jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vasileios G. Ataloglou | Sajjad Taravati | George V. Eleftheriades
Source: National Science Review (review, 2023-06-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurfaces: physics and applications in wireless communications The ever increasing number of wireless devices and systems has led to a crowded spectrum and increased the demand for versatile and multi-functional wireless apparatuses. Recently, metasurfaces have been explored as a prominent technological solution to the current paradigm of spectrum scarcity by opportunistically sharing the spectrum with various users. In general, metasurfaces are passive/dynamic, ultra-compact, multi-functional and programmable structures that are capable of both reciprocal and nonreciprocal signal-wave transmissions. The controllability and programmability of such metasurfaces are governed through DC bias and occasionally a radio-frequency modulation applied to the active components of the unit cells of the metasurface, e.g. diodes and transistors. This article overviews some of the recently proposed passive and dynamic metasurfaces and shows that metasurfaces can enhance the performance of wireless communication systems thanks to their unique physical features such as real-time signal coding, nonreciprocal-beam radiation, nonreciprocal beamsteering amplification and advanced pattern-coding multiple access communication. Wireless ; Computer science ; Coding (social sciences) ; Electronic engineering ; Telecommunications ; Physics ; Engineering Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Wireless Communication Technologies
Authors: Atul Kumar Maurya | Felipe M. de Souza | Tim Dawsey | Ram K. Gupta
Source: Polymer Composites (article, 2023-12-10)
Field: Materials Science — Biomaterials

Biodegradable polymers and composites: Recent development and challenges Abstract Biodegradable polymers are highly sought after as they have the potential to reduce the issue of plastic waste and eventually lead to a circular economy. Yet, conciliating eco‐friendliness with competitive properties is often challenging. Polymerization and processing techniques have been developed and optimized to improve such factors. Some of the common polymerization methods to obtain such materials are condensation, ring‐opening, addition, and biocatalytic‐assisted polymerization. Alongside that, processing techniques such as melt mixing/extrusion, solution castings, compression, and injection molding have also been employed for the manufacture of biodegradable polymers. While these traditional techniques are viable routes to market, there is still the need to further improve their properties to make them comparable to their petrochemical‐based counterparts. To assist with that, the production of composites and blending techniques are facile approaches that demonstrated promising improvements in their properties. Based on these aspects, this review is aimed at discussing the main types of biodegradable polymers, followed by the compositing approaches reported in the recent literature, their structure–property relationships, and biodegradability mechanisms. The first section provides an introductory overview of biodegradable polymers and their composites. The second section focuses on the most prominent polymers used in the industry. Furthermore, the fabrication techniques, along with their property and application relationships are provided with an emphasis on mechanical reinforcements and the biomedical field. Finally, some future perspectives are given to clarify some of the known challenges and, possibly, provide some insights for the reader to develop novel ideas. Materials science ; Polymer ; Polymerization ; Biodegradation ; Biodegradable polymer ; Extrusion ; Molding (decorative) ; Compositing ; Composite material ; Polymer science ; Biochemical engineering ; Process engineering ; Computer science ; Engineering biodegradable polymer synthesis and properties ; Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies
Authors: Vera Regina Leopoldo Constantino | Mariana Pires Figueiredo | Vagner Roberto Magri | Denise Eulálio | Vanessa R. R. Cunha | Ana C. S. Alcântara | Gustavo Frigi Perotti
Source: Pharmaceutics (review, 2023-01-26)
Field: Materials Science — Materials Chemistry

