jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abdelhamid Khelifi | Messaouda Boumaaza | Ahmed Belaadi | Djedid Tarek | Afonso Rangel Garcez de Azevedo | Mostefa Bourchak | Mohammad Jawaid
Source: Biomass Conversion and Biorefinery (article, 2023-04-28)
Field: Materials Science — Polymers and Plastics

Effects of alkaline treatment of Washingtonia mesh waste on the mechanical and physical properties of bio-mortar: experimental and prediction models Mortar ; Response surface methodology ; Compressive strength ; Artificial neural network ; Materials science ; Polypropylene ; Flexural strength ; Mean squared error ; Building material ; Cement ; Environmentally friendly ; Composite material ; Process engineering ; Structural engineering ; Computer science ; Mathematics ; Engineering ; Machine learning ; Statistics Natural Fiber Reinforced Composites ; Innovations in Concrete and Construction Materials ; Innovative concrete reinforcement materials
Authors: Owais Ahmad | Naveen Kumar | R. Mukherjee | Somnath Bhowmick
Source: Physical Review Materials (article, 2023-08-07)
Field: Materials Science — Materials Chemistry

Accelerating microstructure modeling via machine learning: A method combining Autoencoder and ConvLSTM Phase-field modeling is an elegant and versatile computation tool to predict microstructure evolution in materials in the mesoscale regime. However, these simulations require rigorous numerical solutions of differential equations, which are accurate but computationally expensive. To overcome this difficulty, we combine two popular machine-learning techniques, autoencoder and convolutional long short-term memory (ConvLSTM), to accelerate the study of microstructural evolution without compromising the resolution of the microstructural representation. After training with phase-field-generated microstructures of 10 known compositions, the model can accurately predict the microstructure for the future $n\mathrm{th}$ frames based on the previous $m$ frames for an unknown composition. Replacing $n$ phase-field steps with machine-learned microstructures can significantly accelerate the in silico study of microstructure evolution. Autoencoder ; Microstructure ; Representation (politics) ; Materials science ; Artificial intelligence ; Phase (matter) ; Field (mathematics) ; Mesoscale meteorology ; Computation ; Computer science ; Machine learning ; Computational science ; Algorithm ; Pattern recognition (psychology) ; Deep learning ; Metallurgy ; Physics ; Mathematics Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels
Authors: Byung Do Lee | Jin-Woong Lee | Junuk Ahn | Seonghwan Kim | Woon Bae Park | Kee‐Sun Sohn
Source: Advanced Intelligent Systems (article, 2023-06-02)
Field: Materials Science — Materials Chemistry

A Deep Learning Approach to Powder X‐Ray Diffraction Pattern Analysis: Addressing Generalizability and Perturbation Issues Simultaneously A deep learning (DL)‐based approach for analysis is proposed. Using synthetic XRD data for a DL approach is inevitable due to the lack of real‐world XRD data. There are two main challenges when conducting a DL‐based XRD analysis: generating realistic XRD data including all possible perturbations, such as peak shift, broadening, texture, and noisy background, and generalizing the DL model applicability to all ICSD entries. To address both the perturbation and generalizability issues, a large‐scale computation is required because it would be infeasible with typical lab‐scale computation. Cloud computing infrastructures are leveraged for parallel computations and to obtain symmetry classification test accuracies of 98.95%, 97.18%, and 96.03% for the crystal system, extinction group, and space group, respectively. A stricter individual compound‐based train and test dataset‐splitting scheme also produces reasonable test accuracies of 92.25%, 87.34%, and 84.39%, which are still state‐of‐the‐art records. Crucially, the DL model trained on synthetic data is assessed using real‐world experimental XRD datasets to ensure its practical applicability. When tested on the real‐world experimental XRD dataset, the model achieves a test accuracy of 90.38% in predicting crystal systems. Generalizability theory ; Computation ; Computer science ; Artificial intelligence ; Algorithm ; Synthetic data ; Diffraction ; Machine learning ; Mathematics ; Physics ; Optics ; Statistics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Zhipeng Ding | Wei Su | F. Hakimi | Yinlong Luo | Wenlong Li | Yuanhang Zhou | Lipengan Ye | Hongbing Yao
Source: Solar Energy Materials and Solar Cells (article, 2023-09-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning in prediction of MXenes-based metasurface absorber for maximizing solar spectral absorption MXenes ; Absorption (acoustics) ; Materials science ; Solar energy ; Broadband ; Computer science ; Process (computing) ; Optoelectronics ; Biological system ; Optics ; Physics ; Nanotechnology ; Engineering ; Telecommunications Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Energy Harvesting in Wireless Networks
Authors: Ting-Wei Wu | Pinjing He | Dong-Ying Lan | Fan Lü | Jisheng Long | Hua Zhang
Source: ACS ES&T Engineering (article, 2023-04-06)
Field: Chemistry — Analytical Chemistry

