jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shi-Yu Wang | Yuanqi Du | Xiaojie Guo | Bo Pan | Zhaohui Qin | Liang Zhao
Source: ACM Computing Surveys (review, 2024-03-09)
Field: Materials Science — Materials Chemistry

Controllable Data Generation by Deep Learning: A Review Designing and generating new data under targeted properties has been attracting various critical applications such as molecule design, image editing and speech synthesis. Traditional hand-crafted approaches heavily rely on expertise experience and intensive human efforts, yet still suffer from the insufficiency of scientific knowledge and low throughput to support effective and efficient data generation. Recently, the advancement of deep learning has created the opportunity for expressive methods to learn the underlying representation and properties of data. Such capability provides new ways of determining the mutual relationship between the structural patterns and functional properties of the data and leveraging such relationships to generate structural data, given the desired properties. This article is a systematic review that explains this promising research area, commonly known as controllable deep data generation. First, the article raises the potential challenges and provides preliminaries. Then the article formally defines controllable deep data generation, proposes a taxonomy on various techniques and summarizes the evaluation metrics in this specific domain. After that, the article introduces exciting applications of controllable deep data generation, experimentally analyzes and compares existing works. Finally, this article highlights the promising future directions of controllable deep data generation and identifies five potential challenges. Computer science ; Deep learning ; Data science ; Artificial intelligence ; Representation (politics) ; Domain (mathematical analysis) ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Lucas T. Thobejane | Bonginkosi Thango
Source: Applied Sciences (article, 2024-07-12)
Field: Materials Science — Materials Chemistry

Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review Power transformers, like other High-Voltage (HV) electrical equipment, experience aging and insulation degradation due to chemical, mechanical and electrical forces during their operation. Partial discharges (PD) are among the most predominant insulation breakdown mechanisms. Monitoring partial discharges has proven to provide valuable information on the state of the insulation systems of power transformer, allowing transformer operators to make calculated decisions for maintenance, major interventions and plan for replacement. This systematic literature review aims to systematically examine the use of machine learning techniques in classifying PD in transformers to present a complete indicator of the available literature as well as potential literature gaps which will allow for future research in the field. The systematic review surveyed a total of 81 research literatures published from 2010 to 2023 that fulfilled a specific methodology which was developed as part of this study. The results revealed that supervised learning has been the most widely used Artificial Intelligence (AI) algorithm, primarily in the form of Support Vector Machine (SVM). The collected research indicated 20 countries represented in the publications, with China carrying out 32% of the research, followed by India with 10%. Regarding PD, the survey revealed that most researchers tend to investigate numerous types of PD and compare them to one another. Furthermore, the use of artificial PD defect models to simulate the occurrence of PD is widely used versus the use of actual power transformers. Most of the literature tends to not specify the physical characteristics of PD, such as the magnitude of PD, PD inception voltage and PD extinction voltage. Partial discharge ; Transformer ; Reliability engineering ; Computer science ; Support vector machine ; Engineering ; Electrical engineering ; Voltage ; Machine learning High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Souvik Pore | Arkaprava Banerjee | Kunal Roy
Source: Molecular Informatics (article, 2024-02-19)
Field: Materials Science — Materials Chemistry

