jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wencong Song | Saz Muhammad | Siying Dang | Xingyan Ou | Xingzi Fang | Yinghe Zhang | Lihe Huang | Bing Guo | XueLian Du
Source: Frontiers in Chemistry (article, 2024-02-26)
Field: Materials Science — Polymers and Plastics

The state-of-art polyurethane nanoparticles for drug delivery applications Nowadays, polyurethanes (PUs) stand out as a promising option for drug delivery owing to their versatile properties. PUs have garnered significant attention in the biomedical sector and are extensively employed in diverse forms, including bulk devices, coatings, particles, and micelles. PUs are crucial in delivering various therapeutic agents such as antibiotics, anti-cancer medications, dermal treatments, and intravaginal rings. Effective drug release management is essential to ensure the intended therapeutic impact of PUs. Commercially available PU-based drug delivery products exemplify the adaptability of PUs in drug delivery, enabling researchers to tailor the polymer properties for specific drug release patterns. This review primarily focuses on the preparation of PU nanoparticles and their physiochemical properties for drug delivery applications, emphasizing how the formation of PUs affects the efficiency of drug delivery systems. Additionally, cutting-edge applications in drug delivery using PU nanoparticle systems, micelles, targeted, activatable, and fluorescence imaging-guided drug delivery applications are explored. Finally, the role of artificial intelligence and machine learning in drug design and delivery is discussed. The review concludes by addressing the challenges and providing perspectives on the future of PUs in drug delivery, aiming to inspire the design of more innovative solutions in this field. Drug delivery ; Nanotechnology ; Drug ; Targeted drug delivery ; Materials science ; Computer science ; Medicine ; Pharmacology Polymer composites and self-healing ; Advanced Polymer Synthesis and Characterization ; Dendrimers and Hyperbranched Polymers
Authors: Bin Wei | Yongheng Li | Li Wang | Kai Wang | Qiyang Sun | Xiaolong Yang | D. L. Abernathy | Qilong Gao | Chen Li | Jiawang Hong | Yuanhua Lin
Source: Physical review. B./Physical review. B (article, 2024-04-25)
Field: Materials Science — Materials Chemistry

High-order phonon anharmonicity and thermal conductivity in GaN A comprehensive understanding of phonon transport is essential to develop effective solutions for heat dissipation. Gallium nitride (GaN), a representative of third-generation power semiconductors, has been extensively studied regarding its thermodynamics and lattice dynamics. However, the temperature-dependent phonon properties, especially the anharmonicity at high temperatures, are poorly understood. Here, by combining inelastic neutron scattering (INS) experiments and calculations including the temperature effect based on machine learning potentials, we report the high-order phonon anharmonicity in GaN over a wide temperature range. Our calculations agree well with the experimental phonon dispersion, density of states and entropy, underlining the significance of anharmonicity of GaN at elevated temperatures. Moreover, considering the four-phonon processes, the calculated thermal conductivity is suppressed by 20%, and the anisotropy is also reduced gradually with increasing temperature. Such behavior arises mainly from the large four-phonon scattering channels between 20 and 30 meV, where the critical scattering rule for the three-phonon process is largely restricted at high temperatures. Our study highlights the importance of high-order phonon anharmonicity for thermal transport in GaN and provides a theoretical reference for thermal management in other related semiconductors. Anharmonicity ; Phonon ; Condensed matter physics ; Thermal conductivity ; Phonon scattering ; Materials science ; Gallium nitride ; Scattering ; Semiconductor ; Atmospheric temperature range ; Inelastic neutron scattering ; Inelastic scattering ; Physics ; Thermodynamics ; Nanotechnology ; Optics ; Optoelectronics Thermal properties of materials ; GaN-based semiconductor devices and materials ; Machine Learning in Materials Science
Authors: Wenrui Jia | Ying Li | Chuchu Chen | Yaohua Wu | Yongqi Liang | Juanshan Du | Xiaochi Feng | Huazhe Wang | Qinglian Wu | Wanqian Guo
Source: Journal of Hazardous Materials (article, 2024-07-21)
Field: Chemistry — Electrochemistry