Application of XGBoost for Fast Identification of Typical Industrial Organic Waste Samples with Near-Infrared Hyperspectral Imaging Waste material identification is an essential part of waste recycling and treatment. Hyperspectral imaging (HSI) enables fast, accurate, nondestructive, and non-invasive identification of waste materials. In this study, HSI-based classification of typical industrial organic waste that cannot be sorted via traditional methods has been explored, namely, leather, paper, plastic, rubber, textile, and wood. The extreme gradient boosting (XGBoost) algorithm, a supervised machine learning algorithm that has never been investigated for waste identification-related fields, was adopted. The results show that XGBoost obtained a higher pixel-wise weighted average F1-score of 82.72% and a faster prediction time of 270 ms for the tested images compared with the commonly used partial least squares-discriminant analysis (77.83% and 444 ms). XGBoost was more effective and efficient in aiding HSI identification and classification of industrial organic waste. The technique can be a significant advancement in the development of an online sorting or identification platform, affording significant labor cost reduction, time savings, and the provision of a stable, accurate, and rapid method for waste intelligent identification. Hyperspectral imaging ; Identification (biology) ; Sorting ; Linear discriminant analysis ; Partial least squares regression ; Artificial intelligence ; Pixel ; Computer science ; Boosting (machine learning) ; Biodegradable waste ; Pattern recognition (psychology) ; Environmental science ; Waste management ; Engineering ; Machine learning ; Algorithm Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Advanced Chemical Sensor Technologies
Authors: Devdutt Tripathi | Hardik Vyas | S. Vijay Kumar | Soumyashree S. Panda | Ravi S. Hegde
Source: Nanotechnology (review, 2023-08-18)
Field: Materials Science — Materials Chemistry

Recent developments in Chalcogenide phase change material-based nanophotonics There is now a deep interest in actively reconfigurable nanophotonics as they will enable the next generation of optical devices. Of the various alternatives being explored for reconfigurable nanophotonics, Chalcogenide phase change materials (PCMs) are considered highly promising owing to the nonvolatile nature of their phase change. Chalcogenide PCM nanophotonics can be broadly classified into integrated photonics (with guided wave light propagation) and Meta-optics (with free space light propagation). Despite some early comprehensive reviews, the pace of development in the last few years has shown the need for a topical review. Our comprehensive review covers recent progress on nanophotonic architectures, tuning mechanisms, and functionalities in tunable PCM Chalcogenides. In terms of integrated photonics, we identify novel PCM nanoantenna geometries, novel material utilization, the use of nanostructured waveguides, and sophisticated excitation pulsing schemes. On the meta-optics front, the breadth of functionalities has expanded, enabled by exploring design aspects for better performance. The review identifies immediate, and intermediate-term challenges and opportunities in (1) the development of novel chalcogenide PCM, (2) advance in tuning mechanism, and (3) formal inverse design methods, including machine learning augmented inverse design, and provides perspectives on these aspects. The topical review will interest researchers in further advancing this rapidly growing subfield of nanophotonics. Nanophotonics ; Chalcogenide ; Photonics ; Materials science ; Nanotechnology ; Computer science ; Engineering physics ; Optoelectronics ; Engineering Phase-change materials and chalcogenides ; Neural Networks and Reservoir Computing ; Perovskite Materials and Applications
Authors: Ke Li | Xueping Quan | Bing Yan
Source: Sensors and Actuators B Chemical (article, 2023-06-02)
Field: Chemistry — Inorganic Chemistry

