jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Balakrishnan Subeshan | Asonganyi Atayo | Eylem Asmatulu
Source: Journal of Materials Science (review, 2024-07-30)
Field: Materials Science — Biomaterials

Machine learning applications for electrospun nanofibers: a review Abstract Electrospun nanofibers have gained prominence as a versatile material, with applications spanning tissue engineering, drug delivery, energy storage, filtration, sensors, and textiles. Their unique properties, including high surface area, permeability, tunable porosity, low basic weight, and mechanical flexibility, alongside adjustable fiber diameter distribution and modifiable wettability, make them highly desirable across diverse fields. However, optimizing the properties of electrospun nanofibers to meet specific requirements has proven to be a challenging endeavor. The electrospinning process is inherently complex and influenced by numerous variables, including applied voltage, polymer concentration, solution concentration, solution flow rate, molecular weight of the polymer, and needle-to-collector distance. This complexity often results in variations in the properties of electrospun nanofibers, making it difficult to achieve the desired characteristics consistently. Traditional trial-and-error approaches to parameter optimization have been time-consuming and costly, and they lack the precision necessary to address these challenges effectively. In recent years, the convergence of materials science and machine learning (ML) has offered a transformative approach to electrospinning. By harnessing the power of ML algorithms, scientists and researchers can navigate the intricate parameter space of electrospinning more efficiently, bypassing the need for extensive trial-and-error experimentation. This transformative approach holds the potential to significantly reduce the time and resources invested in producing electrospun nanofibers with specific properties for a wide range of applications. Herein, we provide an in-depth analysis of current work that leverages ML to obtain the target properties of electrospun nanofibers. By examining current work, we explore the intersection of electrospinning and ML, shedding light on advancements, challenges, and future directions. This comprehensive analysis not only highlights the potential of ML in optimizing electrospinning processes but also provides valuable insights into the evolving landscape, paving the way for innovative and precisely engineered electrospun nanofibers to meet the target properties for various applications. Graphical abstract Electrospinning ; Nanofiber ; Materials science ; Nanotechnology ; Computer science ; Polymer ; Process engineering ; Composite material Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Nan Zhang | Feng Gao | Ride Wang | Zhonglei Shen | Donghai Han | Yuqing Cui | Liuyang Zhang | Chao Chang | Cheng‐Wei Qiu | Xuefeng Chen
Source: Advanced Materials (article, 2024-10-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep‐Learning Empowered Customized Chiral Metasurface for Calibration‐Free Biosensing As a 2D metamaterial, metasurfaces offer an unprecedented avenue to facilitate light-matter interactions. The current "one-by-one design" method is hindered by time-consuming, repeated testing within a confined space. However, intelligent design strategies for metasurfaces, limited by data-driven properties, have rarely been explored. To address this gap, a data iterative strategy based on deep learning, coupled with a global optimization network is proposed, to achieve the customized design of chiral metasurfaces. This methodology is applied to precisely identify different chiral molecules in a label-free manner. Fundamentally different from the traditional approach of collecting data purely through simulation, the proposed data generation strategy encompasses the entire design space, which is inaccessible by conventional methods. The dataset quality is significantly improved, with a 21-fold increase in the number of chiral structures exhibiting the desired circular dichroism (CD) response (>0.6). The method's efficacy is validated by a monolayer structure that is easily prepared, demonstrating advanced sensing abilities for enantiomer-specific analysis of bio-samples. These results demonstrate the superior capability of data-driven schemes in photonic design and the potential of chiral metasurface-based platforms for calibration-free biosensing applications. The proposed approach will accelerate the development of complex systems for rapid molecular detection, spectroscopic imaging, and other applications. Metamaterial ; Materials science ; Computer science ; Nanotechnology ; Photonics ; Calibration ; Biosensor ; Optoelectronics ; Physics Metamaterials and Metasurfaces Applications ; Animal Vocal Communication and Behavior ; Plasmonic and Surface Plasmon Research
Authors: Luis Barroso-Luque | Muhammed Shuaibi | Xiang Fu | Brandon M. Wood | Misko Dzamba | Meng Gao | Ammar Rizvi | C. Lawrence Zitnick | Zachary W. Ulissi
Source: arXiv (Cornell University) (preprint, 2024-10-16)
Field: Materials Science — Materials Chemistry

