jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Juthaporn Ponphaiboon | Wantanwa Krongrawa | Wah Wah Aung | Nawinda Chinatangkul | Sontaya Limmatvapirat | Chutima Limmatvapirat
Source: Molecules (review, 2023-07-02)
Field: Materials Science — Biomaterials

Advances in Natural Product Extraction Techniques, Electrospun Fiber Fabrication, and the Integration of Experimental Design: A Comprehensive Review The present review explores the growing interest in the techniques employed for extracting natural products. It emphasizes the limitations of conventional extraction methods and introduces superior non-conventional alternatives, particularly ultrasound-assisted extraction. Characterization and quantification of bioactive constituents through chromatography coupled with spectroscopy are recommended, while the importance of method development and validation for biomarker quantification is underscored. At present, electrospun fibers provide a versatile platform for incorporating bioactive extracts and have extensive potential in diverse fields due to their unique structural and functional characteristics. Thus, the review also highlights the fabrication of electrospun fibers containing bioactive extracts. The preparation of biologically active extracts under optimal conditions, including the selection of safe solvents and cost-effective equipment, holds promising potential in the pharmaceutical, food, and cosmetic industries. Integration of experimental design into extraction procedures and formulation development is essential for the efficient production of health products. The review explores potential applications of encapsulating natural product extracts in electrospun fibers, such as wound healing, antibacterial activity, and antioxidant properties, while acknowledging the need for further exploration and optimization in this field. The findings discussed in this review are anticipated to serve as a valuable resource for the processing industry, enabling the utilization of affordable and environmentally friendly, natural, and raw materials. Biochemical engineering ; Environmentally friendly ; Computer science ; Extraction (chemistry) ; Nanotechnology ; Raw material ; Natural product ; Process engineering ; Materials science ; Chemistry ; Engineering ; Chromatography Electrospun Nanofibers in Biomedical Applications ; Silk-based biomaterials and applications ; Coconut Research and Applications
Authors: Yueqiang Hu | Yuting Jiang | Yi Zhang | Xing Yang | Xiangnian Ou | Ling Li | Xianghong Kong | Xingsi Liu | Cheng‐Wei Qiu | Huigao Duan
Source: Nature Communications (article, 2023-10-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Asymptotic dispersion engineering for ultra-broadband meta-optics Dispersion decomposes compound light into its monochromatic components, which is detrimental to broadband imaging but advantageous for spectroscopic applications. Metasurfaces provide a unique path to modulate the dispersion by adjusting structural parameters on a two-dimensional plane. However, conventional linear phase compensation does not adequately match the meta-unit's dispersion characteristics with required complex dispersion, hindering at-will dispersion engineering over a very wide bandwidth particularly. Here, we propose an asymptotic phase compensation strategy for ultra-broadband dispersion-controlled metalenses. Metasurfaces with extraordinarily high aspect ratio nanostructures have been fabricated for arbitrary dispersion control in ultra-broad bandwidth, and we experimentally demonstrate the single-layer achromatic metalenses in the visible to infrared spectrum (400 nm~1000 nm, NA = 0.164). Our proposed scheme provides a comprehensive theoretical framework for single-layer meta-optics, allowing for arbitrary dispersion manipulation without bandwidth restrictions. This development is expected to have significant applications in ultra-broadband imaging and chromatography detection, among others. Broadband ; Dispersion (optics) ; Computer science ; Optics ; Physics Metamaterials and Metasurfaces Applications ; Photonic and Optical Devices ; Orbital Angular Momentum in Optics
Authors: E.E. Abdel‐Hady | Hamdy F. M. Mohamed | Sarah H.M. Hafez | Abdalla M. M. Fahmy | Abdelhamed Magdy | Aya S. Mohamed | Eman O. Ali | Hager R. Abdelhamed | Osama M. Mahmoud
Source: Scientific Reports (article, 2023-04-20)
Field: Materials Science — Materials Chemistry

