jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexander Azarov | Javier García‐Fernández | J. Zhao | Flyura Djurabekova | Huan He | Ru He | Øystein Prytz | Lasse Vines | Umutcan Bektas | Paul Chekhonin | Nico Klingner | Gregor Hlawacek | Andrej Kuznetsov
Source: Nature Communications (article, 2023-08-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Universal radiation tolerant semiconductor Radiation tolerance is determined as the ability of crystalline materials to withstand the accumulation of the radiation induced disorder. Nevertheless, for sufficiently high fluences, in all by far known semiconductors it ends up with either very high disorder levels or amorphization. Here we show that gamma/beta (γ/β) double polymorph Ga<sub>2</sub>O<sub>3</sub> structures exhibit remarkably high radiation tolerance. Specifically, for room temperature experiments, they tolerate a disorder equivalent to hundreds of displacements per atom, without severe degradations of crystallinity; in comparison with, e.g., Si amorphizable already with the lattice atoms displaced just once. We explain this behavior by an interesting combination of the Ga- and O- sublattice properties in γ-Ga<sub>2</sub>O<sub>3</sub>. In particular, O-sublattice exhibits a strong recrystallization trend to recover the face-centered-cubic stacking despite the stronger displacement of O atoms compared to Ga during the active periods of cascades. Notably, we also explained the origin of the β-to-γ Ga<sub>2</sub>O<sub>3</sub> transformation, as a function of the increased disorder in β-Ga<sub>2</sub>O<sub>3</sub> and studied the phenomena as a function of the chemical nature of the implanted atoms. As a result, we conclude that γ/β double polymorph Ga<sub>2</sub>O<sub>3</sub> structures, in terms of their radiation tolerance properties, benchmark a class of universal radiation tolerant semiconductors. Semiconductor ; Computational biology ; Computer science ; Biology ; Physics ; Optoelectronics Ga2O3 and related materials ; Electronic and Structural Properties of Oxides ; Perovskite Materials and Applications
Authors: Hua Zhang | Yuan Liu | Lauren Fields | Xudong Shi | Penghsuan Huang | Haiyan Lu | Andrew J. Schneider | Xindi Tang | Luigi Puglielli | Nathan V. Welham | Lingjun Li
Source: Nature Communications (article, 2023-08-25)
Field: Chemistry — Spectroscopy

Single-cell lipidomics enabled by dual-polarity ionization and ion mobility-mass spectrometry imaging Single-cell (SC) analysis provides unique insight into individual cell dynamics and cell-to-cell heterogeneity. Here, we utilize trapped ion mobility separation coupled with dual-polarity ionization mass spectrometry imaging (MSI) to enable high-throughput in situ profiling of the SC lipidome. Multimodal SC imaging, in which dual-polarity-mode MSI is used to perform serial data acquisition runs on individual cells, significantly enhanced SC lipidome coverage. High-spatial resolution SC-MSI identifies both inter- and intracellular lipid heterogeneity; this heterogeneity is further explicated by Uniform Manifold Approximation and Projection and machine learning-driven classifications. We characterize SC lipidome alteration in response to stearoyl-CoA desaturase 1 inhibition and, additionally, identify cell-layer specific lipid distribution patterns in mouse cerebellar cortex. This integrated multimodal SC-MSI technology enables high-resolution spatial mapping of intercellular and cell-to-cell lipidome heterogeneity, SC lipidome remodeling induced by pharmacological intervention, and region-specific lipid diversity within tissue. Lipidomics ; Ion-mobility spectrometry ; Mass spectrometry ; Polarity (international relations) ; Mass spectrometry imaging ; Ion ; Ion-mobility spectrometry–mass spectrometry ; Chemistry ; Ionization ; Electrospray ionization ; Cell ; Chromatography ; Sample preparation in mass spectrometry ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Feng Feng | Xiyong Song | Zan Tan | Yujie Tu | Longyou Xiao | Pengfei Xie | Yahao Ma | Xiumin Sun | Junwu Ma | Limin Rong | Liumin He
Source: Science Advances (article, 2023-06-23)
Field: Materials Science — Biomaterials

