jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Michele Formica | Tatiana Rogova | Heyao Shi | Naoto Sahara | Branislav Ferko | Alistair J. M. Farley | Kirsten E. Christensen | Fernanda Duarte | Ken Yamazaki | Darren J. Dixon
Source: Nature Chemistry (article, 2023-05-01)
Field: Chemistry — Organic Chemistry

Catalytic enantioselective nucleophilic desymmetrization of phosphonate esters Molecules that contain a stereogenic phosphorus atom are crucial to medicine, agrochemistry and catalysis. While methods are available for the selective construction of various chiral organophosphorus compounds, catalytic enantioselective approaches for their synthesis are far less common. Given the vastness of possible substituent combinations around a phosphorus atom, protocols for their preparation should also be divergent, providing facile access not only to one but to many classes of phosphorus compounds. Here we introduce a catalytic and enantioselective strategy for the preparation of an enantioenriched phosphorus(V) centre that can be diversified enantiospecifically to a wide range of biologically relevant phosphorus(V) compounds. The process, which involves an enantioselective nucleophilic substitution catalysed by a superbasic bifunctional iminophosphorane catalyst, can accommodate a wide range of carbon substituents at phosphorus. The resulting stable, yet versatile, synthetic intermediates can be combined with a multitude of medicinally relevant O-, N- and S-based nucleophiles. Enantioselective synthesis ; Chemistry ; Desymmetrization ; Nucleophile ; Stereocenter ; Bifunctional ; Phosphonate ; Catalysis ; Combinatorial chemistry ; Substituent ; Organic chemistry Organophosphorus compounds synthesis ; Chemical Synthesis and Analysis ; Asymmetric Hydrogenation and Catalysis
Authors: Pan Xu | Jingjing Xie | Duo‐Sheng Wang | X. Peter Zhang
Source: Nature Chemistry (article, 2023-01-12)
Field: Chemistry — Organic Chemistry

Metalloradical approach for concurrent control in intermolecular radical allylic C−H amination Chemistry ; Amination ; Regioselectivity ; Allylic rearrangement ; Chemoselectivity ; Combinatorial chemistry ; Intermolecular force ; Catalysis ; Radical ; Organic synthesis ; Reactivity (psychology) ; Organic chemistry ; Molecule Synthesis and Catalytic Reactions ; Catalytic C–H Functionalization Methods ; Radical Photochemical Reactions
Authors: Roshan Patel | Michael A. Webb
Source: ACS Applied Bio Materials (review, 2023-01-26)
Field: Materials Science — Materials Chemistry

Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning Polymers, with the capacity to tunably alter properties and response based on manipulation of their chemical characteristics, are attractive components in biomaterials. Nevertheless, their potential as functional materials is also inhibited by their complexity, which complicates rational or brute-force design and realization. In recent years, machine learning has emerged as a useful tool for facilitating materials design via efficient modeling of structure-property relationships in the chemical domain of interest. In this Spotlight, we discuss the emergence of data-driven design of polymers that can be deployed in biomaterials with particular emphasis on complex copolymer systems. We outline recent developments, as well as our own contributions and takeaways, related to high-throughput data generation for polymer systems, methods for surrogate modeling by machine learning, and paradigms for property optimization and design. Throughout this discussion, we highlight key aspects of successful strategies and other considerations that will be relevant to the future design of polymer-based biomaterials with target properties. Rational design ; Computer science ; Throughput ; Realization (probability) ; Domain (mathematical analysis) ; Property (philosophy) ; Biochemical engineering ; Nanotechnology ; Material Design ; Systems engineering ; Materials science ; Engineering ; Wireless Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Polymer Synthesis and Characterization
Authors: Warren C. W. Chan
Source: BME Frontiers (review, 2023-01-01)
Field: Materials Science — Biomaterials