Application of machine learning‐based read‐across structure‐property relationship (RASPR) as a new tool for predictive modelling: Prediction of power conversion efficiency (PCE) for selected classes of organic dyes in dye‐sensitized solar cells (DSSCs) The application of various in-silico-based approaches for the prediction of various properties of materials has been an effective alternative to experimental methods. Recently, the concepts of Quantitative structure-property relationship (QSPR) and read-across (RA) methods were merged to develop a new emerging chemoinformatic tool: read-across structure-property relationship (RASPR). The RASPR method can be applicable to both large and small datasets as it uses various similarity and error-based measures. It has also been observed that RASPR models tend to have an increased external predictivity compared to the corresponding QSPR models. In this study, we have modeled the power conversion efficiency (PCE) of organic dyes used in dye-sensitized solar cells (DSSCs) by using the quantitative RASPR (q-RASPR) method. We have used relatively larger classes of organic dyes-Phenothiazines (n=207), Porphyrins (n=281), and Triphenylamines (n=229) for the modelling purpose. We have divided each of the datasets into training and test sets in 3 different combinations, and with the training sets we have developed three different QSPR models with structural and physicochemical descriptors and validated them with the corresponding test sets. These corresponding modeled descriptors were used to calculate the RASPR descriptors using a Java-based tool RASAR Descriptor Calculator v2.0 (https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home), and then data fusion was performed by pooling the previously selected structural and physicochemical descriptors with the calculated RASPR descriptors. Further feature selection algorithm was employed to develop the final RASPR PLS models. Here, we also developed different machine learning (ML) models with the descriptors selected in the QSPR PLS and RASPR PLS models, and it was found that models with RASPR descriptors superseded in external predictivity the models with only structural and physicochemical descriptors: RMSEP reduced for phenothiazines from 1.16-1.25 to 1.07-1.18, for porphyrins from 1.60-1.79 to 1.45-1.53, for triphenylamines from 1.27-1.54 to 1.20-1.47. Quantitative structure–activity relationship ; Computer science ; Pooling ; Molecular descriptor ; Artificial intelligence ; Machine learning ; Data mining ; Cheminformatics ; Similarity (geometry) ; In silico ; Feature selection ; Biological system ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Canglong Xing | Ming Luo | Qiuhui Sheng | Zhichao Zhu | Dan Yu | Jian Feng Huang | Dan He | Meng Zhang | Wei Fan | Dongzhen Chen
Source: ACS Applied Materials & Interfaces (article, 2024-09-13)
Field: Materials Science — Biomaterials

