar Prabhakaran | Grant E. Johnson | Julia Laskin
Source: Current Opinion in Electrochemistry (article, 2023-05-15)
Field: Chemistry — Electrochemistry

Ion soft landing: A unique tool for understanding electrochemical processes Characterization (materials science) ; Electrochemistry ; Ion ; Deposition (geology) ; Electrolyte ; Redox ; Electrode ; Nanotechnology ; Materials science ; Mass spectrometry ; Electrochemical energy conversion ; Electron transfer ; Chemical physics ; Chemical engineering ; Chemistry ; Inorganic chemistry ; Physical chemistry ; Organic chemistry ; Chromatography Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Electrochemical sensors and biosensors
Authors: Jiang Li | Deqiang Yin | Yi Qin
Source: Applied Surface Science (article, 2023-12-21)
Field: Materials Science — Materials Chemistry

An investigation of the thermal stability and mechanical properties of carbon framework materials hybridized by graphene and double benzene rings through combining first-principles calculations and classical molecular dynamic simulations Graphene ; Materials science ; Thermal stability ; Benzene ; Carbon fibers ; Molecular dynamics ; Metastability ; Chemical physics ; Composite material ; Shear modulus ; Ultimate tensile strength ; Nanotechnology ; Computational chemistry ; Chemistry ; Organic chemistry ; Composite number Graphene research and applications ; Supercapacitor Materials and Fabrication ; Boron and Carbon Nanomaterials Research
Authors: Vancho Kocevski | James A. Valdez | Benjamin K. Derby | Y.Q. Wang | Ghanshyam Pilania | Blas P. Uberuaga
Source: Materials Advances (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Predicting and accessing metastable phases Metastable phase formation of Ln 2 O 3 phases was explained and successfully predicted using calculated metastable phase diagrams. Irradiation experiments of Lu 2 O 3 show formation of 3 phases, confirming the prediction, showing a unique behavior. Metastability ; Phase (matter) ; Phase diagram ; Materials science ; Irradiation ; Crystallography ; Thermodynamics ; Chemistry ; Physics ; Nuclear physics Nuclear materials and radiation effects ; Advanced Condensed Matter Physics ; Nuclear Materials and Properties
Authors: Yao Zhang | Fanyong Yan | Xiule Wang | Runze Tong
Source: Particle & Particle Systems Characterization (review, 2023-11-03)
Field: Materials Science — Materials Chemistry

Design and Fabrication of Carbon Dots/Semiconductor Photocatalysts with Superior Photocatalytic Activity: A Review Abstract Semiconductor photocatalysts are being recognized as innovative materials for enhancing water quality and environmental optimization. Nevertheless, the limited activity and quick electron–hole pair recombination under visible light have hindered their widespread use in photocatalysis. Nanomaterials based on carbon dots (CDs) show promising applications due to their excellent light absorption ability. Notably, CDs/semiconductor photocatalysts have emerged as advanced photocatalytic materials. This review summarizes strategies to improve the performance of conventional semiconductor photocatalysts by incorporating CDs. These include TiO 2 , g‐C 3 N 4 , Bi‐based, Cu 2 O, and ZnO photocatalysts. Various effective synthesis routes for composites are discussed, including morphology optimization, heteroatom doping, heterojunction building, and creation of polymeric hybrids. Ultimately, the challenges and future prospects of CDs/semiconductor photocatalysts are discussed. Photocatalysis ; Semiconductor ; Materials science ; Heteroatom ; Nanotechnology ; Heterojunction ; Nanomaterials ; Doping ; Carbon fibers ; Visible spectrum ; Semiconductor materials ; Absorption (acoustics) ; Fabrication ; Optoelectronics ; Composite number ; Chemistry ; Catalysis ; Composite material ; Organic chemistry Carbon and Quantum Dots Applications ; Advanced Photocatalysis Techniques ; Nanocluster Synthesis and Applications
Authors: Ha Do | Soo Young Kim
Source: Symmetry (review, 2023-09-27)
Field: Chemistry — Inorganic Chemistry

Metal-Organic Frameworks Based Multifunctional Materials for Solar Cells: A Review Developing low-cost and stable materials for converting solar energy into electricity is vital in meeting the world’s energy demand. Metal-organic frameworks (MOFs) have gained attention for solar cells due to their natural porous architectures and tunable chemical structures. They are built by high-symmetry metal clusters as secondary building units and organic carboxylate/azolate ligands as linkers. This review commences with an exploration of the synthetic methods of MOFs. Moreover, we discuss the various roles of MOFs, including photoanodes and counter electrodes in dye-sensitized solar cells and interfacial layers and charge carriers in perovskite solar cells. Additionally, studies involving the application of MOFs for OSC were additionally presented. Ultimately, burdensome tasks and possible directions for advancing MOFs-based nanomaterials are provided for solar cells. Metal-organic framework ; Materials science ; Nanotechnology ; Nanomaterials ; Perovskite (structure) ; Solar energy ; Energy transformation ; Charge carrier ; Organic solar cell ; Chemical engineering ; Chemistry ; Optoelectronics ; Polymer ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Perovskite Materials and Applications ; Advanced Nanomaterials in Catalysis
Authors: Sukanta K. Mondal | Peter O. Aina | Ali A. Rownaghi | Fateme Rezaei
Source: ChemPlusChem (article, 2023-12-20)
Field: Materials Science — Materials Chemistry