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models The ability to discover new materials with desirable properties is critical for numerous applications from helping mitigate climate change to advances in next generation computing hardware. AI has the potential to accelerate materials discovery and design by more effectively exploring the chemical space compared to other computational methods or by trial-and-error. While substantial progress has been made on AI for materials data, benchmarks, and models, a barrier that has emerged is the lack of publicly available training data and open pre-trained models. To address this, we present a Meta FAIR release of the Open Materials 2024 (OMat24) large-scale open dataset and an accompanying set of pre-trained models. OMat24 contains over 110 million density functional theory (DFT) calculations focused on structural and compositional diversity. Our EquiformerV2 models achieve state-of-the-art performance on the Matbench Discovery leaderboard and are capable of predicting ground-state stability and formation energies to an F1 score above 0.9 and an accuracy of 20 meV/atom, respectively. We explore the impact of model size, auxiliary denoising objectives, and fine-tuning on performance across a range of datasets including OMat24, MPtraj, and Alexandria. The open release of the OMat24 dataset and models enables the research community to build upon our efforts and drive further advancements in AI-assisted materials science. Materials science ; Environmental science Machine Learning in Materials Science
Authors: Chaochen Shao | Xiaohan Yu | Yujin Ji | Jie Xu | Yuchen Yan | Yongpan Hu | Youyong Li | Wei Huang | Yanguang Li
Source: Nature Communications (article, 2024-09-13)
Field: Materials Science — Materials Chemistry

Perfluoroalkyl-modified covalent organic frameworks for continuous photocatalytic hydrogen peroxide synthesis and extraction in a biphasic fluid system H<sub>2</sub>O<sub>2</sub> photosynthesis represents an appealing approach for sustainable and decentralized H<sub>2</sub>O<sub>2</sub> production. Unfortunately, current reactions are mostly carried out in laboratory-scale single-phase batch reactors, which have a limited H<sub>2</sub>O<sub>2</sub> production rate (<100 μmol h<sup>-1</sup>) and cannot operate in an uninterrupted manner. Herein, we propose continuous H<sub>2</sub>O<sub>2</sub> photosynthesis and extraction in a biphasic fluid system. A superhydrophobic covalent organic framework photocatalyst with perfluoroalkyl functionalization is rationally designed and prepared via the Schiff-base reaction. When applied in a home-built biphasic fluid photo-reactor, the superhydrophobicity of our photocatalyst allows its selective dispersion in the oil phase, while formed H<sub>2</sub>O<sub>2</sub> is spontaneously extracted to the water phase. Through optimizing reaction parameters, we achieve continuous H<sub>2</sub>O<sub>2</sub> photosynthesis and extraction with an unprecedented production rate of up to 968 μmol h<sup>-1</sup> and tunable H<sub>2</sub>O<sub>2</sub> concentrations from 2.2 to 38.1 mM. As-obtained H<sub>2</sub>O<sub>2</sub> solution could satisfactorily meet the general demands of household disinfection and wastewater treatments. Hydrogen peroxide ; Photocatalysis ; Covalent bond ; Extraction (chemistry) ; Chemistry ; Chemical engineering ; Combinatorial chemistry ; Organic chemistry ; Catalysis ; Engineering Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Seema Lal | Parul Singh | Anchal Singhal | Sanjay Kumar | Ajay Pratap Singh Gahlot | N. Indra Gandhi | Pratibha Kumari
Source: RSC Advances (review, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Advances in metal–organic frameworks for water remediation applications Rapid industrialization and agricultural development have resulted in the accumulation of a variety of harmful contaminants in water resources. Thus, various approaches such as adsorption, photocatalytic degradation and methods for sensing water contaminants have been developed to solve the problem of water pollution. Metal-organic frameworks (MOFs) are a class of coordination networks comprising organic-inorganic hybrid porous materials having organic ligands attached to inorganic metal ions/clusters <i>via</i> coordination bonds. MOFs represent an emerging class of materials for application in water remediation owing to their versatile structural and chemical characteristics, such as well-ordered porous structures, large specific surface area, structural diversity, and tunable sites. The present review is focused on recent advances in various MOFs for application in water remediation <i>via</i> the adsorption and photocatalytic degradation of water contaminants. The sensing of water pollutants using MOFs <i>via</i> different approaches, such as luminescence, electrochemical, colorimetric, and surface-enhanced Raman spectroscopic techniques, is also discussed. The high porosity and chemical tunability of MOFs are the main driving forces for their widespread applications, which have huge potential for their commercial use. Environmental remediation ; Metal-organic framework ; Environmental science ; Groundwater remediation ; Environmental chemistry ; Chemistry ; Contamination ; Adsorption ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Advanced Photocatalysis Techniques
Authors: Yaru Gong | Wei Dou | Bochen Lu | Xuemei Zhang | Zhu He | Pan Ying | Qingtang Zhang | Yuqi Liu | Yanan Li | Xinqi Huang | Muhammad Faisal Iqbal | Shihua Zhang | Di Li | Yongsheng Zhang | Haijun Wu | Guodong Tang
Source: Nature Communications (article, 2024-05-18)
Field: Materials Science — Materials Chemistry

