jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaoxiao Duan | Ting Zhao | Ben Niu | Zheng Wei | Ganggang Li | Zhongshen Zhang | Jie Cheng | Zhengping Hao
Source: Advanced Science (article, 2022-11-27)
Field: Materials Science — Materials Chemistry

Simultaneously Constructing Active Sites and Regulating Mn–O Strength of Ru‐Substituted Perovskite for Efficient Oxidation and Hydrolysis Oxidation of Chlorobenzene Chlorinated volatile organic compounds (CVOCs) are a class of hazardous pollutants that severely threaten environmental safety and human health. Although the catalytic oxidation technique for CVOCs elimination is effective, enhancing the catalytic efficiency and simultaneously inhibiting the production of organic byproducts is still of great challenge. Herein, Ru-substituted LaMn(Ru)O<sub>3+</sub> <sub>δ</sub> perovskite with Ru-O-Mn structure and weakened Mn-O bond strength has been developed for catalytic oxidation of chlorobenzene (CB). The formed Ru-O-Mn structure serves as favorable sites for CB adsorption and activation, while the weakening of Mn-O bond strength facilitates the formation of active oxygen species and improves oxygen mobility and catalyst reducibility. Therefore, LaMn(Ru)O<sub>3+</sub> <sub>δ</sub> exhibits superior low-temperature activity with the temperature of 90% CB conversion decreasing by over 90 °C compared with pristine perovskite, and the deep oxidation of chlorinated byproducts produced in low temperature is also accelerated. Furthermore, the introduction of water vapor into reaction system triggers the process of hydrolysis oxidation that promotes CB destruction and inhibits the generation of chlorinated byproducts, due to the higher-activity *OOH species generated from the dissociated H<sub>2</sub> O reacting with adsorbed oxygen. This work can provide a unique, high-efficiency, and facile strategy for CVOCs degradation and environmental improvement. Chlorobenzene ; Catalysis ; Chemistry ; Hydrolysis ; Adsorption ; Oxygen ; Perovskite (structure) ; Degradation (telecommunications) ; Inorganic chemistry ; Catalytic oxidation ; Redox ; Chemical engineering ; Organic chemistry Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques ; Nanomaterials for catalytic reactions
Authors: Xi–Chao Wang | Bo Li | Cheng‐Wei Ju | Dongbing Zhao
Source: Nature Communications (article, 2022-06-13)
Field: Chemistry — Organic Chemistry

Nickel(0)-catalyzed divergent reactions of silacyclobutanes with internal alkynes Transition metal-catalyzed reactions of silacyclobutanes with a variety of π units have attracted much attention and become one of the most straightforward and efficient approaches to rapidly access structurally diverse organosilicon compounds. However, the reaction of silacyclobutanes with alkynes still suffers from some limitations: (1) internal alkynes remain challenging substrates; (2) expensive Pd- or Rh-based catalysts have been employed in all existing systems; (3) controlling chemodivergence has not yet been realized. Herein we realize Ni-catalyzed chemodivergent reactions of silacyclobutanes with alkynes. In comparison with the previous Pd or Rh catalytic systems, our Ni-catalytic system features: 1) complementary substrate scope; 2) ligand-controlled chemodivergence; 3) low cost. The ligand precisely dictates the pathway selectivity, leading to the divergent formation of (benzo)silacyclohexenes and allyl vinylsilanes. Moreover, we demonstrate that employment of a chiral phosphine ligand is capable of forming silicon-stereogenic allyl vinylsilanes in high yields and enantioselectivities. In addition, DFT calculation is performed to elucidate the origin of the switchable selectivities, which is mainly attributed to different ligand steric effects. Ligand (biochemistry) ; Catalysis ; Steric effects ; Chemistry ; Substrate (aquarium) ; Stereocenter ; Combinatorial chemistry ; Phosphine ; Organosilicon ; Nickel ; Organic chemistry ; Enantioselective synthesis Organoboron and organosilicon chemistry ; Catalytic Cross-Coupling Reactions ; Cyclopropane Reaction Mechanisms
Authors: Wang Sun | Xianhui Zhao | Erin Webb | Gang Xu | Weijie Zhang | Yang Wang
Source: Journal of Materials Chemistry A (article, 2022-12-20)
Field: Chemistry — Inorganic Chemistry