Silk Fabric Functionalized by Nanosilver Enabling the Wearable Sensing for Biomechanics and Biomolecules Integrating biomechanical and biomolecular sensing mechanisms into wearable devices is a formidable challenge and key to acquiring personalized health management. To address this, we have developed an innovative multifunctional sensor enabled by plasma functionalized silk fabric, which possesses multimodal sensing capabilities for biomechanics and biomolecules. A seed-mediated <i>in situ</i> growth method was employed to coat silver nanoparticles (AgNPs) onto silk fibers, resulting in silk fibers functionalized with AgNPs (SFs@Ag) that exhibit both piezoresistive response and localized surface plasmon resonance effects. The SFs@Ag membrane enables accurate detection of mechanical pressure and specific biomolecules during wearable sensing, offering a versatile solution for comprehensive personalized health monitoring. Additionally, a machine learning algorithm has been established to specifically recognize muscle strain signals, potentially extending to the diagnosis and monitoring of neuromuscular disorders such as amyotrophic lateral sclerosis (ALS). Unlike electromyography, which detects large muscles in clinical medicine, sensing data for tiny muscles enhance our understanding of muscle coordination using the SFs@Ag sensor. This detection model provides feasibility for the early detection and prevention of neuromuscular diseases. Beyond muscle stress and strain sensing, biomolecular detection is a critical addition to achieving effective health management. In this study, we developed highly sensitive surface-enhanced Raman scattering (SERS) detection for wearable health monitoring. Finite-difference time-domain numerical simulations ware utilized to analyze the efficacy of the SFs@Ag sensor for wearable SERS sensing of biomolecules. Based on the specific SERS spectra, automatic extraction of signals of sweat molecules was also achieved. In summary, the SFs@Ag sensor bridges the gap between biomechanical and biomolecular sensing in wearable applications, providing significant value for personalized health management. Materials science ; Wearable computer ; Biomolecule ; Nanotechnology ; Wearable technology ; Textile ; SILK ; Composite material ; Computer science ; Embedded system Silk-based biomaterials and applications ; Advanced Sensor and Energy Harvesting Materials ; Polydiacetylene-based materials and applications
Authors: Eugene Sefa Appiah | Perseverance Dzikunu | Samuel Olukayode Akinwamide | Eric Fangnon | Kwadwo Mensah‐Darkwa | Anthony Andrews | Frank Ofori Agyemang | Martinson Addo Nartey | Katlego Makgopa | Sven Bossuyt
Source: Ionics (review, 2024-10-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A review on progress and prospects of diatomaceous earth as a bio-template material for electrochemical energy storage: synthesis, characterization, and applications Abstract This comprehensive review explores the remarkable progress and prospects of diatomaceous earth (DE) as a bio-template material for synthesizing electrode materials tailored explicitly for supercapacitor and battery applications. The unique structures within DE, including its mesoporous nature and high surface area, have positioned it as a pivotal material in energy storage. The mesoporous framework of DE, often defined by pores with diameters between 2 and 50 nm, provides a substantial surface area, a fundamental element for charge storage, and transfer in electrochemical energy conversion and storage. Its bio-templating capabilities have ushered in the creation of highly efficient electrode materials. Moreover, the role of DE in enhancing ion accessibility has made it an excellent choice for high-power applications. As we gaze toward the future, the prospects of DE as a bio-template material for supercapacitor and battery electrode material appear exceptionally promising. Customized material synthesis, scalability challenges, multidisciplinary collaborations, and sustainable initiatives are emerging as key areas of interest. The natural abundance and eco-friendly attributes of DE align with the growing emphasis on sustainability in energy solutions, and its contribution to electrode material synthesis for supercapacitors and batteries presents an exciting avenue to evolve energy storage technologies. Its intricate structures and bio-templating capabilities offer a compelling path for advancing sustainable, high-performance energy storage solutions, marking a significant step toward a greener and more efficient future. Graphical Abstract Characterization (materials science) ; Nanotechnology ; Electrochemistry ; Materials science ; Energy storage ; Electrochemical energy storage ; Biochemical engineering ; Chemistry ; Engineering ; Supercapacitor ; Physics ; Electrode Supercapacitor Materials and Fabrication ; Microbial Fuel Cells and Bioremediation ; Extraction and Separation Processes
Authors: Chiara Bisio | Jocelyne Brendlé | Sébastien Cahen | Yongjun Feng | Seong‐Ju Hwang | Klára Melánová | Morena Nocchetti | Dermot O’Hare | Pierre Rabu | Fabrice Leroux
Source: Dalton Transactions (review, 2024-01-01)
Field: Materials Science — Materials Chemistry

Recent advances and perspectives on intercalation layered compounds part 1: design and applications in the field of energy Herein, initially, we present a general overview of the global financial support for chemistry devoted to materials science, specifically intercalation layered compounds (ILCs). Subsequently, the strategies to synthesise these host structures and the corresponding guest-host hybrid assemblies are exemplified on the basis of some families of materials, including pillared clays (PILCs), porous clay heterostructures (PCHs), zirconium phosphate (ZrP), layered double hydroxides (LDHs), graphite intercalation compounds (GICs), graphene-based materials, and MXenes. Additionally, a non-exhaustive survey on their possible application in the field of energy through electrochemical storage, mostly as electrode materials but also as electrolyte additives, is presented, including lithium technologies based on lithium ion batteries (LIBs), and beyond LiBs with a focus on possible alternatives such XIBs (X = Na (NIB), K (KIB), Al (AIB), Zn (ZIB), and Cl (CIB)), reversible Mg batteries (RMBs), dual-ion batteries (DIBs), Zn-air and Zn-sulphur batteries and supercapacitors as well as their relevance in other fields related to (opto)electronics. This selective panorama should help readers better understand the reason why ILCs are expected to meet the challenge of tomorrow as electrode materials. Intercalation (chemistry) ; Field (mathematics) ; Energy (signal processing) ; Nanotechnology ; Materials science ; Engineering physics ; Chemistry ; Engineering ; Physics ; Inorganic chemistry ; Mathematics MXene and MAX Phase Materials ; Advancements in Battery Materials ; Inorganic Chemistry and Materials
Authors: Xiong Zhou | Qian Shen | Yongfeng Wang | Yafei Dai | Xiaopeng Cheng | Kai Wu
Source: National Science Review (review, 2024-08-02)
Field: Materials Science — Materials Chemistry

