jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Muhammad Sufyan Javed | Sumreen Asim | Tayyaba Najam | Muhammad Khalid | Iftikhar Hussain | Awais Ahmad | Mohammed A. Assiri | Weihua Han
Source: Carbon Energy (article, 2022-11-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent progress in flexible Zn‐ion hybrid supercapacitors: Fundamentals, fabrication designs, and applications Abstract One of the most exciting new developments in energy storage technology is flexible Zn‐ion hybrid supercapacitors (f‐ZIHSCs), which combine the high energy of Zn‐ion batteries with high‐power supercapacitors to satisfy the needs of portable flexible electronics. However, the development of f‐ZHSCs is still in its infancy, and there are numerous barriers to overcome before they can be widely implemented for practical applications. This review gives an up‐to‐date description of recent achievements and underlying concepts in energy storage mechanisms of f‐ZIHSCs and emphasizes the critical role of cathode, anode, and electrolyte materials systems in speeding the prosperity of f‐ZIHSCs. The innovative nanostructured‐based cathode materials for f‐ZIHSCs include carbon (e.g., porous carbon, heteroatom‐doped carbon, biomass‐derived porous carbon, graphene, etc.), metal‐oxides, MXenes, and metal/covalent‐organic frameworks, and other materials (e.g., activated carbon, phosphorene, etc.) are mainly focused. Afterward, the latest developments in flexible anode and electrolyte frameworks and impacts of electrolyte compositions on the electrochemical properties of f‐ZIHSC are elaborated. Subsequently, the advancements based on fabrication designs, including quasi‐solid‐state, micro, fiber‐shaped, and all climate‐changed f‐ZIHSCs, are discussed in detail. Lastly, a summary of current challenges and recommendations for the future progress of advanced f‐ZIHSC are addressed. This review article is anticipated to further understand the viable strategies and achievable approaches for assembling high‐performance f‐ZIHSCs and boost the technical revolutions on cathode, anode, and electrolytes for f‐ZIHSC devices. Supercapacitor ; Nanotechnology ; Anode ; Materials science ; Energy storage ; Electrolyte ; Cathode ; Graphene ; Engineering physics ; Electrochemistry ; Electrical engineering ; Engineering ; Chemistry ; Power (physics) ; Electrode ; Physics Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Advancements in Battery Materials
Authors: Upala Dasmahapatra | Kiran Kumar Chitluri | Soumyadip Das | Prathima Thimma Subramanian | Poornimaa Murali | Isaac Arnold Emerson | K. Ramanathan | Balamurali MM | Kaushik Chanda
Source: Frontiers in Chemistry (article, 2022-09-30)
Field: Chemistry — Organic Chemistry

In-silico molecular modelling, MM/GBSA binding free energy and molecular dynamics simulation study of novel pyrido fused imidazo[4,5-c]quinolines as potential anti-tumor agents With an alarming increase in the number of cancer patients and a variety of tumors, it is high time for intensive investigation on more efficient and potent anti-tumor agents. Though numerous agents have enriched the literature, still there exist challenges, with the availability of different targets and possible cross-reactivity. Herein we have chosen the phosphoinositide 3-kinase (PI3K) as the target of interest and investigated the potential of pyrido fused imidazo[4,5-<i>c</i>]quinoline derivatives to bind strongly to the active site, thereby inhibiting the progression of various types of tumors. The AutoDock, Glide and the Prime-MM/GBSA analysis are used to execute the molecular docking investigation and validation for the designed compounds. The anti-tumor property evaluations were carried out by using PASS algorithm. Based on the GLIDE score, the binding affinity of the designed molecules towards the target PI3K was evaluated. The energetics associated with static interactions revealed <b>1j</b> as the most potential candidate and the dynamic investigations including RMSD, RMSF, Rg, SASA and hydrogen bonding also supported the same through relative stabilization induced through ligand interactions. Subsequently, the binding free energy of the Wortmannin and <b>1j</b> complex calculated using MM-PBSA analysis. Further evaluations with PASS prediction algorithm also supported the above results. The studies reveal that there is evidence for considering appropriate pyrido fused imidazo[4,5-<i>c</i>]quinoline compounds as potential anti-tumor agents. Molecular dynamics ; AutoDock ; Quinoline ; Chemistry ; In silico ; Hydrogen bond ; Docking (animal) ; Molecular mechanics ; Combinatorial chemistry ; Stereochemistry ; Ligand (biochemistry) ; Computational chemistry ; Molecule ; Biochemistry ; Organic chemistry Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and Reactivity of Heterocycles
Authors: Lakshmipathy Muthukrishnan
Source: Colloid & Polymer Science (review, 2022-06-24)
Field: Materials Science — Biomaterials