Advances in metal–organic framework-based hydrogel materials: preparation, properties and applications This review provides the engineering strategies, systematic classification and representative applications of MOF-based hydrogels, and future prospects have also been presented in detail. Self-healing hydrogels ; Nanotechnology ; Metal-organic framework ; Computer science ; Materials science ; Systems engineering ; Engineering ; Chemistry ; Polymer chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Advanced Nanomaterials in Catalysis
Authors: Rachel Miller | Ben T. Jordan | Madison Mehlferber | Erin D. Jeffery | Christina Chatzipantsiou | Simi Kaur | Robert J. Millikin | Yunxiang Dai | Simone Tiberi | Peter J. Castaldi | Michael R. Shortreed | Chance John Luckey | Ana Conesa | Lloyd M. Smith | Anne Deslattes Mays | Gloria Sheynkman
Source: Genome biology (article, 2022-03-03)
Field: Chemistry — Spectroscopy

Enhanced protein isoform characterization through long-read proteogenomics Abstract Background The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g., PacBio or Oxford Nanopore) provides full-length transcripts which can be used to predict full-length protein isoforms. Results We describe here a long-read proteogenomics approach for integrating sample-matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data to enable detection of protein isoforms previously intractable to MS-based detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis. Conclusions Our work suggests that the incorporation of long-read sequencing and proteomic data can facilitate improved characterization of human protein isoform diversity. Our first-generation pipeline provides a strong foundation for future development of long-read proteogenomics and its adoption for both basic and translational research. Proteogenomics ; Biology ; Computational biology ; Gene isoform ; Human genetics ; Evolutionary biology ; Proteomics ; Genetics ; Genome Biology ; Genomics ; Bioinformatics ; Genome ; Gene Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; RNA and protein synthesis mechanisms
Authors: Geetanjali Deokar | Junjie Jin | Udo Schwingenschlögl | Pedro M. F. J. Costa
Source: npj 2D Materials and Applications (article, 2022-03-04)
Field: Materials Science — Materials Chemistry

Chemical vapor deposition-grown nitrogen-doped graphene’s synthesis, characterization and applications Abstract The physical properties of carbon materials can be altered by doping. For instance, the electronic properties of graphene can be modulated by controlling the substitutional doping of the carbon lattice with N. In addition, C–N bonding configurations with three ring types are recognized: pyridinic-N, pyrrolic-N, and graphitic-N. Controlling the type and relative density of various types of substitutional N is an important objective that requires an extremely high level of precision when the atomic lattice is constructed. This control can be accomplished only via bottom-up methods, such as chemical vapor deposition (CVD). The number of reports on N-doped graphene (NDG) grown via CVD has increased over the past decade, but a reliable wafer-scale production strategy that can realize the desired atomic-precision growth of NDG is still lacking. To identify the most promising strategies and analyze the consistency of the results published in the literature, we review the CVD growth and characterization of two-dimensional NDG and two of the most popular applications of NDG films: field-effect transistors and energy storage devices. Graphene ; Chemical vapor deposition ; Doping ; Characterization (materials science) ; Materials science ; Carbon fibers ; Nanotechnology ; Lattice (music) ; Deposition (geology) ; Nitrogen ; Chemical engineering ; Chemistry ; Optoelectronics ; Organic chemistry ; Composite number ; Physics Graphene research and applications ; Advancements in Battery Materials ; 2D Materials and Applications
Authors: Zhuoran Qiao | Anders S. Christensen | Matthew Welborn | Frederick R. Manby | Anima Anandkumar | Thomas F. Miller
Source: Proceedings of the National Academy of Sciences (article, 2022-07-28)
Field: Materials Science — Materials Chemistry

Informing geometric deep learning with electronic interactions to accelerate quantum chemistry Predicting electronic energies, densities, and related chemical properties can facilitate the discovery of novel catalysts, medicines, and battery materials. However, existing machine learning techniques are challenged by the scarcity of training data when exploring unknown chemical spaces. We overcome this barrier by systematically incorporating knowledge of molecular electronic structure into deep learning. By developing a physics-inspired equivariant neural network, we introduce a method to learn molecular representations based on the electronic interactions among atomic orbitals. Our method, OrbNet-Equi, leverages efficient tight-binding simulations and learned mappings to recover high-fidelity physical quantities. OrbNet-Equi accurately models a wide spectrum of target properties while being several orders of magnitude faster than density functional theory. Despite only using training samples collected from readily available small-molecule libraries, OrbNet-Equi outperforms traditional semiempirical and machine learning-based methods on comprehensive downstream benchmarks that encompass diverse main-group chemical processes. Our method also describes interactions in challenging charge-transfer complexes and open-shell systems. We anticipate that the strategy presented here will help to expand opportunities for studies in chemistry and materials science, where the acquisition of experimental or reference training data is costly. Quantum chemistry ; Quantum ; Computer science ; Nanotechnology ; Data science ; Chemistry ; Cognitive science ; Human–computer interaction ; Physics ; Psychology ; Materials science ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Yingzhe Zheng | Xiaonan Wang | Zhe Wu
Source: Industrial & Engineering Chemistry Research (article, 2022-04-13)
Field: Materials Science — Materials Chemistry