Cooperative and Bifunctional Adsorbent‐Catalyst Materials for <i>In‐situ</i> VOCs Capture‐Conversion Volatile organic compounds (VOCs) are gases that are emitted into the air from products or processes and are major components of air pollution that significantly deteriorate air quality and seriously affect human health. Different types of metals, metal oxides, mixed-metal oxides, polymers, activated carbons, zeolites, metal-organic frameworks (MOFs) and mixed-matrixed materials have been developed and used as adsorbent or catalyst for diversified VOCs detection, removal, and destruction. In this comprehensive review, we first discuss the general classification of VOCs removal materials and processes and outline the historical development of bifunctional and cooperative adsorbent-catalyst materials for the removal of VOCs from air. Subsequently, particular attention is devoted to design of strategies for cooperative adsorbent-catalyst materials, along with detailed discussions on the latest advances on these bifunctional materials, reaction mechanisms, long-term stability, and regeneration for VOCs removal processes. Finally, challenges and future opportunities for the environmental implementation of these bifunctional materials are identified and outlined with the intent of providing insightful guidance on the design and fabrication of more efficient materials and systems for VOCs removal in the future. Bifunctional ; Catalysis ; Adsorption ; In situ ; Bifunctional catalyst ; Chemical engineering ; Chemistry ; Materials science ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Industrial Gas Emission Control
Authors: Fenfei Wei | Sen Lin | Hua Guo
Source: JACS Au (article, 2023-09-18)
Field: Materials Science — Materials Chemistry

Direct or Precursor-Mediated? Mechanisms for Methane Dissociation on Pt(110)-(2 × 1) at Both Low and High Incidence Energies The activation of alkanes on metal catalysts may involve a precursor-mediated mechanism, in which impinging molecules are first trapped on the catalyst surface to form an adsorbed precursor and may undergo extensive excursion on the surface in search of an active site. A characteristic feature of such a mechanism is an increasing initial sticking probability (<i>S</i><sub>0</sub>) with decreasing incidence energy at low incidence energies. Indeed, such "negative activation" was observed on the reconstructed Pt(110)-(2 × 1) surface with a missing row structure. In this paper, we describe an extensive theoretical investigation of methane dissociation on Pt(110)-(2 × 1) using a machine-learned high-dimensional potential energy surface (PES) based on a first-principles training data set. Quasi-classical trajectories (QCTs) are calculated on the PES to simulate the dissociation of both CH<sub>4</sub> and CHD<sub>3</sub> at various incidence energies. The agreement with the measured initial sticking probabilities is shown to be substantially improved for high incidence energies when compared to previous theoretical studies, indicating a better characterization of the dissociation barrier. Additional QCT calculations have been carried out for the trapping and diffusion of CHD<sub>3</sub> under experimental conditions at low incidence energies. The trapping probability is shown to increase with decreasing incidence energy, consistent with the experimentally observed "negative activation" below 10 kJ/mol. The reactivity of the trapped methane is attributed to the combined effect of its nonthermal diffusion across the surface Pt rows and the lowered barrier reached by surface thermal fluctuation. These simulations shed valuable light on the microscopic dynamics of the initial and often rate-limiting step in heterogeneous catalytic processes involving alkanes. Dissociation (chemistry) ; Sticking probability ; Chemical physics ; Methane ; Activation energy ; Adsorption ; Surface diffusion ; Catalysis ; Chemistry ; Atomic physics ; Trapping ; Diffusion ; Physical chemistry ; Thermodynamics ; Physics ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Advanced Chemical Physics Studies
Authors: Lorenzo Marchi | Simone Fantuzzi | Andrea Cingolani | Alessandro Messori | Rita Mazzoni | Stefano Zacchini | Marina Cocchi | Luca Rigamonti
Source: Dalton Transactions (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A proficient multivariate approach for iron(<scp>ii</scp>) spin crossover behaviour modelling in the solid state Iron(II) <i>bis</i>-pyrazolilpyridyl (bpp-R) complexes [Fe(bpp-R)<sub>2</sub>](X)<sub>2</sub>·solvent, R = substituent and X<sup>-</sup> = anion, can undergo a spin transition from high (<i>S</i> = 2, HS) to low spin (<i>S</i> = 0, LS), being spin crossover (SCO) in the solid state. The distortion of the octahedral coordination environment around the metal centre is governed by crystal packing, <i>i.e.</i> the intermolecular interactions among the substituent R of the bpp-R ligands, the anion X<sup>-</sup>, and the co-crystallized solvent, and this modulates the SCO behaviour. In this work, an innovative multivariate approach, through the combination of the chemometric tools Principal Component Analysis and Partial Least Squares regression, was applied on the coordination bond distances and angles and selected torsional angles of the available HS structures. The obtained results can efficiently model and rationalize the structural data distinguishing between SCO-active and HS-blocked complexes bearing different R groups, X<sup>-</sup> anions, and co-crystallized solvents and help predict the spin transition temperature <i>T</i><sub>1/2</sub>. Substituent ; Spin crossover ; Chemistry ; Octahedron ; Crystallography ; Spin states ; Intermolecular force ; Spin transition ; Coordination number ; Transition metal ; Solvent ; Ion ; Crystal structure ; Stereochemistry ; Inorganic chemistry ; Molecule ; Organic chemistry Magnetism in coordination complexes ; Electron Spin Resonance Studies ; Lanthanide and Transition Metal Complexes
Authors: Boyu Zhang | Jiaping Lin | Lei Du | Liangshun Zhang
Source: Polymers (article, 2023-05-08)
Field: Materials Science — Materials Chemistry