An overview on electrospinning and its advancement toward hard and soft tissue engineering applications Electrospinning ; Nanofiber ; Nanotechnology ; Tissue engineering ; Materials science ; Computer science ; Polymer ; Engineering ; Biomedical engineering ; Composite material Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Tissue Engineering and Regenerative Medicine
Authors: Wenbing Cai | Ailijiang Abudurusuli | Congwei Xie | Evgenii Tikhonov | Junjie Li | Shilie Pan | Zhihua Yang
Source: Advanced Functional Materials (article, 2022-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Toward the Rational Design of Mid‐Infrared Nonlinear Optical Materials with Targeted Properties via a Multi‐Level Data‐Driven Approach Abstract Design and exploratory synthesis of new mid‐infrared (mid‐IR) nonlinear optical (NLO) materials are urgently needed for modern laser science and technology because the widely used IR NLO crystals still suffer from their inextricable drawbacks. Herein, a multi‐level data‐driven approach to realize fast and efficient structure prediction for the exploration of promising mid‐IR NLO materials is proposed. Techniques based on machine learning, crystal structure prediction, high‐throughput calculation and screening, database building, and experimental verification are tightly combined for creating pathways from chemical compositions, crystal structures to rational synthesis. Through this data‐driven approach, not only are all known structures successfully predicted but also five thermodynamically stable and 50 metastable new selenides in A I B III Se 2 systems ( A I = Li, Na, K, Rb, and Cs; B III = Al and Ga) are found, among which eight outstanding compounds with wide bandgaps ( &gt; 2.70 eV) and large SHG responses ( &gt; 10 pm V −1 ) are suggested. Moreover, the predicted compounds I 2 d ‐LiGaSe 2 and I 4/ mcm ‐KAlSe 2 are successfully obtained experimentally. In particular, LiGaSe 2 exhibits a robust SHG response ( ≈ 2 × AGS) and long IR absorption edge that can cover two atmospheric windows (3–5, 8–12 µ m). Simultaneously, this new research paradigm is also applicative for discovering new materials in other fields. Materials science ; Nonlinear optical ; Infrared ; Metastability ; Rational design ; Absorption (acoustics) ; Nonlinear system ; Nonlinear optics ; Optoelectronics ; Crystal structure ; Nanotechnology ; Laser ; Optics ; Crystallography ; Physics ; Chemistry ; Organic chemistry Crystal Structures and Properties ; Chalcogenide Semiconductor Thin Films ; Inorganic Chemistry and Materials
Authors: Yifei Wang | Jie Su | Zhenhua Lin | Jincheng Zhang | Jingjing Chang | Yue Hao
Source: Journal of Materials Chemistry C (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent progress on the effects of impurities and defects on the properties of Ga<sub>2</sub>O<sub>3</sub> Herein, the effects of impurities ( e.g. , donors, acceptors, co-doping impurities, Al and In, and surface passivators) and defects ( e.g. , intrinsic defects and deep-level defects) on the material properties of Ga 2 O 3 are summarized. Impurity ; Materials science ; Doping ; Crystallography ; Condensed matter physics ; Optoelectronics ; Chemistry Ga2O3 and related materials ; ZnO doping and properties ; Advanced Photocatalysis Techniques
Authors: Lei Xin | Rui Qiao | Xin Chen | Ngoc Hieu Tran | Shengying Pan | Sahar Rabinoviz | Haibo Bian | Xianliang He | Brenton Morse | Baozhen Shan | Ming Li
Source: Nature Communications (article, 2022-06-07)
Field: Chemistry — Spectroscopy