Principles of Nanoparticle Delivery to Solid Tumors The effective treatment of patients with cancer hinges on the delivery of therapeutics to a tumor site. Nanoparticles provide an essential transport system. We present 5 principles to consider when designing nanoparticles for cancer targeting: (a) Nanoparticles acquire biological identity in vivo, (b) organs compete for nanoparticles in circulation, (c) nanoparticles must enter solid tumors to target tumor components, (d) nanoparticles must navigate the tumor microenvironment for cellular or organelle targeting, and (e) size, shape, surface chemistry, and other physicochemical properties of nanoparticles influence their transport process to the target. This review article describes these principles and their application for engineering nanoparticle delivery systems to carry therapeutics to tumors or other disease targets. Nanoparticle ; Nanotechnology ; Drug delivery ; Cancer ; Nanomedicine ; In vivo ; Materials science ; Chemistry ; Computer science ; Medicine ; Biology Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Yong Zheng | Niaz Ali Khan | Xuepeng Ni | Kai A. I. Zhang | Yi Shen | Niu Huang | Xin Ying Kong | Liqun Ye
Source: Chemical Communications (review, 2023-01-01)
Field: Materials Science — Materials Chemistry

Emerging covalent triazine framework-based nanomaterials for electrochemical energy storage and conversion Recently, the increasing concerns regarding environmental and energy-related issues due to the use of fossil fuels have triggered extensive research on sustainable electrochemical energy storage and conversion (EESC). In this case, covalent triazine frameworks (CTFs) possess a large surface area, tailorable conjugated structures, electron donating-accepting/conducting moieties, and excellent chemical and thermal stabilities. These merits make them leading candidates for EESC. However, their poor electrical conductivity impedes electron and ion conduction, leading to unsatisfactory electrochemical performances, which limit their commercial applications. Thus, to overcome these challenges, CTF-based nanocomposites and their derivatives such as heteroatom-doped porous carbons, which inherit most of the merits of pristine CTFs, lead to excellent performances in the field of EESC. In this review, initially, we briefly highlight the existing strategies for the synthesis of CTFs with application-targeted properties. Next, we review the contemporary progress of CTFs and their derivatives related to electrochemical energy storage (supercapacitors, alkali-ion batteries, lithium-sulfur batteries, <i>etc.</i>) and conversion (oxygen reduction/evolution reaction, hydrogen evolution reaction, carbon dioxide reduction reaction, <i>etc.</i>). Finally, we discuss perspectives on current challenges and recommendations for the further development of CTF-based nanomaterials in burgeoning EESC research. Nanotechnology ; Energy storage ; Electrochemistry ; Materials science ; Electrochemical energy conversion ; Supercapacitor ; Nanomaterials ; Electrochemical energy storage ; Chemistry ; Electrode ; Physics Covalent Organic Framework Applications ; Advanced battery technologies research ; Supercapacitor Materials and Fabrication
Authors: Solmaz Karamikamkar | Ezgi Pinar Yalcintas | Reihaneh Haghniaz | Natan Roberto de Barros | Marvin Mecwan | Rohollah Nasiri | Elham Davoodi | Fatemeh Nasrollahi | Ahmet Erdem | Heemin Kang | Junmin Lee | Yangzhi Zhu | Samad Ahadian | Vadim Jucaud | Hajar Maleki | Mehmet R. Dokmeci | Han‐Jun Kim | Ali Khademhosseini
Source: Advanced Science (review, 2023-05-22)
Field: Chemistry — Spectroscopy

Aerogel‐Based Biomaterials for Biomedical Applications: From Fabrication Methods to Disease‐Targeting Applications Aerogel-based biomaterials are increasingly being considered for biomedical applications due to their unique properties such as high porosity, hierarchical porous network, and large specific pore surface area. Depending on the pore size of the aerogel, biological effects such as cell adhesion, fluid absorption, oxygen permeability, and metabolite exchange can be altered. Based on the diverse potential of aerogels in biomedical applications, this paper provides a comprehensive review of fabrication processes including sol-gel, aging, drying, and self-assembly along with the materials that can be used to form aerogels. In addition to the technology utilizing aerogel itself, it also provides insight into the applicability of aerogel based on additive manufacturing technology. To this end, how microfluidic-based technologies and 3D printing can be combined with aerogel-based materials for biomedical applications is discussed. Furthermore, previously reported examples of aerogels for regenerative medicine and biomedical applications are thoroughly reviewed. A wide range of applications with aerogels including wound healing, drug delivery, tissue engineering, and diagnostics are demonstrated. Finally, the prospects for aerogel-based biomedical applications are presented. The understanding of the fabrication, modification, and applicability of aerogels through this study is expected to shed light on the biomedical utilization of aerogels. Aerogel ; Nanotechnology ; Materials science ; Fabrication ; Microfluidics ; Porosity ; Drug delivery ; Composite material Aerogels and thermal insulation ; Advanced Sensor and Energy Harvesting Materials ; 3D Printing in Biomedical Research
Authors: Pinku Chandra Nath | Amiya Ojha | Shubhankar Debnath | Minaxi Sharma | Kandi Sridhar | Prakash Kumar Nayak | Baskaran Stephen Inbaraj
Source: Agronomy (review, 2023-02-15)
Field: Materials Science — Materials Chemistry