Textural properties and adsorption behavior of Zn–Mg–Al layered double hydroxide upon crystal violet dye removal as a low cost, effective, and recyclable adsorbent The preparation of adsorbents plays a vital role in the adsorption method. In particular, many adsorbents with high specific surface areas and unique shapes are essential for the adsorption strategy. A Zn-Mg-Al/layer double hydroxide (LDH) was designed in this study using a simple co-precipitation process. Adsorbent based on Zn-Mg-Al/LDH was used to remove crystal violet (CV) from the wastewater. The impacts of the initial dye concentration, pH, and temperature on CV adsorption performance were systematically examined. The adsorbents were analyzed both before and after adsorption using FTIR, XRD, and SEM. The roughness parameters and surface morphologies of the produced LDH were estimated using 3D SEM images. Under the best conditions (dose of adsorbent = 0.07 g and pH = 9), the maximum adsorption capacity has been achieved. Adsorption kinetics studies revealed that the reaction that led to the adsorption of CV dye onto Zn-Mg-Al/LDH was a pseudo-second-order model. Additionally, intraparticle diffusion suggests that Zn-Mg-Al/LDH has a fast diffusion constant for CV molecules (0.251 mg/(g min<sup>1/2</sup>)). Furthermore, as predicted by the Langmuir model, the maximal Zn-Mg-Al/LDH adsorption capacity of CV was 64.80 mg/g. The CV dimensionless separation factor (R<sub>L</sub>) onto Zn-Mg-Al/LDH was 0.769, indicating that adsorption was favorable. The effect of temperature was performed at 25, 35, and 45 °C in order to establish the thermodynamic parameters ∆H<sup>o</sup>, ∆S<sup>o</sup>, and ∆G<sup>o</sup>. The computed values indicated exothermic and spontaneous adsorption processes. The study presented here might be used to develop new adsorbents with enhanced adsorption capabilities for the purpose of protecting the water environment. Crystal violet ; Adsorption ; Hydroxide ; Crystal (programming language) ; Chemical engineering ; Chemistry ; Materials science ; Inorganic chemistry ; Organic chemistry ; Computer science ; Microbiology Layered Double Hydroxides Synthesis and Applications ; Magnesium Oxide Properties and Applications ; Adsorption and biosorption for pollutant removal
Authors: Mingyu Chu | Xianpeng Wang | Xuchun Wang | Xiangxi Lou | Congyang Zhang | Muhan Cao | Lu Wang | Youyong Li | Sibao Liu | Tsun‐Kong Sham | Qiao Zhang | Jinxing Chen
Source: Research (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Site-Selective Polyolefin Hydrogenolysis on Atomic Ru for Methanation Suppression and Liquid Fuel Production Catalytic hydrogenolysis of end-of-life polyolefins can produce value-added liquid fuels and therefore holds great promises in plastic waste reuse and environmental remediation. The major challenge limiting the recycling economic benefit is the severe methanation (usually >20%) induced by terminal C-C cleavage and fragmentation in polyolefin chains. Here, we overcome this challenge by demonstrating that Ru single-atom catalyst can effectively suppress methanation by inhibiting terminal C-C cleavage and preventing chain fragmentation that typically occurs on multi-Ru sites. The Ru single-atom catalyst supported on CeO<sub>2</sub> shows an ultralow CH<sub>4</sub> yield of 2.2% and a liquid fuel yield of over 94.5% with a production rate of 314.93 g<sub>fuels</sub> g<sub>Ru</sub> <sup>-1</sup> h<sup>-1</sup> at 250 °C for 6 h. Such remarkable catalytic activity and selectivity of Ru single-atom catalyst in polyolefin hydrogenolysis offer immense opportunities for plastic upcycling. Hydrogenolysis ; Polyolefin ; Catalysis ; Methanation ; Yield (engineering) ; Chemistry ; Materials science ; Organic chemistry ; Metallurgy Nanomaterials for catalytic reactions ; Carbon dioxide utilization in catalysis ; Ammonia Synthesis and Nitrogen Reduction
Authors: Zhe Fang | Baiwei Ma | Erjun Liang | Yu Jia | Shaokang Guan
Source: Coatings (article, 2023-12-25)
Field: Materials Science — Biomaterials