A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics Integrating data-dependent acquisition (DDA) and data-independent acquisition (DIA) approaches can enable highly sensitive mass spectrometry, especially for imunnopeptidomics applications. Here we report a streamlined platform for both DDA and DIA data analysis. The platform integrates deep learning-based solutions of spectral library search, database search, and de novo sequencing under a unified framework, which not only boosts the sensitivity but also accurately controls the specificity of peptide identification. Our platform identifies 5-30% more peptide precursors than other state-of-the-art systems on multiple benchmark datasets. When evaluated on immunopeptidomics datasets, we identify 1.7-4.1 and 1.4-2.2 times more peptides from DDA and DIA data, respectively, than previously reported results. We also discover six T-cell epitopes from SARS-CoV-2 immunopeptidome that might represent potential targets for COVID-19 vaccine development. The platform supports data formats from all major instruments and is implemented with the distributed high-performance computing technology, allowing analysis of tera-scale datasets of thousands of samples for clinical applications. Tera- ; Mass spectrometry ; Computer science ; Scale (ratio) ; Chemistry ; Physics ; Chromatography ; Operating system Advanced Proteomics Techniques and Applications ; Glycosylation and Glycoproteins Research ; Advanced biosensing and bioanalysis techniques
Authors: Wenxuan Quan | Anping Wang | Chao Gao | Chaochan Li
Source: Frontiers in Chemistry (review, 2022-05-24)
Field: Chemistry — Organic Chemistry

Applications of Chinese Camellia oleifera and its By-Products: A Review <i>Camellia oleifera</i> is a woody oil tree species unique to China that has been cultivated and used in China for more than 2,300 years. Most biological research on <i>C. oleifera</i> in recent years has focused on the development of new varieties and breeding. Novel genomic information has been generated for <i>C. oleifera</i>, including a high-quality reference genome at the chromosome level. Camellia seeds are used to process high-quality edible oil; they are also often used in medicine, health foods, and daily chemical products and have shown promise for the treatment and prevention of diseases. <i>C. oleifera</i> by-products, such as camellia seed cake, saponin, and fruit shell are widely used in the daily chemical, dyeing, papermaking, chemical fibre, textile, and pesticide industries. <i>C. oleifera</i> shell can also be used to prepare activated carbon electrodes, which have high electrochemical performance when used as the negative electrode of lithium-ion batteries. <i>C. oleifera</i> is an economically valuable plant with diverse uses, and accelerating the utilization of its by-products will greatly enhance its industrial value. Camellia oleifera ; Camellia ; Biotechnology ; Pulp and paper industry ; Biology ; Traditional medicine ; Toxicology ; Horticulture ; Engineering ; Medicine Edible Oils Quality and Analysis ; Nuts composition and effects ; Tea Polyphenols and Effects
Authors: Annika Stuke | Patrick Rinke | Milica Todorović
Source:  (article, 2022-10-28)
Field: Materials Science — Materials Chemistry

Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization Machine learning methods usually depend on internal parameters-so called hyperparameters-that need to be optimized for best performance. Such optimization poses a burden on machine learning practitioners, requiring expert knowledge, intuition or computationally demanding brute-force parameter searches. We here assess three different hyperparameter selection methods: grid search, random search and an efficient automated optimization technique based on Bayesian optimization (BO). We apply these methods to a machine learning problem based on kernel ridge regression in computational chemistry. Two different descriptors are employed to represent the atomic structure of organic molecules, one of which introduces its own set of hyperparameters to the method. We identify optimal hyperparameter configurations and infer entire prediction error landscapes in hyperparameter space that serve as visual guides for the hyperparameter performance. We further demonstrate that for an increasing number of hyperparameters, BO and random search become significantly more efficient in computational time than an exhaustive grid search, while delivering an equivalent or even better accuracy. Hyperparameter optimization ; Hyperparameter ; Machine learning ; Computer science ; Artificial intelligence ; Bayesian optimization ; Random search ; Kernel (algebra) ; Grid ; Bayesian probability ; Regression ; Algorithm ; Support vector machine ; Mathematics ; Statistics Machine Learning in Materials Science ; Machine Learning and Data Classification ; Machine Learning and Algorithms
Authors: Letian Chen | Yun Tian | Xu Hu | Sai Yao | Zhengyu Lu | Suya Chen | Xu Zhang | Zhen Zhou
Source: Advanced Functional Materials (article, 2022-09-16)
Field: Materials Science — Materials Chemistry