Divacancy and resonance level enables high thermoelectric performance in n-type SnSe polycrystals N-type polycrystalline SnSe is considered as a highly promising candidates for thermoelectric applications due to facile processing, machinability, and scalability. However, existing efforts do not enable a peak ZT value exceeding 2.0 in n-type polycrystalline SnSe. Here, we realized a significant ZT enhancement by leveraging the synergistic effects of divacancy defect and introducing resonance level into the conduction band. The resonance level and increased density of states resulting from tungsten boost the Seebeck coefficient. The combination of the enhanced electrical conductivity (achieved by increasing carrier concentration through WCl<sub>6</sub> doping and Se vacancies) and large Seebeck coefficient lead to a high power factor. Microstructural analyses reveal that the co-existence of divacancy defects (Se vacancies and Sn vacancies) and endotaxial W- and Cl-rich nanoprecipitates scatter phonons effectively, resulting in ultralow lattice conductivity. Ultimately, a record-high peak ZT of 2.2 at 773 K is achieved in n-type SnSe<sub>0.92</sub> + 0.03WCl<sub>6</sub>. Seebeck coefficient ; Materials science ; Thermoelectric effect ; Thermoelectric materials ; Crystallite ; Condensed matter physics ; Doping ; Electrical resistivity and conductivity ; Phonon ; Optoelectronics ; Thermal conductivity ; Metallurgy ; Composite material ; Thermodynamics ; Electrical engineering Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; 2D Materials and Applications
Authors: Yuming Su | Xue Wang | Yubo Ye | Yibo Xie | Yujing Xu | Yibin Jiang | Cheng Wang
Source: Chemical Science (review, 2024-01-01)
Field: Materials Science — Materials Chemistry

Automation and machine learning augmented by large language models in a catalysis study Recent advancements in artificial intelligence and automation are transforming catalyst discovery and design from traditional trial-and-error manual mode into intelligent, high-throughput digital methodologies. This transformation is driven by four key components, including high-throughput information extraction, automated robotic experimentation, real-time feedback for iterative optimization, and interpretable machine learning for generating new knowledge. These innovations have given rise to the development of self-driving labs and significantly accelerated materials research. Over the past two years, the emergence of large language models (LLMs) has added a new dimension to this field, providing unprecedented flexibility in information integration, decision-making, and interacting with human researchers. This review explores how LLMs are reshaping catalyst design, heralding a revolutionary change in the fields. Automation ; Computer science ; Artificial intelligence ; Natural language processing ; Software engineering ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Topic Modeling ; Catalysis and Oxidation Reactions
Authors: Tianle Chen | Zhenqian Pang | Shuaiming He | Yang Li | Snehi Shrestha | Joshua M. Little | Haochen Yang | Tsai‐Chun Chung | Jiayue Sun | Hayden Christopher Whitley | I‐Chi Lee | Taylor J. Woehl | Teng Li | Liangbing Hu | Po‐Yen Chen
Source: Nature Nanotechnology (article, 2024-03-18)
Field: Materials Science — Materials Chemistry

Machine intelligence-accelerated discovery of all-natural plastic substitutes One possible solution against the accumulation of petrochemical plastics in natural environments is to develop biodegradable plastic substitutes using natural components. However, discovering all-natural alternatives that meet specific properties, such as optical transparency, fire retardancy and mechanical resilience, which have made petrochemical plastics successful, remains challenging. Current approaches still rely on iterative optimization experiments. Here we show an integrated workflow that combines robotics and machine learning to accelerate the discovery of all-natural plastic substitutes with programmable optical, thermal and mechanical properties. First, an automated pipetting robot is commanded to prepare 286 nanocomposite films with various properties to train a support-vector machine classifier. Next, through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated stagewise, establishing an artificial neural network prediction model. We demonstrate that the prediction model can conduct a two-way design task: (1) predicting the physicochemical properties of an all-natural nanocomposite from its composition and (2) automating the inverse design of biodegradable plastic substitutes that fulfils various user-specific requirements. By harnessing the model's prediction capabilities, we prepare several all-natural substitutes, that could replace non-biodegradable counterparts as exhibiting analogous properties. Our methodology integrates robot-assisted experiments, machine intelligence and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes starting from building blocks taken from the generally-recognized-as-safe database. Computer science ; Artificial intelligence ; Robot ; Robotics ; Workflow ; Machine learning ; Database Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; Advanced Polymer Synthesis and Characterization
Authors: Ditimoni Dutta | Nandan Sit
Source: Food biomacromolecules. (review, 2024-12-01)
Field: Materials Science — Biomaterials