Eu(III)-functionalized iCOF hybrids by “tandem post-synthetic modifications” for fluorescent detection of folic acid and trimethoprim: A logical judgment by combination of neural networks and logic gates Chemistry ; Combinatorial chemistry ; Cationic polymerization ; Tandem ; Carboxylate ; Ligand (biochemistry) ; Covalent bond ; Folic acid ; Fluorescence ; Lanthanide ; Logic gate ; Stereochemistry ; Ion ; Materials science ; Polymer chemistry ; Organic chemistry ; Computer science ; Algorithm ; Biochemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Molecular Sensors and Ion Detection
Authors: Yuanjing Wang | Wenyu Tang | Chuang Zhang | Мaxim S. Моlokeev | Hong Ming | Yayun Zhou | Shuai Peng | Enhai Song | Qinyuan Zhang
Source: Advanced Functional Materials (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Structure‐Based Machine Learning Enables Discovery of Mn<sup>4+</sup>‐Activated Red‐Light Fluorides for Ultrawide‐Gamut Mini‐Light‐Emitting Diodes Abstract Mn 4+ ‐activated fluorides with a saturated red color and sharp line emission are ideal for applications in the light‐emitting diodes (LEDs) backlight for displays. However, the emissions attributed to 2 E→ 4 A 2 parity and spin‐forbidden transitions limit the design and adjustments of emission wavelength and chromaticity coordinates. Herein, machine learning algorithms are used to build a wavelength‐prediction model for Mn 4+ ‐activated fluorides. The model precisely identifies the key structural features that affect wavelengths and discovers target materials. The predicted candidate Cs 2 NaAlF 6 :Mn 4+ (CNAF) with a long‐wavelength zero‐phonon‐line emission at 628 nm exhibits a redshift in comparison with other reported Mn 4+ ‐activated fluorides and commercial K 2 SiF 6 :Mn 4+ , but maintains narrow spectral emission with full‐width half maximum (FWHM) of 11.2 nm. The redshift and narrow spectra result in a color purity of 99.7% and Commission Internationale de L'Eclairage (CIE) chromaticity coordinate of (0.7032,0.2967) that is close to the pure red‐light point of Recommendation BT. 2020 (Rec. 2020). Moreover, CNAF is prepared as a transparent red‐light film, and the device fabricated using the blue‐light mini‐LEDs, green quantum‐dot film, and CNAF film exhibits a wide color‐gamut of 121.5% National Television Standards Committee (NTSC) or 90.6% Rec. 2020, suggesting that CNAF has potential for wide‐color‐gamut displays. Gamut ; Chromaticity ; Light-emitting diode ; Backlight ; Materials science ; Color temperature ; Optoelectronics ; Wavelength ; Full width at half maximum ; Emission spectrum ; Optics ; Electroluminescence ; Diode ; Spectral line ; Physics ; Liquid-crystal display ; Nanotechnology Luminescence Properties of Advanced Materials ; Perovskite Materials and Applications ; Ga2O3 and related materials
Authors: Idris Temitope Bello | Ridwan Taiwo | Oladapo Christopher Esan | Adesola Habeeb Adegoke | Ahmed Olanrewaju Ijaola | Zheng Li | Siyuan Zhao | Chen Wang | Zongping Shao | Meng Ni
Source: Energy and AI (article, 2023-11-09)
Field: Materials Science — Materials Chemistry