A Universal Machine Learning Framework for Electrocatalyst Innovation: A Case Study of Discovering Alloys for Hydrogen Evolution Reaction Abstract Massive efforts have been made to develop efficient electrocatalysts for green hydrogen production. The introduction of machine learning (ML) has brought new opportunities to the design of electrocatalysts. However, current ML studies have shown that the efficiency and accuracy of this method in electrocatalyst development are severely hindered by two major problems, high computational cost paid for electronic or geometrical structures with high accuracy, and large errors resulted from those easily accessible and relatively simple physical and chemical properties with lower level of accuracy. Here, a universal ML framework is proposed that achieves local structure optimization by using local machine learning potential (MLP) to efficiently obtain accurate structure descriptors, and by combining simple physical properties with graph convolutional neural networks, 43 high‐performance alloys are successfully screened as potential hydrogen evolution reaction electrocatalysts from 2973 candidates. More importantly, part of the best candidates identified from this framework have been verified in experiments, and one of them (AgPd) is systematically investigated by ab initio calculations under realistic electrocatalytic environments to further demonstrate the accuracy. More significantly, the computational efficiency and accuracy can be compromised with this local MLP optimized structural descriptor as the input, and a new paradigm could be established in designing high‐performance electrocatalysts. Electrocatalyst ; Convolutional neural network ; Computer science ; Materials science ; Simple (philosophy) ; Artificial neural network ; Ab initio ; Deep learning ; Artificial intelligence ; Electrochemistry ; Electrode ; Chemistry ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: Suwarno Suwarno | Ghazy Dicky | Abdillah Suyuthi | Mohammad Khoirul Effendi | Witantyo Witantyo | Lukman Noerochim | M. Ismail
Source: International Journal of Hydrogen Energy (article, 2022-03-01)
Field: Materials Science — Materials Chemistry

Machine learning analysis of alloying element effects on hydrogen storage properties of AB2 metal hydrides Hydrogen storage ; Laves phase ; Hydrogen ; Hydride ; Materials science ; Alloy ; Titanium alloy ; Titanium ; Metal ; Enthalpy ; Zirconium ; Thermodynamics ; Metallurgy ; Intermetallic ; Chemistry ; Physics Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Nuclear Materials and Properties
Authors: Xu Zhang | Yun Tian | Letian Chen | Xu Hu | Zhen Zhou
Source: The Journal of Physical Chemistry Letters (review, 2022-08-18)
Field: Materials Science — Materials Chemistry

Machine Learning: A New Paradigm in Computational Electrocatalysis Designing and screening novel electrocatalysts, understanding electrocatalytic mechanisms at an atomic level, and uncovering scientific insights lie at the center of the development of electrocatalysis. Despite certain success in experiments and computations, it is still difficult to achieve the above objectives due to the complexity of electrocatalytic systems and the vastness of the chemical space for candidate electrocatalysts. With the advantage of machine learning (ML) and increasing interest in electrocatalysis for energy conversion and storage, data-driven scientific research motivated by artificial intelligence (AI) has provided new opportunities to discover promising electrocatalysts, investigate dynamic reaction processes, and extract knowledge from huge data. In this Perspective, we summarize the recent applications of ML in electrocatalysis, including the screening of electrocatalysts and simulation of electrocatalytic processes. Furthermore, interpretable machine learning methods for electrocatalysis are discussed to accelerate knowledge generation. Finally, the blueprint of machine learning is envisaged for future development of electrocatalysis. Electrocatalyst ; Computer science ; Blueprint ; Nanotechnology ; Artificial intelligence ; Materials science ; Chemistry ; Electrochemistry ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Computational Drug Discovery Methods
Authors: Lihua Shen | Muhammad Arif Khan | Xianyong Wu | Jian Cai | Tian Lu | Tai Ning | Zhanmin Liu | Wencong Lu | Daixin Ye | Hongbin Zhao | Jiujun Zhang
Source: Biosensors and Bioelectronics (article, 2022-02-21)
Field: Materials Science — Materials Chemistry

Fe–N–C single-atom nanozymes based sensor array for dual signal selective determination of antioxidants Ascorbic acid ; Detection limit ; Chemistry ; Analyte ; Combinatorial chemistry ; Analytical Chemistry (journal) ; Chromatography Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Jialong Wu | Yanyan Yu | Gaoxing Su
Source: Nanomaterials (review, 2022-03-01)
Field: Materials Science — Materials Chemistry