A comprehensive review on types and properties of biopolymers as sustainable bio‐based alternatives for packaging Abstract Biopolymers, derived from renewable resources such as plants, animals, and microorganisms, are emerging as sustainable alternatives to traditional petroleum‐based polymers. The review examines the key characteristics of biopolymers, including their biodegradability, biocompatibility, and potential for carbon neutrality. These characteristics are crucial for determining their suitability for different packaging applications and their potential to reduce environmental pollution. While acknowledging the promise of biopolymers, the review also addresses challenges such as production costs, scalability issues, and performance limitations. Strategies to enhance biopolymer performance, including plasticization, blending, and nanomaterial reinforcement, are discussed. Interestingly, the review highlights the emerging field of active and intelligent packaging systems, which incorporate antimicrobial agents and sensors to extend shelf life and monitor food quality in real‐time. The review emphasizes the importance of life cycle assessments in evaluating the overall environmental impact of biopolymer‐based packaging compared to conventional alternatives. In conclusion, this review provides an overview of the current state of biopolymer research and identifies areas for future investigation. By synthesizing current knowledge, identifying challenges, and highlighting opportunities, this review contributes to ongoing efforts to create a more sustainable and circular packaging industry. Biochemical engineering ; Risk analysis (engineering) ; Business ; Engineering biodegradable polymer synthesis and properties ; Nanocomposite Films for Food Packaging ; Microplastics and Plastic Pollution
Authors: Lifeng Wang | Haiyan Wang | Chunxiao Wu | Jiaxin Bai | Tiancheng He | Yan Li | Huhu Cheng | Liangti Qu
Source: Nature Communications (article, 2024-06-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Moisture-enabled self-charging and voltage stabilizing supercapacitor Supercapacitor is highly demanded in emerging portable electronics, however, which faces frequent charging and inevitable rapid self-discharging of huge inconvenient. Here, we present a flexible moisture-powered supercapacitor (mp-SC) that capable of spontaneously moisture-enabled self-charging and persistently voltage stabilizing. Based on the synergy effect of moisture-induced ions diffusion of inner polyelectrolyte-based moist-electric generator and charges storage ability of inner graphene electrochemical capacitor, this mp-SC demonstrates the self-charged high areal capacitance of 138.3 mF cm<sup>-2</sup> and ~96.6% voltage maintenance for 120 h. In addition, a large-scale flexible device of 72 mp-SC units connected in series achieves a self-charged 60 V voltage in air, efficiently powering various commercial electronics in practical applications. This work will provide insight into the design self-powered and ultra-long term stable supercapacitors and other energy storage devices. Supercapacitor ; Moisture ; Voltage ; Materials science ; Environmental science ; Nanotechnology ; Chemistry ; Electrode ; Capacitance ; Electrical engineering ; Composite material ; Engineering Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials ; Advanced Battery Technologies Research
Authors: Zhiyuan Zhang | Hui Liu | Deng‐Guang Yu | S. W. Annie Bligh
Source: Biomolecules (review, 2024-07-03)
Field: Materials Science — Biomaterials

Alginate-Based Electrospun Nanofibers and the Enabled Drug Controlled Release Profiles: A Review Alginate is a natural polymer with good biocompatible properties and is a potential polymeric material for the sustainable development and replacement of petroleum derivatives. However, the non-spinnability of pure alginate solutions has hindered the expansion of alginate applications. With the continuous development of electrospinning technology, synthetic polymers, such as PEO and PVA, are used as co-spinning agents to increase the spinnability of alginate. Moreover, the coaxial, parallel Janus, tertiary and other diverse and novel electrospun fiber structures prepared by multi-fluid electrospinning have found a new breakthrough for the problem of poor spinning of natural polymers. Meanwhile, the diverse electrospun fiber structures effectively achieve multiple release modes of drugs. The powerful combination of alginate and electrostatic spinning is widely used in many biomedical fields, such as tissue engineering, regenerative engineering, bioscaffolds, and drug delivery, and the research fever continues to climb. This is particularly true for the controlled delivery aspect of drugs. This review provides a brief overview of alginate, introduces new advances in electrostatic spinning, and highlights the research progress of alginate-based electrospun nanofibers in achieving various controlled release modes, such as pulsed release, sustained release, biphasic release, responsive release, and targeted release. Electrospinning ; Spinning ; Nanofiber ; Materials science ; Drug delivery ; Nanotechnology ; Polymer ; Tissue engineering ; Biocompatible material ; Controlled release ; Biomedical engineering ; Composite material Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Tissue Engineering and Regenerative Medicine