Interaction Regularity of Biomolecules on Mg and Mg-Based Alloy Surfaces: A First-Principles Study Adsorbed molecules can modulate the behavior of magnesium (Mg) and Mg alloy in biomedical applications. The interaction regularity and mechanism of biomolecules (such as amino acids, dipeptides, and tripeptide) on a Mg(0001) surface, the influence of dipole correction, and the effects of alloying elements and electronic structure were investigated in this study using first-principles calculations. Specifically, the adsorption energy (Eads) of functional groups (-NH2, -COOH and -CN3H4), amino acids (arginine (Arg), glycine (Gly), and aspartic acid (Asp)), dipeptides (arginine–glycine (Arg-Gly), glycine–aspartic acid (Gly-Asp), and arginine–aspartic acid (Arg-Asp)), and arginine–glycine–aspartic acid (RGD) tripeptide were systematically calculated. Dipole correction slightly enhanced the interaction between molecules and Mg surfaces, but the Eads trend remained unchanged. The addition of alloying elements improved the interaction of molecules and Mg-based alloy surfaces. This study will be of fundamental importance in understanding the interaction regularity of molecules on Mg and Mg-based alloy surfaces and provide possibilities for surface modification design of biomedical materials. Tripeptide ; Aspartic acid ; Biomolecule ; Molecule ; Glycine ; Arginine ; Amino acid ; Chemistry ; Adsorption ; Stereochemistry ; Combinatorial chemistry ; Organic chemistry ; Biochemistry Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials ; Corrosion Behavior and Inhibition
Authors: Wenda Li | Lingyan Huang | Hongyi Zhang | Yong Wu | Facai Wei | Tingting Zhang | Jianwei Fu | Chengbin Jing | Jiangong Cheng | Shaohua Liu
Source: Matter (article, 2023-05-19)
Field: Materials Science — Materials Chemistry

Supramolecular mineralization strategy for engineering covalent organic frameworks with superior Zn-I2 battery performances Mineralization (soil science) ; Covalent bond ; Supramolecular chemistry ; Chemistry ; Materials science ; Organic chemistry ; Molecule Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Shadfar Davoodi | Hung Vo Thanh | David A. Wood | Mohammad Mehrad | Mohammed Al-Shargabi | Valeriy S. Rukavishnikov
Source: Separation and Purification Technology (article, 2023-04-10)
Field: Materials Science — Materials Chemistry

Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables Extreme learning machine ; Artificial neural network ; Adaptive neuro fuzzy inference system ; Machine learning ; Support vector machine ; Leverage (statistics) ; Artificial intelligence ; Computer science ; Outlier ; Variables ; Data mining ; Fuzzy logic ; Fuzzy control system Hydrogen Storage and Materials ; Fuel Cells and Related Materials ; Hybrid Renewable Energy Systems
Authors: Jarno Laakso | Lauri Himanen | Henrietta Homm | Eiaki V. Morooka | Marc O. J. Jäger | Milica Todorović | Patrick Rinke
Source: The Journal of Chemical Physics (article, 2023-06-20)
Field: Materials Science — Materials Chemistry

Updates to the DScribe library: New descriptors and derivatives We present an update of the DScribe package, a Python library for atomistic descriptors. The update extends DScribe's descriptor selection with the Valle-Oganov materials fingerprint and provides descriptor derivatives to enable more advanced machine learning tasks, such as force prediction and structure optimization. For all descriptors, numeric derivatives are now available in DScribe. For the many-body tensor representation (MBTR) and the Smooth Overlap of Atomic Positions (SOAP), we have also implemented analytic derivatives. We demonstrate the effectiveness of the descriptor derivatives for machine learning models of Cu clusters and perovskite alloys. Python (programming language) ; Computer science ; Representation (politics) ; Artificial intelligence ; Tensor (intrinsic definition) ; Machine learning ; Mathematics ; Programming language ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Yiwen Hu | Markus J. Buehler
Source: APL Machine Learning (article, 2023-02-14)
Field: Materials Science — Materials Chemistry