Safety Assessment of 2D MXenes: In Vitro and In Vivo MXenes, representing a new class of two-dimensional nanomaterial, have attracted intense interest in a variety of fields as supercapacitors, catalysts, and sensors, and in biomedicine. The assessment of the safety of MXenes and related materials in biological systems is thus an issue that requires significant attention. In this review, the toxic effects of MXenes and their derivatives are summarized through the discussion of current research into their behaviors in mammalian cells, animals and plants. Numerous studies have shown that MXenes have generally low cytotoxicity and good biocompatibility. However, a few studies have indicated that MXenes are toxic to stem cells and embryos. These in vitro and in vivo toxic effects are strongly associated with the dose of material, the cell type, the mode of exposure, and the specific type of MXene. In addition, surface modifications alter the toxic effects of MXenes. The stability of MXenes must be considered during toxicity evaluation, as degradation can lead to potentially toxic byproducts. Although research concerning the toxicity of MXenes is limited, this review provides an overview of the current understanding of interactions of MXenes with biological systems and suggests future research directions. MXenes ; Biomedicine ; Biology ; Biochemical engineering ; Nanotechnology ; Materials science ; Bioinformatics ; Engineering MXene and MAX Phase Materials ; Graphene and Nanomaterials Applications ; Advanced Photocatalysis Techniques
Authors: Mehri Razavi | Alexandre Barras | Madjid Ifires | Abir Swaidan | Maryam Khoshkam | Sabine Szunerits | Mohsen Kompany‐Zareh | Rabah Boukherroub
Source: Journal of Colloid and Interface Science (article, 2022-01-10)
Field: Materials Science — Materials Chemistry

Colorimetric assay for the detection of dopamine using bismuth ferrite oxide (Bi2Fe4O9) nanoparticles as an efficient peroxidase-mimic nanozyme Fourier transform infrared spectroscopy ; Chemistry ; Detection limit ; X-ray photoelectron spectroscopy ; Zinc ferrite ; Nuclear chemistry ; Absorbance ; Analytical Chemistry (journal) ; Spectroscopy ; Nanoparticle ; Materials science ; Chemical engineering ; Chromatography ; Nanotechnology Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Nanocluster Synthesis and Applications
Authors: Ruichao Zhu | Jiafu Wang | Tianshuo Qiu | Yajuan Han | Xinmin Fu | Yuzhi Shi | Xingsi Liu | Tonghao Liu | Zhongtao Zhang | Zuntian Chu | Cheng‐Wei Qiu | Shaobo Qu
Source: eLight (article, 2022-06-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Remotely mind-controlled metasurface via brainwaves Abstract The power of controlling objects with mind has captivated a popular fascination to human beings. One possible path is to employ brain signal collecting technologies together with emerging programmable metasurfaces (PM), whose functions or operating modes can be switched or customized via on-site programming or pre-defined software. Nevertheless, most of existing PMs are wire-connected to users, manually-controlled and not real-time. Here, we propose the concept of remotely mind-controlled metasurface (RMCM) via brainwaves. Rather than DC voltage from power supply or AC voltages from signal generators, the metasurface is controlled by brainwaves collected in real time and transmitted wirelessly from the user. As an example, we demonstrated a RMCM whose scattering pattern can be altered dynamically according to the user’s brain waves via Bluetooth. The attention intensity information is extracted as the control signal and a mapping between attention intensity and scattering pattern of the metasurface is established. With such a framework, we experimentally demonstrated and verified a prototype of such metasurface system which can be remotely controlled by the user to modify its scattering pattern. This work paves a new way to intelligent metasurfaces and may find applications in health monitoring, 5G/6G communications, smart sensors, etc. Bluetooth ; SIGNAL (programming language) ; Computer science ; Power (physics) ; Wireless ; Voltage ; Scattering ; Computer hardware ; Electrical engineering ; Telecommunications ; Physics ; Optics ; Engineering Metamaterials and Metasurfaces Applications ; Opportunistic and Delay-Tolerant Networks ; Energy Harvesting in Wireless Networks
Authors: S.S. Fatullayeva | D.B. Тagiyev | N.А. Zeynalov | Samira Mammadova | Elmira Aliyeva
Source: Journal of Polymer Research (article, 2022-06-06)
Field: Materials Science — Biomaterials

Recent advances of chitosan-based polymers in biomedical applications and environmental protection Chitosan ; Biocompatibility ; Tissue engineering ; Drug delivery ; Materials science ; Nanotechnology ; Biochemical engineering ; Biodegradation ; Polymer ; Environmental safety ; Biomedical engineering ; Biotechnology ; Human health ; Chemistry ; Organic chemistry ; Biology ; Medicine ; Composite material ; Engineering Nanocomposite Films for Food Packaging ; Antimicrobial agents and applications ; Advanced Drug Delivery Systems