Biomaterials Based on Organic Polymers and Layered Double Hydroxides Nanocomposites: Drug Delivery and Tissue Engineering The development of biomaterials has a substantial role in pharmaceutical and medical strategies for the enhancement of life quality. This review work focused on versatile biomaterials based on nanocomposites comprising organic polymers and a class of layered inorganic nanoparticles, aiming for drug delivery (oral, transdermal, and ocular delivery) and tissue engineering (skin and bone therapies). Layered double hydroxides (LDHs) are 2D nanomaterials that can intercalate anionic bioactive species between the layers. The layers can hold metal cations that confer intrinsic biological activity to LDHs as well as biocompatibility. The intercalation of bioactive species between the layers allows the formation of drug delivery systems with elevated loading capacity and modified release profiles promoted by ion exchange and/or solubilization. The capacity of tissue integration, antigenicity, and stimulation of collagen formation, among other beneficial characteristics of LDH, have been observed by in vivo assays. The association between the properties of biocompatible polymers and LDH-drug nanohybrids produces multifunctional nanocomposites compatible with living matter. Such nanocomposites are stimuli-responsive, show appropriate mechanical properties, and can be prepared by creative methods that allow a fine-tuning of drug release. They are processed in the end form of films, beads, gels, monoliths etc., to reach orientated therapeutic applications. Several studies attest to the higher performance of polymer/LDH-drug nanocomposite compared to the LDH-drug hybrid or the free drug. Nanocomposite ; Layered double hydroxides ; Drug delivery ; Biocompatibility ; Materials science ; Polymer ; Nanotechnology ; Intercalation (chemistry) ; Nanomaterials ; Tissue engineering ; Transdermal ; Chemical engineering ; Chemistry ; Biomedical engineering ; Organic chemistry ; Composite material ; Pharmacology Layered Double Hydroxides Synthesis and Applications ; biodegradable polymer synthesis and properties ; Bone Tissue Engineering Materials
Authors: Wei Tan | Shaohua Xie | Yandi Cai | Haowei Yu | Kailong Ye | Meiyu Wang | Weijian Diao | Lu Ma | Steven N. Ehrlich | Fei Gao | Lin Dong | Fudong Liu
Source: Environmental Science & Technology (article, 2023-08-11)
Field: Materials Science — Materials Chemistry

Surface Lattice-Embedded Pt Single-Atom Catalyst on Ceria-Zirconia with Superior Catalytic Performance for Propane Oxidation Tuning the metal-support interaction and coordination environment of single-atom catalysts can help achieve satisfactory catalytic performance for targeted reactions. Herein, via the facile control of calcination temperatures for Pt catalysts on pre-stabilized Ce<sub>0.9</sub>Zr<sub>0.1</sub>O<sub>2</sub> (CZO) support, Pt single atoms (Pt<sub>1</sub>) with different strengths of Pt-CeO<sub>2</sub> interaction and coordination environment were successfully constructed. With the increase in calcination temperature from 350 to 750 °C, a stronger Pt-CeO<sub>2</sub> interaction and higher Pt-O-Ce coordination number were achieved due to the reaction between PtO<sub><i>x</i></sub> and surface Ce<sup>3+</sup> species as well as the migration of Pt<sub>1</sub> into the surface lattice of CZO. The Pt/CZO catalyst calcined at 750 °C (Pt/CZO-750) exhibited a surprisingly higher C<sub>3</sub>H<sub>8</sub> oxidation activity than that calcined at 550 °C (Pt/CZO-550). Through systematic characterizations and reaction mechanism study, it was revealed that the higher concentration of surface Ce<sup>3+</sup> species/oxygen vacancies and the stronger Pt-CeO<sub>2</sub> interaction on Pt/CZO-750 could better facilitate the activation of oxygen to oxidize C<sub>3</sub>H<sub>8</sub> into reactive carbonate/carboxyl species and further promote the transformation of these intermediates into gaseous CO<sub>2</sub>. The Pt/CZO-750 catalyst can be a potential candidate for the catalytic removal of hydrocarbons from vehicle exhaust. Catalysis ; Calcination ; Metal ; Propane ; Chemistry ; Oxygen ; Chemical engineering ; Inorganic chemistry ; Materials science ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Fang Zheng | Rundao Chen | Ying Liu | Qiwei Yang | Zhiguo Zhang | Yiwen Yang | Qilong Ren | Zongbi Bao
Source: Advanced Science (article, 2023-01-26)
Field: Chemistry — Inorganic Chemistry