Deep language models for interpretative and predictive materials science Machine learning (ML) has emerged as an indispensable methodology to describe, discover, and predict complex physical phenomena that efficiently help us learn underlying functional rules, especially in cases when conventional modeling approaches cannot be applied. While conventional feedforward neural networks are typically limited to performing tasks related to static patterns in data, recursive models can both work iteratively based on a changing input and discover complex dynamical relationships in the data. Deep language models can model flexible modalities of data and are capable of learning rich dynamical behaviors as they operate on discrete or continuous symbols that define the states of a physical system, yielding great potential toward end-to-end predictions. Similar to how words form a sentence, materials can be considered as a self-assembly of physically interacted building blocks, where the emerging functions of materials are analogous to the meaning of sentences. While discovering the fundamental relationships between building blocks and function emergence can be challenging, language models, such as recurrent neural networks and long-short term memory networks, and, in particular, attention models, such as the transformer architecture, can solve many such complex problems. Application areas of such models include protein folding, molecular property prediction, prediction of material failure of complex nonlinear architected materials, and also generative strategies for materials discovery. We outline challenges and opportunities, especially focusing on extending the deep-rooted kinship of humans with symbolism toward generalizable artificial intelligence (AI) systems using neuro-symbolic AI, and outline how tools such as ChatGPT and DALL·E can drive materials discovery. Computer science ; Bridging (networking) ; Artificial intelligence ; Generative grammar ; Deep learning ; Artificial neural network ; Function (biology) ; Sentence ; Recurrent neural network ; Machine learning ; Cognitive science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Aditya Nandy | Shuwen Yue | Changhwan Oh | Chenru Duan | Gianmarco Terrones | Yongchul G. Chung | Heather J. Kulik
Source: Matter (article, 2023-04-05)
Field: Chemistry — Inorganic Chemistry

A database of ultrastable MOFs reassembled from stable fragments with machine learning models Leverage (statistics) ; Metal-organic framework ; Stability (learning theory) ; Deliverable ; Database ; Computer science ; Materials science ; Nanotechnology ; Chemistry ; Artificial intelligence ; Adsorption ; Machine learning ; Engineering ; Systems engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Aik Rui Tan | Shingo Urata | Samuel Goldman | Johannes C. B. Dietschreit | Rafael Gómez‐Bombarelli
Source: npj Computational Materials (article, 2023-12-16)
Field: Materials Science — Materials Chemistry

Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles Abstract Neural networks (NNs) often assign high confidence to their predictions, even for points far out of distribution, making uncertainty quantification (UQ) a challenge. When they are employed to model interatomic potentials in materials systems, this problem leads to unphysical structures that disrupt simulations, or to biased statistics and dynamics that do not reflect the true physics. Differentiable UQ techniques can find new informative data and drive active learning loops for robust potentials. However, a variety of UQ techniques, including newly developed ones, exist for atomistic simulations and there are no clear guidelines for which are most effective or suitable for a given case. In this work, we examine multiple UQ schemes for improving the robustness of NN interatomic potentials (NNIPs) through active learning. In particular, we compare incumbent ensemble-based methods against strategies that use single, deterministic NNs: mean-variance estimation (MVE), deep evidential regression, and Gaussian mixture models (GMM). We explore three datasets ranging from in-domain interpolative learning to more extrapolative out-of-domain generalization challenges: rMD17, ammonia inversion, and bulk silica glass. Performance is measured across multiple metrics relating model error to uncertainty. Our experiments show that none of the methods consistently outperformed each other across the various metrics. Ensembling remained better at generalization and for NNIP robustness; MVE only proved effective for in-domain interpolation, while GMM was better out-of-domain; and evidential regression, despite its promise, was not the preferable alternative in any of the cases. More broadly, cost-effective, single deterministic models cannot yet consistently match or outperform ensembling for uncertainty quantification in NNIPs. Robustness (evolution) ; Computer science ; Artificial neural network ; Uncertainty quantification ; Generalization ; Artificial intelligence ; Applicability domain ; Gaussian ; Machine learning ; Mixture model ; Algorithm ; Mathematics Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: Laura K. G. Ackerman | Stavros K. Kariofillis | Daniel J. Weix
Source: Journal of the American Chemical Society (review, 2023-03-13)
Field: Chemistry — Inorganic Chemistry