Biogeneration of Valuable Nanomaterials from Agro-Wastes: A Comprehensive Review The exponential growth of agricultural output is a result of the increasing world population. Agro-wastes are now acknowledged as an alternative material for the manufacture of renewable, inexpensive, and sustainable bio-composite-based products. Huge amounts of agricultural produce are often lost owing to a lack of processing facilities or storage space. Agriculture wastes contain a significant concentration of carbohydrates as well as various multifunctional groups and organic substances, such as polymeric proteins. Interestingly, nanotechnology provides better potential to transform agricultural wastes easily into valuable and cost-effective products, removing the need to utilize noxious chemicals, which can create a variety of health and environmental difficulties. Recently, there has been an increase in interest in eco-friendly nanomaterial (NM) production techniques that utilize extracts generated from agricultural waste. Such nanoparticles (NPs) have been extensively studied for numerous uses, including antibacterial agents, water purification, the breakdown of industrial colours, and many others. Consequently, the purpose of this review is to investigate the different sources, characteristics, and nano-management of agro-waste; valuable NMs derived from agro-waste; and their possible applications. Agriculture ; Agricultural waste ; Global population ; Waste management ; Population ; Environmental science ; Biochemical engineering ; Business ; Engineering Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Biofuel production and bioconversion
Authors: Sigbjørn Løland Bore | Francesco Paesani
Source: Nature Communications (article, 2023-06-08)
Field: Materials Science — Materials Chemistry

Realistic phase diagram of water from “first principles” data-driven quantum simulations Since the experimental characterization of the low-pressure region of water's phase diagram in the early 1900s, scientists have been on a quest to understand the thermodynamic stability of ice polymorphs on the molecular level. In this study, we demonstrate that combining the MB-pol data-driven many-body potential for water, which was rigorously derived from "first principles" and exhibits chemical accuracy, with advanced enhanced-sampling algorithms, which correctly describe the quantum nature of molecular motion and thermodynamic equilibria, enables computer simulations of water's phase diagram with an unprecedented level of realism. Besides providing fundamental insights into how enthalpic, entropic, and nuclear quantum effects shape the free-energy landscape of water, we demonstrate that recent progress in "first principles" data-driven simulations, which rigorously encode many-body molecular interactions, has opened the door to realistic computational studies of complex molecular systems, bridging the gap between experiments and simulations. Phase diagram ; Statistical physics ; Bridging (networking) ; Quantum ; Molecular dynamics ; Thermodynamic integration ; Computer science ; Physics ; Phase (matter) ; Quantum mechanics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Quantum, superfluid, helium dynamics
Authors: Tanima Bhattacharya | Gye Hwa Shin | Jun Tae Kim
Source: Pharmaceutics (review, 2023-03-22)
Field: Materials Science — Materials Chemistry