Surface and interfacial sciences for future technologies Physical science has undergone an evolutional transition in research focus from solid bulks to surfaces, culminating in numerous prominent achievements. Currently, it is experiencing a new exploratory phase-interfacial science. Many a technology with a tremendous impact is closely associated with a functional interface which delineates the boundary between disparate materials or phases, evokes complexities that surpass its pristine comprising surfaces, and thereby unveils a plethora of distinctive properties. Such an interface may generate completely new or significantly enhanced properties. These specific properties are closely related to the interfacial states formed at the interfaces. Therefore, establishing a quantitative relationship between the interfacial states and their functionalities has become a key scientific issue in interfacial science. However, interfacial science also faces several challenges such as invisibility in characterization, inaccuracy in calculation, and difficulty in precise construction. To tackle these challenges, people must develop new strategies for precise detection, accurate computation, and meticulous construction of functional interfaces. Such strategies are anticipated to provide a comprehensive toolbox tailored for future interfacial science explorations and thereby lay a solid scientific foundation for several key future technologies. Toolbox ; Nanotechnology ; Interface (matter) ; Invisibility ; Key (lock) ; Characterization (materials science) ; Computer science ; Focus (optics) ; Materials science ; Engineering ethics ; Engineering ; Physics ; Artificial intelligence Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; nanoparticles nucleation surface interactions
Authors: Zhixin Ren | Ke Gong | Bo Zhao | Shi‐Lu Chen | Jing Xie
Source: Materials Chemistry Frontiers (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Boosting the catalytic performance of metalloporphyrin-based covalent organic frameworks <i>via</i> coordination engineering for CO<sub>2</sub> and O<sub>2</sub> reduction Coordination engineering is an effective strategy for improving the performance of Por-COFs in catalyzing CO 2 and O 2 electroreduction. Our computations suggest that Co–N 2 O 2 and Ni–N 2 S 2 coordinated Por-COFs could be potential candidates. Boosting (machine learning) ; Catalysis ; Metal-organic framework ; Covalent bond ; Reduction (mathematics) ; Materials science ; Covalent organic framework ; Coordination complex ; Chemistry ; Nanotechnology ; Combinatorial chemistry ; Computer science ; Organic chemistry ; Metal ; Artificial intelligence ; Mathematics Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; CO2 Reduction Techniques and Catalysts
Authors: Bushra Tousian | Alireza Khosravi | Mohammad Hadi Ghasemi | Majid Kadkhodaie
Source: Materials Today Bio (review, 2024-06-20)
Field: Chemistry — Inorganic Chemistry

Biomimetic functionalized metal organic frameworks as multifunctional agents: Paving the way for cancer vaccine advances Biomimetic functionalized metal-organic frameworks (Fn-MOFs) represent a cutting-edge approach in the realm of cancer vaccines. These multifunctional agents, inspired by biological systems, offer unprecedented opportunities for the development of next-generation cancer vaccines. The vast surface area, tunable pore size, and diverse chemistry of MOFs provide a versatile scaffold for the encapsulation and protection of antigenic components, crucial for vaccine stability and delivery. This work delves into the innovative design and application of Fn-MOFs, highlighting their role as carriers for immune enhancement and their potential to revolutionize vaccine delivery. By mimicking natural processes, Fn-MOFs, with their ability to be functionalized with a myriad of chemical and biological entities, exhibit superior biocompatibility and stimuli-responsive behavior and facilitate targeted delivery to tumor sites. This review encapsulates the latest advancements in Fn-MOF technology, from their synthesis and surface modification to their integration into stimuli-responsive and combination therapies. It underscores the significance of biomimetic approaches in overcoming current challenges in cancer vaccine development, such as antigen stability and immune evasion. By leveraging the biomimetic nature of Fn-MOFs, this work paves the way for innovative strategies in cancer vaccines, aiming to induce potent and long-lasting immune responses against malignancies. Metal-organic framework ; Nanotechnology ; Materials science ; Chemistry ; Adsorption ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; RNA Interference and Gene Delivery
Authors: Minting Lei | Peijian Wang | Xiaofeng Ke | Jun Xie | Min Yue | Mei Zhao | Kenan Zhang | Youqing Dong | Quanlong Xu | Chao Zou | Shun Wang | Lijie Zhang
Source: Electron (article, 2024-06-12)
Field: Materials Science — Materials Chemistry