Strengthening Intraframework Interaction within Flexible MOFs Demonstrates Simultaneous Sieving Acetylene from Ethylene and Carbon Dioxide Efficient separation of acetylene (C<sub>2</sub> H<sub>2</sub> )/ethylene (C<sub>2</sub> H<sub>4</sub> ) and acetylene/carbon dioxide (CO<sub>2</sub> ) by adsorption is an industrially promising process, but adsorbents capable of simultaneously capturing trace acetylene from ethylene and carbon dioxide are scarce. Herein, a gate-opening effect on three isomorphous flexible metal-organic frameworks (MOFs) named Co(4-DPDS)<sub>2</sub> MO<sub>4</sub> (M = Cr, Mo, W; 4-DPDS = 4,4-dipyridyldisulfide) is modulated by anion pillars substitution. The shortest CrO<sub>4</sub> <sup>2-</sup> strengthens intraframework hydrogen bonding and thus blocks structural transformation after activation, striking a good balance among working capacity, separation selectivity, and trace impurity removal of flexible MOFs out of nearly C<sub>2</sub> H<sub>2</sub> /C<sub>2</sub> H<sub>4</sub> and C<sub>2</sub> H<sub>2</sub> /CO<sub>2</sub> molecular sieving. The exceptional separation performance of Co(4-DPDS)<sub>2</sub> CrO<sub>4</sub> is confirmed by dynamic breakthrough experiments. It reveals the specific threshold pressures control in anion-pillared flexible materials enabled elimination of the impurity leakage to realize high purity products through precise control of the intraframework interaction. The adsorption mechanism and multimode structural transformation property are revealed by both calculations and crystallography studies. This work demonstrates the feasibility of modulating flexibility for controlling gate-opening effect, especially for some cases of significant aperture shrinkage after activation. Acetylene ; Ethylene ; Materials science ; Adsorption ; Chemical engineering ; Selectivity ; Carbon dioxide ; Ethylene glycol ; Metal-organic framework ; Impurity ; Hydrogen ; Selective adsorption ; Nanotechnology ; Chemistry ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon dioxide utilization in catalysis
Authors: Mengchu Feng | Ziru Niu | Chunyan Xing | Yehao Jin | Xiao Feng | Yuanyuan Zhang | Bo Wang
Source: Angewandte Chemie International Edition (article, 2023-06-13)
Field: Materials Science — Materials Chemistry

Covalent Organic Framework Based Crosslinked Porous Microcapsules for Enzymatic Catalysis The design of porous microcapsules with selective mass transfer and mechanical robustness for enzyme encapsulation is highly desired for biocatalysis, yet the construction remains challenging. Herein, we report the facile fabrication of porous microcapsules by assembling covalent organic framework (COF) spheres at the interfaces of emulsion droplets followed by interparticle crosslinking. The COF microcapsules could offer an enclosed aqueous environment for enzymes, with size-selective porous shells that allow for the fast diffusion of substrates and products while excluding larger molecules such as protease. Crosslinking of COF spheres not only enhances the structural stability of capsules but also imparts enrichment effects. The enzymes encased in the COF microcapsules show enhanced activity and durability in organic media as verified in both batch reaction and continuous-flow reaction. The COF microcapsules offer a promising platform for the encapsulation of biomacromolecules. Covalent bond ; Porosity ; Enzyme catalysis ; Catalysis ; Chemistry ; Covalent organic framework ; Immobilized enzyme ; Chemical engineering ; Materials science ; Polymer chemistry ; Nanotechnology ; Organic chemistry ; Enzyme ; Engineering Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Nung Siong Lai | Yi Shen Tew | Xialin Zhong | Jun Yin | Jiali Li | Binhang Yan | Xiaonan Wang
Source: Industrial & Engineering Chemistry Research (article, 2023-10-12)
Field: Materials Science — Materials Chemistry