Carbon Dots: Opportunities and Challenges in Cancer Therapy Recently, carbon dots (CDs) have been actively studied and reported for their various properties. In particular, the specific characteristics of carbon dots have been considered as a possible technique for cancer diagnosis and therapy. This is also a cutting-edge technology that offers fresh ideas for treating various disorders. Though carbon dots are still in their infancy and have not yet shown their value to society, their discovery has already resulted in some noteworthy advancements. The application of CDs indicates conversion in natural imaging. Photography using CDs has demonstrated extraordinary appropriateness in bio-imaging, the discovery of novel drugs, the delivery of targeted genes, bio-sensing, photodynamic therapy, and diagnosis. This review seeks to provide a comprehensive understanding of CDs, including their benefits, characteristics, applications, and mode of action. In this overview, many CD design strategies will be highlighted. In addition, we will discuss numerous studies on cytotoxic testing to demonstrate the safety of CDs. The current study will address the production method, mechanism, ongoing research, and application of CDs in cancer diagnosis and therapy. Nanotechnology ; Photodynamic therapy ; Cancer therapy ; Cancer ; Medicine ; Computer science ; Materials science ; Chemistry Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Liaw Hui Zoe | Sheba R. David | Rajan Rajabalaya
Source: Toxicology Reports (article, 2023-06-28)
Field: Materials Science — Materials Chemistry

Chitosan nanoparticle toxicity: A comprehensive literature review of in vivo and in vitro assessments for medical applications This literature review did not uncover any published clinical trials with available results. Subsequent research endeavors should prioritize conducting clinical trials involving human volunteers to directly assess toxicity, rather than relying on cell or animal models. Toxicity ; Chitosan ; In vivo ; Cytotoxicity ; Pharmacology ; Zebrafish ; In vitro ; Nanoparticle ; Chemistry ; Medicine ; Nanotechnology ; Biochemistry ; Biology ; Materials science ; Biotechnology Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery
Authors: Annabell Martin | Pablo Rivera‐Fuentes
Source: Nature Chemistry (article, 2023-11-27)
Field: Chemistry — Organic Chemistry

A general strategy to develop fluorogenic polymethine dyes for bioimaging Fluorescence imaging is an invaluable tool to study biological processes and further progress depends on the development of advanced fluorogenic probes that reach intracellular targets and label them with high specificity. Excellent fluorogenic rhodamine dyes have been reported, but they often require long and low-yielding syntheses, and are spectrally limited to the visible range. Here we present a general strategy to transform polymethine compounds into fluorogenic dyes using an intramolecular ring-closure approach. We illustrate the generality of this method by creating both spontaneously blinking and no-wash, turn-on polymethine dyes with emissions across the visible and near-infrared spectrum. These probes are compatible with self-labelling proteins and small-molecule targeting ligands, and can be combined with rhodamine-based dyes for multicolour and fluorescence lifetime multiplexing imaging. This strategy provides access to bright, fluorogenic dyes that emit at wavelengths that are more red-shifted compared with those of existing rhodamine-based dyes. Chemistry ; Rhodamine ; Fluorescence ; Intramolecular force ; Rhodamine B ; Photochemistry ; Nanotechnology ; Combinatorial chemistry ; Organic chemistry ; Optics Click Chemistry and Applications ; Biotin and Related Studies ; Advanced Fluorescence Microscopy Techniques
Authors: Taicheng Guo | Kehan Guo | Bozhao Nan | Zhengwen Liang | Zhichun Guo | Nitesh V. Chawla | Olaf Wiest | Xiangliang Zhang
Source: arXiv (Cornell University) (preprint, 2023-05-27)
Field: Materials Science — Materials Chemistry

What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks Large Language Models (LLMs) with strong abilities in natural language processing tasks have emerged and have been applied in various kinds of areas such as science, finance and software engineering. However, the capability of LLMs to advance the field of chemistry remains unclear. In this paper, rather than pursuing state-of-the-art performance, we aim to evaluate capabilities of LLMs in a wide range of tasks across the chemistry domain. We identify three key chemistry-related capabilities including understanding, reasoning and explaining to explore in LLMs and establish a benchmark containing eight chemistry tasks. Our analysis draws on widely recognized datasets facilitating a broad exploration of the capacities of LLMs within the context of practical chemistry. Five LLMs (GPT-4, GPT-3.5, Davinci-003, Llama and Galactica) are evaluated for each chemistry task in zero-shot and few-shot in-context learning settings with carefully selected demonstration examples and specially crafted prompts. Our investigation found that GPT-4 outperformed other models and LLMs exhibit different competitive levels in eight chemistry tasks. In addition to the key findings from the comprehensive benchmark analysis, our work provides insights into the limitation of current LLMs and the impact of in-context learning settings on LLMs' performance across various chemistry tasks. The code and datasets used in this study are available at https://github.com/ChemFoundationModels/ChemLLMBench. Context (archaeology) ; Benchmark (surveying) ; Computer science ; Task (project management) ; Chemistry ; Engineering ; Geography ; Archaeology ; Cartography Machine Learning in Materials Science ; Topic Modeling
Authors: Guangfei Pan | Feiyang Wang | Chunlei Shang | Hong‐Hui Wu | Guilin Wu | Junheng Gao | Shuize Wang | Zhijun Gao | Xiaoye Zhou | Xinping Mao
Source: International Journal of Minerals Metallurgy and Materials (article, 2023-04-18)
Field: Materials Science — Materials Chemistry