Cooperative assembly of a designer peptide and silk fibroin into hybrid nanofiber gels for neural regeneration after spinal cord injury Local reconstruction of a permissive environment with biomaterials is a promising strategy to treat spinal cord injury (SCI). We reported a hybrid hydrogel fabricated from a small functional self-assembling peptide (F-SAP) and large silk fibroin (SF). The diffusion of SF micelles into F-SAP solution was driven by the dynamic synergy between osmotic pressure and F-SAP/SF electrostatic interactions, resulting in the rearrangement of SF micelles and the formation of rod-like filaments with axes nearly perpendicular to F-SAP nanofibers. Spectroscopy analysis, including circular dichroism, Raman and fluorescence, indicated conformation changes of SF from random coil to β sheet, which contributed to enhanced mechanical properties of the resultant hybrid hydrogel. Furthermore, the F-SAP/SF hybrid hydrogel coupled with controlled release of NT-3 provided a permissive environment for neural regeneration by providing nanofibrous substrates for regenerating axons, inflammatory modulation and remyelination, consequently resulting in improved locomotion and electrophysiological properties. This hydrogel could be used as a long-term stent in vivo for the treatment of SCI. Fibroin ; Nanofiber ; Regeneration (biology) ; Biophysics ; Self-healing hydrogels ; Materials science ; Neural tissue engineering ; Electrospinning ; Nanotechnology ; Tissue engineering ; Chemistry ; Biomedical engineering ; SILK ; Polymer ; Polymer chemistry ; Cell biology ; Composite material Silk-based biomaterials and applications ; Nerve injury and regeneration ; Supramolecular Self-Assembly in Materials
Authors: Gregory J. O. Beran
Source: Chemical Science (review, 2023-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials The reliability of organic molecular crystal structure prediction has improved tremendously in recent years. Crystal structure predictions for small, mostly rigid molecules are quickly becoming routine. Structure predictions for larger, highly flexible molecules are more challenging, but their crystal structures can also now be predicted with increasing rates of success. These advances are ushering in a new era where crystal structure prediction drives the experimental discovery of new solid forms. After briefly discussing the computational methods that enable successful crystal structure prediction, this perspective presents case studies from the literature that demonstrate how state-of-the-art crystal structure prediction can transform how scientists approach problems involving the organic solid state. Applications to pharmaceuticals, porous organic materials, photomechanical crystals, organic semi-conductors, and nuclear magnetic resonance crystallography are included. Finally, efforts to improve our understanding of which predicted crystal structures can actually be produced experimentally and other outstanding challenges are discussed. Crystal (programming language) ; Crystal structure prediction ; Organic molecules ; Nanotechnology ; Materials science ; Chemistry ; Crystal structure ; Molecule ; Computer science ; Organic chemistry Crystallography and molecular interactions ; Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography
Authors: Wei Chen | Yuan Gao | Yuyang Li | Yiming Yan | Jun‐Yu Ou | Wenzhuang Ma | Jinfeng Zhu
Source: Advanced Science (article, 2023-02-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Broadband Solar Metamaterial Absorbers Empowered by Transformer‐Based Deep Learning The research of metamaterial shows great potential in the field of solar energy harvesting. In the past decade, the design of broadband solar metamaterial absorber (SMA) has attracted a surge of interest. The conventional design typically requires brute-force optimizations with a huge sampling space of structure parameters. Very recently, deep learning (DL) has provided a promising way in metamaterial design, but its application on SMA development is barely reported due to the complicated features of broadband spectrum. Here, this work develops the DL model based on metamaterial spectrum transformer (MST) for the powerful design of high-performance SMAs. The MST divides the optical spectrum of metamaterial into N patches, which overcomes the severe problem of overfitting in traditional DL and boosts the learning capability significantly. A flexible design tool based on free customer definition is developed to facilitate the real-time on-demand design of metamaterials with various optical functions. The scheme is applied to the design and fabrication of SMAs with graded-refractive-index nanostructures. They demonstrate the high average absorptance of 94% in a broad solar spectrum and exhibit exceptional advantages over many state-of-the-art counterparts. The outdoor testing implies the high-efficiency energy collection of about 1061 kW h m<sup>-2</sup> from solar radiation annually. This work paves a way for the rapid smart design of SMA, and will also provide a real-time developing tool for many other metamaterials and metadevices. Metamaterial ; Broadband ; Computer science ; Electronic engineering ; Telecommunications ; Optoelectronics ; Materials science ; Engineering Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Energy Harvesting in Wireless Networks
Authors: Mingjin Cui | Bo Xu | Lianhui Wang
Source: BMEMat (article, 2023-09-19)
Field: Materials Science — Materials Chemistry