AI-enabled materials discovery for advanced ceramic electrochemical cells Ceramic electrochemical cells (CECs) are promising devices for clean and efficient energy conversion and storage due to their high energy efficiency, more extended system durability, and less expensive materials. However, the search for suitable materials with desired properties, including high ionic and electronic conductivity, thermal stability, and chemical compatibility, presents ongoing challenges that impede widespread adoption and further advancement in the field. Artificial intelligence (AI) has emerged as a versatile tool capable of enhancing and expediting the materials discovery cycle in CECs through data-driven modeling, simulation, and optimization techniques. Herein, we comprehensively review the state-of-the-art AI applications for materials design and optimization for CECs, covering various material aspects, database construction, data pre-processing, and AI methods. We also present some representative case studies of AI-predicted and synthesized materials for CECs and provide insightful highlights about their approaches. We emphasize the main implications and contributions of the AI approach for advancing the CEC technology, such as reducing the trial-and-error experiments, exploring the vast materials space, discovering novel and optimal materials, and enhancing the understanding of the materials-performance relationships. We also discuss the AI approach's main limitations and future directions for CECs, such as addressing the data and model challenges, improving and extending the AI models and methods, and integrating with other computational and experimental techniques. We conclude by suggesting some potential applications and collaborations for AI in materials design for CECs. Expediting ; Electrochemical energy storage ; Computer science ; Big data ; Efficient energy use ; Nanotechnology ; Systems engineering ; Data science ; Materials science ; Engineering ; Data mining ; Supercapacitor Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advancements in Solid Oxide Fuel Cells
Authors: Jun Cai | Peng Yang | Yanxin Jiang | Li Li | Hua Wang | Kongzhai Li
Source: Molecules (review, 2023-10-17)
Field: Chemistry — Inorganic Chemistry

Application of Fe-MOFs in Photodegradation and Removal of Air and Water Pollutants: A Review Photocatalytic technology has received increasing attention in recent years. A pivotal facet of photocatalytic technology lies in the development of photocatalysts. Porous metal-organic framework (MOF) materials, distinguished by their unique properties and structural characteristics, have emerged as a focal point of research in the field, finding widespread application in the photo-treatment and conversion of various substances. Fe-based MOFs have attained particular prominence. This review explores recent advances in the photocatalytic degradation of aqueous and gaseous substances. Furthermore, it delves into the interaction between the active sites of Fe-MOFs and pollutants, offering deeper insights into their mechanism of action. Fe-MOFs, as photocatalysts, predominantly facilitate pollutant removal through redox processes, interaction with acid sites, the formation of complexes with composite metal elements, binding to unsaturated metal ligands (CUSs), and hydrogen bonding to modulate their respiratory behavior. This review also highlights the focal points of future research, elucidating the challenges and opportunities that lie ahead in harnessing the characteristics and advantages of Fe-MOF composite catalysts. In essence, this review provides a comprehensive summary of research progress on Fe-MOF-based catalysts, aiming to serve as a guiding reference for other catalytic processes. Photocatalysis ; Photodegradation ; Catalysis ; Nanotechnology ; Pollutant ; Metal-organic framework ; Materials science ; Chemistry ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Gas Sensing Nanomaterials and Sensors
Authors: Shreya Talreja | Shashank Tiwari
Source: Journal of Pharmacy and Pharmacology Research (article, 2023-01-01)
Field: Materials Science — Biomaterials