Advances in machine learning- and artificial intelligence-assisted material design of steels Artificial intelligence ; Machine learning ; Computer science ; Field (mathematics) ; Mathematics Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: N. Scott Bobbitt | Kaihang Shi | Benjamin J. Bucior | Haoyuan Chen | Nathaniel Tracy-Amoroso | Zhao Li | Yangzesheng Sun | Julia Merlin | J. Ilja Siepmann | Daniel W. Siderius | Randall Q. Snurr
Source: Journal of Chemical & Engineering Data (article, 2023-01-04)
Field: Chemistry — Inorganic Chemistry

MOFX-DB: An Online Database of Computational Adsorption Data for Nanoporous Materials Machine learning and data mining coupled with molecular modeling have become powerful tools for materials discovery. Metal–organic frameworks (MOFs) are a rich area for this due to their modular construction and numerous applications. Here, we make data from several previous large-scale studies in MOFs and zeolites from our groups (and new data for N2 and Ar adsorption in MOFs) easily accessible in one place. The database includes over three million simulated adsorption data points for H2, CH4, CO2, Xe, Kr, Ar, and N2 in over 160 000 MOFs and 286 zeolites, textural properties like pore sizes and surface areas, and the structure file for each material. We include metadata about the Monte Carlo simulations to enable reproducibility. The database is searchable by MOF properties, and the data are stored in a standardized JavaScript Object Notation format that is interoperable with the NIST adsorption database. We also identify several MOFs that meet high performance targets for multiple applications, such as high storage capacity for both hydrogen and methane or high CO2 capacity plus good Xe/Kr selectivity. By providing this data publicly, we hope to facilitate machine learning studies on these materials, leading to new insights on adsorption in MOFs and zeolites. Nanoporous ; Database ; Adsorption ; Metal-organic framework ; Metadata ; Chemistry ; Interoperability ; Computer science ; World Wide Web Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Chia-Hsiang Lin | Shih‐Hsiu Huang | Ting-Hsuan Lin | Pin Chieh Wu
Source: Nature Communications (article, 2023-11-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurface-empowered snapshot hyperspectral imaging with convex/deep (CODE) small-data learning theory Hyperspectral imaging is vital for material identification but traditional systems are bulky, hindering the development of compact systems. While previous metasurfaces address volume issues, the requirements of complicated fabrication processes and significant footprint still limit their applications. This work reports a compact snapshot hyperspectral imager by incorporating the meta-optics with a small-data convex/deep (CODE) deep learning theory. Our snapshot hyperspectral imager comprises only one single multi-wavelength metasurface chip working in the visible window (500-650 nm), significantly reducing the device area. To demonstrate the high performance of our hyperspectral imager, a 4-band multispectral imaging dataset is used as the input. Through the CODE-driven imaging system, it efficiently generates an 18-band hyperspectral data cube with high fidelity using only 18 training data points. We expect the elegant integration of multi-resonant metasurfaces with small-data learning theory will enable low-profile advanced instruments for fundamental science studies and real-world applications. Hyperspectral imaging ; Computer science ; Snapshot (computer storage) ; Multispectral image ; Data cube ; Artificial intelligence ; Spectral imaging ; Remote sensing ; Computer vision ; Optics ; Data mining ; Physics ; Database ; Geology Metamaterials and Metasurfaces Applications ; Polydiacetylene-based materials and applications ; Advanced Antenna and Metasurface Technologies