Recent advances in multi‐metallic‐based nanozymes for enhanced catalytic cancer therapy Abstract Nanozymes have emerged as a promising alternative to natural enzymes, effectively addressing natural enzymes' inherent limitation. Versatility and potential applications of nanozyme span across various fields, with catalytic tumor therapy being one prominent area. This has sparked significant interest and exploration in the utilization of nanozymes for targeted cancer treatment. Recent advancements in interdisciplinary research, nanotechnology, biotechnology, and catalytic technology have led to the emergence of multi‐metallic‐based nanozymes, which exhibit tremendous potential for further development. This review focuses on investigating the synergistic effects of multi‐metallic‐based nanozymes, aiming to enhance our understanding of their catalytic activities and facilitate their broader applications. We comprehensively survey the remarkable achievements in the synthesis, catalytic mechanisms, and the latest applications of multi‐metallic‐based nanozymes in cancer catalytic therapy. Furthermore, we identify the current limitations and prospects of multi‐metallic‐based nanozymes in the development of new materials and the application of novel technologies, along with the potential challenges associated with catalytic cancer therapy. This review underscores the significance of multi‐metallic‐based nanozymes and emphasizes the need for continued exploration as well as their potential impact on the development of novel materials and the realization of breakthroughs in catalytic tumor therapy. Nanotechnology ; Cancer therapy ; Biochemical engineering ; Computer science ; Cancer ; Materials science ; Medicine ; Engineering Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Nanoplatforms for cancer theranostics
Authors: Xiao‐Wen Gu | Enyu Wu | Jia‐Xin Wang | Hui‐Min Wen | Banglin Chen | Bin Li | Guodong Qian
Source: Science Advances (article, 2023-08-04)
Field: Chemistry — Inorganic Chemistry

Programmed fluorine binding engineering in anion-pillared metal-organic framework for record trace acetylene capture from ethylene Porous physisorbents are attractive candidates for selective capture of trace gas or volatile compounds due to their low energy footprints. However, many physisorbents suffer from insufficient sorbate-sorbent interactions, resulting in low uptake or inadequate selectivity when gases are present at trace levels. Here, we report a strategy of programmed fluorine binding engineering in anion-pillared metal-organic frameworks to maximize C<sub>2</sub>H<sub>2</sub> binding affinity for benchmark trace C<sub>2</sub>H<sub>2</sub> capture from C<sub>2</sub>H<sub>4</sub>. A robust material (ZJU-300a) was elaborately designed to provide multiple-site fluorine binding model, resulting in an ultrastrong C<sub>2</sub>H<sub>2</sub> binding affinity. ZJU-300a exhibits a record-high C<sub>2</sub>H<sub>2</sub> uptake of 3.23 millimoles per gram (at 0.01 bar and 296 kelvin) and one of the highest C<sub>2</sub>H<sub>2</sub>/C<sub>2</sub>H<sub>4</sub> selectivity (1672). The adsorption binding of C<sub>2</sub>H<sub>2</sub> and C<sub>2</sub>H<sub>4</sub> was visualized by gas-loaded ZJU-300a structures. The separation capacity was confirmed by breakthrough experiments for 1/99 C<sub>2</sub>H<sub>2</sub>/C<sub>2</sub>H<sub>4</sub> mixtures, affording the maximal dynamic selectivity (264) and C<sub>2</sub>H<sub>4</sub> productivity of 436.7 millimoles per gram. Selectivity ; Sorbent ; Adsorption ; Fluorine ; Sorption ; Acetylene ; Ethylene ; Chemistry ; Materials science ; Inorganic chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Inorganic Fluorides and Related Compounds
Authors: Kangming Li | Daniel Persaud | Kamal Choudhary | Brian DeCost | Michael T. Greenwood | Jason Hattrick‐Simpers
Source: Nature Communications (article, 2023-11-10)
Field: Materials Science — Materials Chemistry