Supramolecular Chemistry: Unveiling the Fascinating World of Non-Covalent Interactions and Complex Assemblies Supramolecular chemistry is a captivating and interdisciplinary field that explores the interactions between molecules to form complex and functional assemblies through non-covalent forces. This review paper presents an in-depth exploration of the fundamental concepts, supramolecular assemblies and structures, applications in nanotechnology and biology, as well as challenges and future perspectives in supramolecular chemistry. The paper begins by elucidating the fundamental principles of supramolecular chemistry, emphasizing the significance of weak, non-covalent interactions such as hydrogen bonding, van der Waals forces, and π-π interactions. Molecular recognition, self-assembly, and host-guest interactions are highlighted as key concepts shaping the field. Subsequently, the review delves into various supramolecular assemblies and structures, showcasing the diversity of nanoscale architectures that arise from self-assembly processes. From nanotubes and nanofibers to metal-organic frameworks and dynamic supramolecular systems, each structure's properties and potential applications are explored. The application of supramolecular chemistry in nanotechnology and biology is a central focus of the paper. It covers the design of supramolecular materials for drug delivery, nanoelectronics, nanosensors, and biomimetic systems. Additionally, the integration of supramolecular approaches in biology, including molecular recognition, enzyme mimics, and bioimaging, is discussed in detail. Furthermore, the challenges faced by supramolecular chemistry, such as predictability, stability, and scalability, are addressed. The paper also looks into the future perspectives of the field, envisioning adaptive materials, supramolecular machines, and data-driven design as exciting prospects. Overall, this comprehensive review offers a thorough understanding of the captivating world of supramolecular chemistry and its potential to revolutionize various scientific and technological domains. Through interdisciplinary efforts and a focus on sustainability, supramolecular chemistry holds promise for addressing real-world challenges and shaping a future defined by innovative materials and transformative applications. Supramolecular chemistry ; Nanotechnology ; Chemistry ; Materials science ; Molecule ; Organic chemistry Supramolecular Self-Assembly in Materials ; Supramolecular Chemistry and Complexes ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Hanyu Tu | Huaxin Liu | Laiqiang Xu | Zheng Luo | Lin Li | Ye Tian | Wentao Deng | Guoqiang Zou | Hongshuai Hou | Xiaobo Ji
Source: Chemical Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Carbon dots from alcohol molecules: principles and the reaction mechanism Carbon dots (CDs) have attracted significant attention in the energy, environment, and biology fields due to their exceptional physicochemical properties. However, owing to the multifarious precursors and complex reaction mechanisms, the production of carbon dots from organic molecules is still a mysterious process. Inspired by the color change of sodium hydroxide ethanol solution after standing for some time, in this work, we thoroughly investigated the reaction mechanism from alcohol molecules to carbon dots through a lot of experiments and theoretical calculations, and it was found that the rate-controlling reaction is the formation of aldehydes, and it is also confirmed that there is a self-catalysis reaction, which can accelerate the conversion from alcohol to aldehyde, further facilitating the final formation of CDs. After the rate-controlling reaction of alcohol to aldehyde, under strongly alkaline conditions, an aldol reaction occurs to form unsaturated aldehydes, followed by further condensation and polymerization reactions to form long carbon chains, which are cross-linked and dehydrated to form carbon dots with a carbon core and surface functional groups. Additionally, it is found that the reaction can be largely accelerated with the assistance of electricity, which indicates the great prospect of industrial production. Furthermore, the obtained CDs with rich functional groups can be utilized as electrolyte additives to optimize the deposition behavior of Na metal, manifesting great potential towards safe and stable Na metal batteries. Carbon fibers ; Mechanism (biology) ; Electrolyte ; Alcohol ; Molecule ; Reaction mechanism ; Metal ; Materials science ; Chemistry ; Nanotechnology ; Chemical engineering ; Organic chemistry ; Catalysis ; Physical chemistry ; Electrode Carbon and Quantum Dots Applications ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: N. Giakoumakis | Christophe Vos | K. Janssens | Jelle Vekeman | Mats Denayer | Frank De Proft | Carlos Márquez | Dirk De Vos
Source: Green Chemistry (article, 2023-11-29)
Field: Materials Science — Polymers and Plastics

Total revalorization of high impact polystyrene (HIPS): enhancing styrene recovery and upcycling of the rubber phase The styrene recovery from high impact polystyrene waste can be enhanced by pre-fractionation with ethyl acetate followed by pyrolysis of the isolated polystyrene phase and ethenolysis of the polybutadiene phase. Polystyrene ; Styrene ; Phase (matter) ; Natural rubber ; Chemistry ; Materials science ; Composite material ; Polymer chemistry ; Chemical engineering ; Organic chemistry ; Copolymer ; Polymer ; Engineering Polymer crystallization and properties ; Microplastics and Plastic Pollution ; biodegradable polymer synthesis and properties
Authors: Reshma Jose | Garima Bangar | Sourav Pal | Gopalan Rajaraman
Source: Journal of Chemical Sciences (review, 2023-03-14)
Field: Chemistry — Inorganic Chemistry

Role of molecular modelling in the development of metal-organic framework for gas adsorption applications Metal-organic framework ; Density functional theory ; Adsorption ; Nanotechnology ; Chemistry ; Chemical physics ; Computer science ; Biochemical engineering ; Computational chemistry ; Materials science ; Physical chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Magnetism in coordination complexes