Machine Learning Modeling and Predictive Control of the Batch Crystallization Process This work develops a framework for building machine learning models and machine-learning-based predictive control schemes for batch crystallization processes. We consider a seeded fesoterodine fumarate cooling crystallization and dissolution process in a batch reactor and present the methodology and implementation of simulation, modeling, and controller design. Specifically, to address the experimental data scarcity problem, we first develop a one-dimensional population balance model based on published kinetic parameters that were obtained empirically to describe the formation of crystals via nucleation, growth, and agglomeration. Then, recurrent neural network (RNN) and autoencoder–RNN (AERNN) models are developed using data from extensive open-loop simulations of the semi-empirical population balance model under various operating conditions to capture the process dynamic behavior. Two model predictive control (MPC) schemes using the respective RNN and AERNN models are developed to optimize the crystallization process with respect to product yield, crystal size, number of fines in the final product, and energy consumption, while accounting for the constraints on manipulated inputs. Through open- and closed-loop simulations, it is demonstrated that the RNN and AERNN models capture the process dynamics well, and the RNN- and AERNN-based MPCs achieved the desired product yield and crystal size with significantly improved computational efficiency. Model predictive control ; Computer science ; Population ; Autoencoder ; Crystallization ; Machine learning ; Artificial intelligence ; Artificial neural network ; Engineering ; Control (management) Crystallization and Solubility Studies ; Advanced Control Systems Optimization ; Process Optimization and Integration
Authors: Shufeng Kong | Francesco Ricci | Dan Guevarra | Jeffrey B. Neaton | Carla P. Gomes | John M. Gregoire
Source: Nature Communications (article, 2022-02-17)
Field: Materials Science — Materials Chemistry

Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings Machine learning for materials discovery has largely focused on predicting an individual scalar rather than multiple related properties, where spectral properties are an important example. Fundamental spectral properties include the phonon density of states (phDOS) and the electronic density of states (eDOS), which individually or collectively are the origins of a breadth of materials observables and functions. Building upon the success of graph attention networks for encoding crystalline materials, we introduce a probabilistic embedding generator specifically tailored to the prediction of spectral properties. Coupled with supervised contrastive learning, our materials-to-spectrum (Mat2Spec) model outperforms state-of-the-art methods for predicting ab initio phDOS and eDOS for crystalline materials. We demonstrate Mat2Spec's ability to identify eDOS gaps below the Fermi energy, validating predictions with ab initio calculations and thereby discovering candidate thermoelectrics and transparent conductors. Mat2Spec is an exemplar framework for predicting spectral properties of materials via strategically incorporated machine learning techniques. Probabilistic logic ; Computer science ; Observable ; Scalar (mathematics) ; Ab initio ; Embedding ; Density of states ; Statistical physics ; Artificial intelligence ; Physics ; Mathematics ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; X-ray Diffraction in Crystallography
Authors: Guoping Xu | Zeting Yu | Lei Xia | Changjiang Wang | Shaobo Ji
Source: Energy Conversion and Management (article, 2022-07-31)
Field: Materials Science — Materials Chemistry

Performance improvement of solid oxide fuel cells by combining three-dimensional CFD modeling, artificial neural network and genetic algorithm Computational fluid dynamics ; Artificial neural network ; Genetic algorithm ; Fuel cells ; Solid oxide fuel cell ; Computer science ; Mechanical engineering ; Materials science ; Engineering ; Chemical engineering ; Environmental science ; Artificial intelligence ; Aerospace engineering ; Machine learning ; Chemistry Advancements in Solid Oxide Fuel Cells ; Machine Learning and ELM ; Fuel Cells and Related Materials
Authors: Jiaqi Zhu | Xavier Salvatella | Paul Robustelli
Source: Nature Communications (article, 2022-10-27)
Field: Chemistry — Spectroscopy