Artificial Intelligence (AI) Workflow for Catalyst Design and Optimization In the pursuit of novel catalyst development to address pressing environmental concerns and energy demand, conventional design and optimization methods often fall short due to the complexity and vastness of the catalyst parameter space. The advent of Machine Learning (ML) has ushered in a new era in the field of catalyst optimization, offering potential solutions to the shortcomings of traditional techniques. However, existing methods fail to effectively harness the vast information contained within the expanding body of scientific literature on catalyst synthesis. To address this gap, this study proposes an innovative Artificial Intelligence (AI) workflow that integrates large-language models (LLMs), Bayesian optimization, and an active learning loop to expedite and enhance catalyst optimization. Our methodology combines advanced language understanding with robust optimization strategies, effectively translating knowledge extracted from the diverse literature into actionable parameters for practical experimentation and optimization. In this article, we demonstrate the application of this AI workflow in the optimization of catalyst synthesis for ammonia production. The results underscore the workflow’s ability to streamline the catalyst development process, offering a swift, resource-efficient, and high-precision alternative to conventional methods. Workflow ; Bayesian optimization ; Computer science ; Workflow management system ; Artificial intelligence ; Process optimization ; Field (mathematics) ; Machine learning ; Biochemical engineering ; Engineering ; Database Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Catalytic Processes in Materials Science
Authors: Sheng Gong | Keqiang Yan | Tian Xie | Yang Shao‐Horn | Rafael Gómez‐Bombarelli | Shuiwang Ji | Jeffrey C. Grossman
Source: Science Advances (article, 2023-11-10)
Field: Materials Science — Materials Chemistry

Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity Graph neural networks (GNNs) have recently been used to learn the representations of crystal structures through an end-to-end data-driven approach. However, a systematic top-down approach to evaluate and understand the limitations of GNNs in accurately capturing crystal structures has yet to be established. In this study, we introduce an approach using human-designed descriptors as a compendium of human knowledge to investigate the extent to which GNNs can comprehend crystal structures. Our findings reveal that current state-of-the-art GNNs fall short in accurately capturing the periodicity of crystal structures. We analyze this failure by exploring three aspects: local expressive power, long-range information processing, and readout function. To address these identified limitations, we propose a straightforward and general solution: the hybridization of descriptors with GNNs, which directly supplements the missing information to GNNs. The hybridization enhances the predictive accuracy of GNNs for specific material properties, most notably phonon internal energy and heat capacity, which heavily rely on the periodicity of materials. Compendium ; Computer science ; Graph ; Expressive power ; Artificial intelligence ; Artificial neural network ; Machine learning ; Theoretical computer science ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Xiang Huang | Shengluo Ma | Changying Zhao | Hong Wang | Shenghong Ju
Source: npj Computational Materials (article, 2023-10-14)
Field: Materials Science — Materials Chemistry

Exploring high thermal conductivity polymers via interpretable machine learning with physical descriptors Abstract The efficient and economical exploitation of polymers with high thermal conductivity (TC) is essential to solve the issue of heat dissipation in organic devices. Currently, the experimental preparation of functional polymers with high TC remains a trial-and-error process due to the multi-degrees of freedom during the synthesis and characterization process. Polymer informatics equips machine learning (ML) as a powerful engine for the efficient design of polymers with desired properties. However, available polymer TC databases are rare, and establishing appropriate polymer representation is still challenging. In this work, we propose a high-throughput screening framework for polymer chains with high TC via interpretable ML and physical feature engineering. The hierarchical down-selection process stepwise optimizes the 320 initial physical descriptors to the final 20 dimensions and then assists the ML models to achieve a prediction accuracy R 2 over 0.80, which is superior to traditional graph descriptors. Further, we analyze the contribution of the individual descriptors to TC and derive the explicit equation for TC prediction using symbolic regression. The high TC polymer structures are mostly π-conjugated, whose overlapping p-orbitals enable easy maintenance of strong chain stiffness and large group velocities. Ultimately, we establish the connections between the individual chains and the amorphous state of polymers. Polymer chains with high TC have strong intra-chain interactions, and their corresponding amorphous systems are favorable for obtaining a large radius of gyration and causing enhanced thermal transport. The proposed data-driven framework should facilitate the theoretical and experimental design of polymers with desirable properties. Polymer ; Computer science ; Materials science ; Representation (politics) ; Artificial intelligence ; Biological system ; Machine learning ; Statistical physics ; Physics ; Composite material Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Thermal properties of materials
Authors: Nobuya Tsuji | Pavel Sidorov | Chendan Zhu | Yuuya Nagata | Timur Gimadiev | Alexandre Varnek | Benjamin List
Source: Angewandte Chemie International Edition (article, 2023-01-23)
Field: Materials Science — Materials Chemistry

Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors** Catalyst optimization processes typically rely on inductive and qualitative assumptions of chemists based on screening data. While machine learning models using molecular properties or calculated 3D structures enable quantitative data evaluation, costly quantum chemical calculations are often required. In contrast, readily available binary fingerprint descriptors are time- and cost-efficient, but their predictive performance remains insufficient. Here, we describe a machine learning model based on fragment descriptors, which are fine-tuned for asymmetric catalysis and represent cyclic or polyaromatic hydrocarbons, enabling robust and efficient virtual screening. Using training data with only moderate selectivities, we designed theoretically and validated experimentally new catalysts showing higher selectivities in a challenging asymmetric tetrahydropyran synthesis. Enantioselective synthesis ; Tetrahydropyran ; Catalysis ; Computer science ; Binary number ; Quantum chemical ; Fragment (logic) ; Fingerprint (computing) ; Virtual screening ; Molecular descriptor ; Artificial intelligence ; Chemistry ; Machine learning ; Combinatorial chemistry ; Computational chemistry ; Quantitative structure–activity relationship ; Algorithm ; Molecular dynamics ; Organic chemistry ; Molecule ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: Eike Caldeweyher | Masha Elkin | Golsa Gheibi | Magnus J. Johansson | Christian Sköld | Per‐Ola Norrby | John F. Hartwig
Source: Journal of the American Chemical Society (article, 2023-07-31)
Field: Chemistry — Inorganic Chemistry

Hybrid Machine Learning Approach to Predict the Site Selectivity of Iridium-Catalyzed Arene Borylation The borylation of aryl and heteroaryl C-H bonds is valuable for the site-selective functionalization of C-H bonds in complex molecules. Iridium catalysts ligated by bipyridine ligands catalyze the borylation of the C-H bond that is most acidic and least sterically hindered in an arene, but predicting the site of borylation in molecules containing multiple arenes is difficult. To address this challenge, we report a hybrid computational model that predicts the Site of Borylation (SoBo) in complex molecules. The SoBo model combines density functional theory, semiempirical quantum mechanics, cheminformatics, linear regression, and machine learning to predict site selectivity and to extrapolate these predictions to new chemical space. Experimental validation of SoBo showed that the model predicts the major site of borylation of pharmaceutical intermediates with higher accuracy than prior machine-learning models or human experts, demonstrating that SoBo will be useful to guide experiments for the borylation of specific C(sp<sup>2</sup>)-H bonds during pharmaceutical development. Borylation ; Chemistry ; Steric effects ; Iridium ; Molecule ; Cheminformatics ; Combinatorial chemistry ; Density functional theory ; Catalysis ; Stereochemistry ; Computational chemistry ; Aryl ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Catalytic C–H Functionalization Methods ; Chemical Synthesis and Analysis
Authors: F. E. Abeng | Valentine Chikaodili Anadebe
Source: Computational and Theoretical Chemistry (article, 2023-09-17)
Field: Materials Science — Materials Chemistry

Combined electrochemical, DFT/MD-simulation and hybrid machine learning based on ANN-ANFIS models for prediction of doxorubicin drug as corrosion inhibitor for mild steel in 0.5 M H2SO4 solution Chemistry ; Electrochemistry ; Corrosion inhibitor ; Combinatorial chemistry ; Corrosion ; Biological system ; Chemical engineering ; Computational chemistry ; Physical chemistry ; Organic chemistry ; Engineering Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Shatakshi Saxena | Michael E. Johnson | Fuhar Dixit | Karl Zimmermann | Shreya Chaudhuri | Fiyanshu Kaka | Balasubramanian Kandasubramanian
Source: Renewable and Sustainable Energy Reviews (article, 2023-03-10)
Field: Materials Science — Materials Chemistry

Thinking green with 2-D and 3-D MXenes: Environment friendly synthesis and industrial scale applications and global impact MXenes ; Environmentally friendly ; Nanotechnology ; Biochemical engineering ; Computer science ; Materials science ; Engineering ; Ecology MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; 2D Materials and Applications