Low‐temperature chemical vapor deposition growth of 2D materials Abstract Two‐dimensional (2D) materials have atomic thickness, and thickness‐dependent electronic transport, optical and thermal properties, highlighting great promise applications in future semiconductor devices. Chemical vapor deposition (CVD) is considered as an industry‐oriented method for macro‐synthesis of 2D materials. In conventional CVD, high temperatures are required for the synthesis of high‐quality large‐size 2D materials, which is incompatible with of back‐end‐of‐line of the complementary metal oxide semiconductor (CMOS) techniques. Therefore, low‐temperature synthesis of 2D materials is of critical importance for the advancement toward practical applications of 2D materials with the CMOS technologies. In this review, we focus on strategies for the low‐temperature growth of 2D materials, including the use of low‐melting‐point precursors, metal‐organic CVD, plasma‐enhanced CVD, van der Waals‐substrate vapor phase epitaxy, tellurium‐assisted CVD, salt‐assisted CVD, etc., with discussions of their reaction mechanisms, applications, associated advantages, and limitations. We also provide an outlook and perspectives of future low‐temperature chemical vapor deposition growth of 2D materials. Chemical vapor deposition ; Materials science ; Nanotechnology ; Substrate (aquarium) ; Semiconductor ; Epitaxy ; Deposition (geology) ; Optoelectronics ; Layer (electronics) 2D Materials and Applications ; Nanowire Synthesis and Applications ; Ga2O3 and related materials
Authors: Yaohui Xu | Xi Yang | Yuting Li | Yu Zhao | Shu Xing | Guoying Zhang | Tingna Yang | Yitao Liu | Pingkeng Wu | Zhao Ding
Source: Nanomaterials (review, 2024-10-18)
Field: Materials Science — Materials Chemistry

Rare-Earth Metal-Based Materials for Hydrogen Storage: Progress, Challenges, and Future Perspectives Rare-earth-metal-based materials have emerged as frontrunners in the quest for high-performance hydrogen storage solutions, offering a paradigm shift in clean energy technologies. This comprehensive review delves into the cutting-edge advancements, challenges, and future prospects of these materials, providing a roadmap for their development and implementation. By elucidating the fundamental principles, synthesis methods, characterization techniques, and performance enhancement strategies, we unveil the immense potential of rare-earth metals in revolutionizing hydrogen storage. The unique electronic structure and hydrogen affinity of these elements enable diverse storage mechanisms, including chemisorption, physisorption, and hydride formation. Through rational design, nanostructuring, surface modification, and catalytic doping, the hydrogen storage capacity, kinetics, and thermodynamics of rare-earth-metal-based materials can be significantly enhanced. However, challenges such as cost, scalability, and long-term stability need to be addressed for their widespread adoption. This review not only presents a critical analysis of the state-of-the-art but also highlights the opportunities for multidisciplinary research and innovation. By harnessing the synergies between materials science, nanotechnology, and computational modeling, rare-earth-metal-based hydrogen storage materials are poised to accelerate the transition towards a sustainable hydrogen economy, ushering in a new era of clean energy solutions. Hydrogen storage ; Nanotechnology ; Physisorption ; Materials science ; Rare earth ; Scalability ; Energy storage ; Biochemical engineering ; Process engineering ; Computer science ; Chemistry ; Catalysis ; Engineering ; Alloy ; Physics ; Metallurgy Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; Nanomaterials for catalytic reactions