Small molecules targeting the disordered transactivation domain of the androgen receptor induce the formation of collapsed helical states Intrinsically disordered proteins, which do not adopt well-defined structures under physiological conditions, are implicated in many human diseases. Small molecules that target the disordered transactivation domain of the androgen receptor have entered human trials for the treatment of castration-resistant prostate cancer (CRPC), but no structural or mechanistic rationale exists to explain their inhibition mechanisms or relative potencies. Here, we utilize all-atom molecular dynamics computer simulations to elucidate atomically detailed binding mechanisms of the compounds EPI-002 and EPI-7170 to the androgen receptor. Our simulations reveal that both compounds bind at the interface of two transiently helical regions and induce the formation of partially folded collapsed helical states. We find that EPI-7170 binds androgen receptor more tightly than EPI-002 and we identify a network of intermolecular interactions that drives higher affinity binding. Our results suggest strategies for developing more potent androgen receptor inhibitors and general strategies for disordered protein drug design. Transactivation ; Androgen receptor ; Small molecule ; Intrinsically disordered proteins ; Androgen ; Prostate cancer ; Chemistry ; Biophysics ; Receptor ; Biology ; Cell biology ; Biochemistry ; Cancer ; Gene ; Genetics ; Transcription factor ; Hormone Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Rachel Yie Hang Tan | Choy Sin Lee | Mallikarjuna Rao Pichika | Sit Foon Cheng | Ki Yan Lam
Source: Polymers (review, 2022-04-20)
Field: Materials Science — Polymers and Plastics

PH Responsive Polyurethane for the Advancement of Biomedical and Drug Delivery Due to the specific physiological pH throughout the human body, pH-responsive polymers have been considered for aiding drug delivery systems. Depending on the surrounding pH conditions, the polymers can undergo swelling or contraction behaviors, and a degradation mechanism can release incorporated substances. Additionally, polyurethane, a highly versatile polymer, has been reported for its biocompatibility properties, in which it demonstrates good biological response and sustainability in biomedical applications. In this review, we focus on summarizing the applications of pH-responsive polyurethane in the biomedical and drug delivery fields in recent years. In recent studies, there have been great developments in pH-responsive polyurethanes used as controlled drug delivery systems for oral administration, intravaginal administration, and targeted drug delivery systems for chemotherapy treatment. Other applications such as surface biomaterials, sensors, and optical imaging probes are also discussed in this review. Polyurethane ; Drug delivery ; Biocompatibility ; Nanotechnology ; Polymer ; Drug ; Materials science ; Drug administration ; Swelling ; Biomedical engineering ; Pharmacology ; Medicine ; Composite material Polymer composites and self-healing ; Electrospun Nanofibers in Biomedical Applications ; Advanced Polymer Synthesis and Characterization
Authors: Hamza Abu Owida | Jamal Al-Nabulsi | Feras Alnaimat | Ashraf Al Sharah | Muhammad Al-Ayyad | Nidal Turab | Mustafa Abdullah
Source: Advances in Pharmacological and Pharmaceutical Sciences (review, 2022-04-19)
Field: Materials Science — Biomaterials

Advancement of Nanofibrous Mats and Common Useful Drug Delivery Applications Electrospinning enables simple and cost-effective production of polymer nanofibers from different polymer materials. Drug delivery systems are capable of achieving maximum drug treatment benefits by significantly reducing adverse complications. Electrospun nanofibers have recently attracted considerable attention owing to their distinctive properties, including flexibility and biocompatibility. The implementation of functional constituents within nanostructure fibers blends is an effective technique for the administration of a variety of drugs in animal research, broadening the nanofiber capability and reliability. The nanofibrous mesh and its various application purposes are discussed in terms of a summary of recent research, emphasizing the ease of streaming and a large number of combinations of this approach, which could lead to a breakthrough in targeted therapy. Nanofiber ; Electrospinning ; Biocompatibility ; Drug delivery ; Nanotechnology ; Materials science ; Flexibility (engineering) ; Polymer ; Composite material Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Electrohydrodynamics and Fluid Dynamics
Authors: Monica Sikka | Alok Sarkar | Samridhi Garg
Source: Research Journal of Textile and Apparel (article, 2022-04-11)
Field: Materials Science — Polymers and Plastics

Artificial intelligence (AI) in textile industry operational modernization Purpose With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing. Design/methodology/approach The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration. Findings AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances. Originality/value This research conducts a thorough analysis of artificial neural network applications in the textile sector. Automation ; Artificial neural network ; Artificial intelligence ; Engineering ; Yarn ; Textile industry ; Process (computing) ; Manufacturing engineering ; Backpropagation ; Quality (philosophy) ; Textile ; Computer science ; Machine learning ; Industrial engineering ; Mechanical engineering Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Dyeing and Modifying Textile Fibers