Harnessing Data Augmentation and Normalization Preprocessing to Improve the Performance of Chemical Reaction Predictions of Data-Driven Model As a template-free, data-driven methodology, the molecular transformer model provides an alternative by which to predict the outcome of chemical reactions and design the route of the retrosynthetic plane in the field of organic synthesis and polymer chemistry. However, in consideration of the small datasets of chemical reactions, the data-driven model suffers from the difficulty of low accuracy in the prediction tasks of chemical reactions. In this contribution, we integrate the molecular transformer model with the strategies of data augmentation and normalization preprocessing to accomplish the three tasks of chemical reactions, including the forward predictions of chemical reactions, and single-step retrosynthetic predictions with and without the reaction classes. It is clearly demonstrated that the prediction accuracy of the molecular transformer model can be significantly raised by the use of proposed strategies for the three tasks of chemical reactions. Notably, after the introduction of the 40-level data augmentation and normalization preprocessing, the top-1 accuracy of the forward prediction increases markedly from 71.6% to 84.2% and the top-1 accuracy of the single-step retrosynthetic prediction with additional reaction class increases from 53.2% to 63.4%. Furthermore, it is found that the superior performance of the data-driven model originates from the correction of the grammatical errors of the SMILES strings, especially for the case of the reaction classes with small datasets. Normalization (sociology) ; Retrosynthetic analysis ; Preprocessor ; Computer science ; Data pre-processing ; Transformer ; Artificial intelligence ; Chemical reaction ; Database normalization ; Biological system ; Data mining ; Pattern recognition (psychology) ; Chemistry ; Voltage ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Rongwei Gao | Shu‐Juan Bao
Source: Chemosensors (review, 2023-08-07)
Field: Materials Science — Materials Chemistry

Advances in the Application of Nano-Enzymes in the Electrochemical Detection of Reactive Oxygen Species: A Review Reactive oxygen species (ROS) play an important role in maintaining human health and are recognized as indicators of oxidative stress linked to various conditions such as neurodegenerative and cardiovascular diseases, as well as cancer. Consequently, detecting ROS levels in biological systems is crucial for biomedical and analytical research. Electrochemical approaches offer promising opportunities for ROS determination due to their exceptional sensitivity, speed, and simplicity of equipment. This review covers studies using advanced electrochemical nanozyme sensors for detecting ROS in biological samples that were published over the last ten years, from 2013 to 2023. Emphasis is placed on the sensor materials and different types of modifiers employed for selective ROS detection. Furthermore, a comprehensive analysis of the sensors’ selectivity was performed. Reactive oxygen species ; Human health ; Oxidative stress ; Nanotechnology ; Biochemical engineering ; Chemistry ; Computational biology ; Computer science ; Materials science ; Medicine ; Biology ; Biochemistry ; Engineering ; Environmental health Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Electrochemical Analysis and Applications
Authors: Patchanee Chammingkwan | Mostafa Khoshsefat | Minoru Terano | Toshiaki Taniike
Source: Polymers (article, 2023-12-17)
Field: Chemistry — Organic Chemistry