Exploiting redundancy in large materials datasets for efficient machine learning with less data Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multiple large datasets for various material properties, by revealing that up to 95% of data can be safely removed from machine learning training with little impact on in-distribution prediction performance. The redundant data is related to over-represented material types and does not mitigate the severe performance degradation on out-of-distribution samples. In addition, we show that uncertainty-based active learning algorithms can construct much smaller but equally informative datasets. We discuss the effectiveness of informative data in improving prediction performance and robustness and provide insights into efficient data acquisition and machine learning training. This work challenges the "bigger is better" mentality and calls for attention to the information richness of materials data rather than a narrow emphasis on data volume. Computer science ; Redundancy (engineering) ; Robustness (evolution) ; Machine learning ; Training set ; Artificial intelligence ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Joshua Schrier | Alexander J. Norquist | Tonio Buonassisi | Jakoah Brgoch
Source: Journal of the American Chemical Society (review, 2023-09-27)
Field: Materials Science — Materials Chemistry

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science Exceptional molecules and materials with one or more extraordinary properties are both technologically valuable and fundamentally interesting, because they often involve new physical phenomena or new compositions that defy expectations. Historically, exceptionality has been achieved through serendipity, but recently, machine learning (ML) and automated experimentation have been widely proposed to accelerate target identification and synthesis planning. In this Perspective, we argue that the data-driven methods commonly used today are well-suited for optimization but not for the realization of new exceptional materials or molecules. Finding such outliers should be possible using ML, but only by shifting away from using traditional ML approaches that tweak the composition, crystal structure, or reaction pathway. We highlight case studies of high-<i>T</i><sub>c</sub> oxide superconductors and superhard materials to demonstrate the challenges of ML-guided discovery and discuss the limitations of automation for this task. We then provide six recommendations for the development of ML methods capable of exceptional materials discovery: (i) Avoid the tyranny of the middle and focus on extrema; (ii) When data are limited, qualitative predictions that provide direction are more valuable than interpolative accuracy; (iii) Sample what can be made and how to make it and defer optimization; (iv) Create room (and look) for the unexpected while pursuing your goal; (v) Try to fill-in-the-blanks of input and output space; (vi) Do not confuse human understanding with model interpretability. We conclude with a description of how these recommendations can be integrated into automated discovery workflows, which should enable the discovery of exceptional molecules and materials. Serendipity ; Chemical space ; Interpretability ; Workflow ; Identification (biology) ; Task (project management) ; Data science ; Artificial intelligence ; Computer science ; Space (punctuation) ; Chemistry ; Nanotechnology ; Drug discovery ; Systems engineering ; Epistemology ; Engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Wendong Wang | Nick Clark | Matthew J. Hamer | Amy Carl | Endre Tóvári | Sam Sullivan Allsop | Evan Tillotson | Yunze Gao | Hugo de Latour | Francisco Selles | James Howarth | Eli G. Castanon | Mingwei Zhou | Haoyu Bai | Xiao Li | Astrid Weston | Kenji Watanabe | Takashi Taniguchi | Cecilia Mattevi | Thomas H. Bointon | Paul V. Wiper | Andrew J. Strudwick | Л. А. Пономаренко | Andrey V. Kretinin | Sarah J. Haigh | Alex Summerfield | Roman Gorbachev
Source: Nature Electronics (article, 2023-12-05)
Field: Materials Science — Materials Chemistry