Multimetallic-Catalyzed C–C Bond-Forming Reactions: From Serendipity to Strategy The use of two or more metal catalysts in a reaction is a powerful synthetic strategy to access complex targets efficiently and selectively from simple starting materials. While capable of uniting distinct reactivities, the principles governing multimetallic catalysis are not always intuitive, making the discovery and optimization of new reactions challenging. Here, we outline our perspective on the design elements of multimetallic catalysis using precedent from well-documented C-C bond-forming reactions. These strategies provide insight into the synergy of metal catalysts and compatibility of the individual components of a reaction. Advantages and limitations are discussed to promote further development of the field. Serendipity ; Chemistry ; Catalysis ; Nanotechnology ; Combinatorial chemistry ; Biochemical engineering ; Organic chemistry ; Epistemology Asymmetric Hydrogenation and Catalysis ; Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions
Authors: Indrek Kalvet | Mary Ortmayer | Jingming Zhao | Rebecca Crawshaw | Nathan M. Ennist | Colin Levy | Anindya Roy | Anthony P. Green | David Baker
Source: Journal of the American Chemical Society (article, 2023-06-21)
Field: Chemistry — Inorganic Chemistry

Design of Heme Enzymes with a Tunable Substrate Binding Pocket Adjacent to an Open Metal Coordination Site The catalytic versatility of pentacoordinated iron is highlighted by the broad range of natural and engineered activities of heme enzymes such as cytochrome P450s, which position a porphyrin cofactor coordinating a central iron atom below an open substrate binding pocket. This catalytic prowess has inspired efforts to design de novo helical bundle scaffolds that bind porphyrin cofactors. However, such designs lack the large open substrate binding pocket of P450s, and hence, the range of chemical transformations accessible is limited. Here, with the goal of combining the advantages of the P450 catalytic site geometry with the almost unlimited customizability of de novo protein design, we design a high-affinity heme-binding protein, dnHEM1, with an axial histidine ligand, a vacant coordination site for generating reactive intermediates, and a tunable distal pocket for substrate binding. A 1.6 Å X-ray crystal structure of dnHEM1 reveals excellent agreement to the design model with key features programmed as intended. The incorporation of distal pocket substitutions converted dnHEM1 into a proficient peroxidase with a stable neutral ferryl intermediate. In parallel, dnHEM1 was redesigned to generate enantiocomplementary carbene transferases for styrene cyclopropanation (up to 93% isolated yield, 5000 turnovers, 97:3 e.r.) by reconfiguring the distal pocket to accommodate calculated transition state models. Our approach now enables the custom design of enzymes containing cofactors adjacent to binding pockets with an almost unlimited variety of shapes and functionalities. Chemistry ; Porphyrin ; Heme ; Ligand (biochemistry) ; Stereochemistry ; Substrate (aquarium) ; Active site ; Cofactor ; Protein design ; Binding site ; Catalysis ; Combinatorial chemistry ; Crystallography ; Enzyme ; Protein structure ; Photochemistry ; Biochemistry Metal-Catalyzed Oxygenation Mechanisms ; Biochemical and Molecular Research ; Cyclopropane Reaction Mechanisms
Authors: Nhi Le | Kyoungtae Kim
Source: International Journal of Molecular Sciences (review, 2023-08-11)
Field: Materials Science — Materials Chemistry

Current Advances in the Biomedical Applications of Quantum Dots: Promises and Challenges Quantum dots (QDs) are a type of nanoparticle with exceptional photobleaching-resistant fluorescence. They are highly sought after for their potential use in various optical-based biomedical applications. However, there are still concerns regarding the use of quantum dots. As such, much effort has been invested into understanding the mechanisms behind the behaviors of QDs, so as to develop safer and more biocompatible quantum dots. In this mini-review, we provide an update on the recent advancements regarding the use of QDs in various biomedical applications. In addition, we also discuss# the current challenges and limitations in the use of QDs and propose a few areas of interest for future research. Quantum dot ; Nanotechnology ; Biocompatible material ; SAFER ; Computer science ; Materials science ; Engineering ; Biomedical engineering ; Computer security Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications ; Carbon and Quantum Dots Applications