Parallel Catalyst Synthesis Protocol for Accelerating Heterogeneous Olefin Polymerization Research The data scientific approach has become an indispensable tool for capturing structure-performance relationships in complex systems, where the quantity and quality of data play a crucial role. In heterogeneous olefin polymerization research, the exhaustive and multi-step nature of Ziegler-Natta catalyst synthesis has long posed a bottleneck in synthetic throughput and data generation. In this contribution, a custom-designed 12-parallel reactor system and a catalyst synthesis protocol were developed to achieve the parallel synthesis of a magnesium ethoxide-based Ziegler-Natta catalyst. The established system, featuring a miniature reaction vessel with magnetically suspended stirring, allows for over a tenfold reduction in synthetic scale while ensuring the consistency and reliability of the synthesis. We demonstrate that the established protocol is highly efficient for the generation of a catalyst library with diverse compositions and physical features, holding promise as a foundation for the data-driven establishment of the structure-performance relationship in heterogeneous olefin polymerization catalysis. Polymerization ; Catalysis ; Bottleneck ; Throughput ; Olefin fiber ; Olefin polymerization ; Materials science ; Protocol (science) ; Computer science ; Nanotechnology ; Chemistry ; Organic chemistry ; Embedded system ; Polymer Organometallic Complex Synthesis and Catalysis ; Machine Learning in Materials Science ; Inorganic Chemistry and Materials
Authors: Jeff Guo | Philippe Schwaller
Source: ChemRxiv (preprint, 2023-05-18)
Field: Materials Science — Materials Chemistry

Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy desired objectives under minimal oracle evaluations (computational prediction or wet-lab experiment). This problem becomes more apparent when using oracles that can provide increased predictive accuracy but impose a significant cost. Molecular generative models have shown remarkable sample efficiency when coupled with reinforcement learn- ing, as demonstrated in the Practical Molecular Optimization (PMO) benchmark. Here, we propose a novel algorithm called Augmented Memory that combines data augmentation with experience replay. We show that scores obtained from oracle calls can be reused to update the model multiple times. We compare Augmented Memory to previously proposed algorithms and show significantly enhanced sample efficiency in an exploitation task and a drug discovery case study requiring both exploration and exploitation. Our method achieves a new state-of-the-art in the PMO benchmark which enforces a computational budget, and outperforms the previous best performing method on 19/23 tasks. Benchmark (surveying) ; Oracle ; Computer science ; Task (project management) ; Generative model ; Sample (material) ; Machine learning ; Sample complexity ; Generative grammar ; Reinforcement learning ; Artificial intelligence ; Programming language ; Engineering ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Bulat N. Galimzyanov | Г. А. Никифоров | С. Г. Аникеев | N. V. Artyukhova | А. В. Мокшин
Source: Crystals (article, 2023-11-30)
Field: Materials Science — Materials Chemistry

A Unified Empirical Equation for Determining the Mechanical Properties of Porous NiTi Alloy: From Nanoporosity to Microporosity The mechanical characteristics of a monolithic (non-porous) crystalline or amorphous material are described by a well-defined set of quantities. It is possible to change the mechanical properties by introducing porosity into this material; as a rule, the strength values decrease with the introduction of porosity. Thus, porosity can be considered an additional degree of freedom that can be used to influence the hardness, strength and plasticity of the material. In the present work, using porous crystalline NiTi as an example, it is shown that the mechanical characteristics such as the Young’s modulus, the yield strength, the ultimate tensile strength, etc., demonstrate a pronounced dependence on the average linear size l¯ of the pores. For the first time, an empirical equation is proposed that correctly reproduces the dependence of the mechanical characteristics on the porosity ϕ and on the average linear size l¯ of the pores in a wide range of sizes: from nano-sized pores to pores of a few hundred microns in size. This equation correctly takes into account the limit case corresponding to the monolithic material. The obtained results can be used directly to solve applied problems associated with the design of materials with the necessary combination of physical and mechanical characteristics, in particular, porous metallic biomaterials. Porosity ; Materials science ; Ultimate tensile strength ; Composite material ; Work (physics) ; Amorphous solid ; Alloy ; Nickel titanium ; Modulus ; Elastic modulus ; Bar (unit) ; Thermodynamics ; Shape-memory alloy Shape Memory Alloy Transformations ; High Entropy Alloys Studies ; Cellular and Composite Structures
Authors: Marc A. S. Short | Clare A. Tovee | Charlotte E. Willans | Bao N. Nguyen
Source: Catalysis Science & Technology (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

High-throughput computational workflow for ligand discovery in catalysis with the CSD A novel semi-automated, high-throughput computational workflow for ligand/catalyst discovery based on the Cambridge Structural Database is reported. Workflow ; Throughput ; Computer science ; Ligand (biochemistry) ; Catalysis ; Chemistry ; Database ; Operating system ; Receptor ; Biochemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science