Clean assembly of van der Waals heterostructures using silicon nitride membranes Abstract Van der Waals heterostructures are fabricated by layer-by-layer assembly of individual two-dimensional materials and can be used to create a wide range of electronic devices. However, current assembly techniques typically use polymeric supports, which limit the cleanliness—and thus the electronic performance—of such devices. Here, we report a polymer-free technique for assembling van der Waals heterostructures using flexible silicon nitride membranes. Eliminating the polymeric supports allows the heterostructures to be fabricated in harsher environmental conditions (incompatible with a polymer) such as at temperatures of up to 600 °C, in organic solvents and in ultra-high vacuum. The resulting heterostructures have high-quality interfaces without interlayer contamination and exhibit strong electronic and optoelectronic behaviour. We use the technique to assemble twisted-graphene heterostructures in ultra-high vacuum, resulting in a tenfold improvement in moiré superlattice homogeneity compared to conventional transfer techniques. Heterojunction ; Materials science ; van der Waals force ; Superlattice ; Graphene ; Optoelectronics ; Nanotechnology ; Silicon nitride ; Polymer ; Silicon ; Layer (electronics) ; Composite material ; Chemistry ; Molecule Graphene research and applications ; 2D Materials and Applications ; Energy Harvesting in Wireless Networks
Authors: Lateef A. Jolaoso | Idris Temitope Bello | Opeyemi A. Ojelade | Abu Yousuf | Chuancheng Duan | Pejman Kazempoor
Source: International Journal of Hydrogen Energy (review, 2023-05-26)
Field: Materials Science — Materials Chemistry

Operational and scaling-up barriers of SOEC and mitigation strategies to boost H2 production- a comprehensive review Commercialization ; Computer science ; Biochemical engineering ; Environmentally friendly ; Scope (computer science) ; Process engineering ; Business ; Engineering Advancements in Solid Oxide Fuel Cells ; Advanced battery technologies research ; Fuel Cells and Related Materials
Authors: Qian Shi | Chenyu Jin | Zhiqiang Chen | Le An | Tiejun Wang
Source: Advanced Functional Materials (article, 2023-05-18)
Field: Materials Science — Polymers and Plastics

On the Welding of Vitrimers: Chemistry, Mechanics and Applications Abstract Linear chains in thermoplastics make them relatively weak in performance but inherently weldable and recyclable. By contrast, thermosets with permanently crosslinked networks possess outstanding mechanical performance, thermal, and chemical stability, but are unweldable and unrecyclable. In the last decade, a kind of thermoplastics‐like thermosets termed as vitrimer has been developed with extensive applications, in which welding of vitrimers plays a central and fundamental role. Herein, we present the current state of the art of the welding of vitrimers and discuss the welding of vitrimers from a broad picture of chemistry, physics, and mechanics: i) chemistry and mechanics of the welding of vitrimers; ii) applicability of the mechanical assessment methods for the welding of vitrimers; iii) design principles and implement strategies to the welding of vitrimers; iv) effects of welding conditions on the welding strength and toughness; and v) applications to the adhesion of chemically inert materials. Finally, advantages, challenges, and open questions to the welding of vitrimers are highlighted, and future opportunities in chemistry, mechanics, design of tough welding, artificial intelligence aided programming of welding technology, mechanical assessment standard, and so on are discussed. The development of vitrimer welding would fuse disciplines and make transformative impact in polymer industry. Welding ; Materials science ; Mechanical engineering ; Thermosetting polymer ; Composite material ; Engineering Polymer composites and self-healing ; Conducting polymers and applications ; Advanced Polymer Synthesis and Characterization
Authors: Juan J. Rojas | James A. Bull
Source: Journal of Medicinal Chemistry (review, 2023-09-07)
Field: Chemistry — Organic Chemistry

Oxetanes in Drug Discovery Campaigns The oxetane ring is an emergent, underexplored motif in drug discovery that shows attractive properties such as low molecular weight, high polarity, and marked three-dimensionality. Oxetanes have garnered further interest as isosteres of carbonyl groups and as molecular tools to fine-tune physicochemical properties of drug compounds such as p<i>K</i><sub>a</sub>, LogD, aqueous solubility, and metabolic clearance. This perspective highlights recent applications of oxetane motifs in drug discovery campaigns (2017-2022), with emphasis on the effect of the oxetane on medicinally relevant properties and on the building blocks used to incorporate the oxetane ring. Based on this analysis, we provide an overview of the potential benefits of appending an oxetane to a drug compound, as well as potential pitfalls, challenges, and future directions. Chemistry ; Drug discovery ; Drug ; Pharmacology ; Computational biology ; Biochemistry Click Chemistry and Applications ; Synthesis and Catalytic Reactions ; Computational Drug Discovery Methods