Unveiling the fate of metal leaching in bimetal-catalyzed Fenton-like systems: pivotal role of aqueous matrices and machine learning prediction Bimetal ; Leaching (pedology) ; Catalysis ; Aqueous solution ; Metal ; Chemistry ; Environmental chemistry ; Chemical engineering ; Inorganic chemistry ; Environmental science ; Organic chemistry ; Engineering ; Soil science ; Physical chemistry ; Soil water Electrochemical Analysis and Applications ; Metal Extraction and Bioleaching ; Extraction and Separation Processes
Authors: Vu Hoang Minh Doan | Cao Duong Ly | Sudip Mondal | Thi Thuy Truong | Tuan Dung Nguyen | Jaeyeop Choi | Byeongil Lee | Junghwan Oh
Source: Analytical Chemistry (article, 2024-07-15)
Field: Chemistry — Analytical Chemistry

Fcg-Former: Identification of Functional Groups in FTIR Spectra Using Enhanced Transformer-Based Model Deep learning (DL) is becoming more popular as a useful tool in various scientific domains, especially in chemistry applications. In the infrared spectroscopy field, where identifying functional groups in unknown compounds poses a significant challenge, there is a growing need for innovative approaches to streamline and enhance analysis processes. This study introduces a transformative approach leveraging a DL methodology based on transformer attention models. With a data set containing approximately 8677 spectra, our model utilizes self-attention mechanisms to capture complex spectral features and precisely predict 17 functional groups, outperforming conventional architectures in both functional group prediction accuracy and compound-level precision. The success of our approach underscores the potential of transformer-based methodologies in enhancing spectral analysis techniques. Chemistry ; Transformative learning ; Transformer ; Artificial intelligence ; Biological system ; Machine learning ; Biochemical engineering ; Computer science ; Engineering ; Voltage Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Jie Gao | Xiumei Guo | Yuanfang Wu | Wei Xiao | Lei Hao
Source: International Journal of Hydrogen Energy (article, 2024-01-27)
Field: Materials Science — Materials Chemistry

The hydrogen absorption process prediction of AB2 hydrogen storage device based on data-driven approach The establishment of a prediction model for the hydrogen absorption state of a solid-state hydrogen storage device is crucial for its practical application. In this paper, a solid-state hydrogen storage device filled with AB2 hydrogen storage alloy is investigated. The experimental data are obtained by controlling the hydrogen absorption temperature and hydrogen absorption rate respectively. The Broyden-Fletcher-Goldfarb-Shanno algorithm (BFGS) is used to fit the solid-state hydrogen storage device experimental data based on the Richards model, and Linear Regression, Gradient Descent and Artificial Neural Network algorithms are used to predict the parameters of the Richards model. It is confirmed that the Linear Regression model has the best prediction effect after verified by the test set, with coefficients of determination R2 ≥ 0.96. This study provides an effective way to predict the hydrogen absorption state of solid-state hydrogen storage devices quickly. Hydrogen storage ; Broyden–Fletcher–Goldfarb–Shanno algorithm ; Hydrogen ; Computer science ; Absorption (acoustics) ; Algorithm ; Materials science ; Chemistry Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Advanced Battery Technologies Research
Authors: Qian Chen | Jiali Li | Jianying Feng | Jianping Qian
Source: Food Quality and Safety (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Dynamic comprehensive quality assessment of post-harvest grape in different transportation chains using SAHP–CatBoost machine learning Abstract Objectives Food quality assessment is critical for indicating the shelf-life and ensuring food safety or value. Due to high environmental sensitivity, the post-harvest quality of fresh fruit will undergo complex changes in the supply chain, with various dynamic quality-related features. It is difficult to efficiently and accurately extract comprehensive quality feature of post-harvest fruits from high-dimensional monitoring data with heterogeneous characteristics (numerical and categorical). Therefore, we proposed a dynamic comprehensive quality assessment method based on self-adaptive analytic hierarchy process (SAHP) integrated with the CatBoost model. Materials and Methods By adaptive weight optimization, the SAHP was utilized to analyze the multi-source quality information and obtain the quantized fusion value, as an output sample of CatBoost machine learning. Then, using heterogeneous monitoring data as input, the CatBoost model was directly trained through unbiased boosting with categorical features for dynamic assessment of overall quality status. Results Three quality index monitoring data sets for ‘Jufeng’ grape in different transportation chains (normal temperature, cold insulation, and cold chain) were individually constructed as the research samples. Furthermore, compared to other machine learning methods, the SAHP–CatBoost had more accurate results in comprehensive quality feature extraction. In actual transportation chains, the mean absolute error, mean absolute percentage error, and root mean squared error of dynamic comprehensive assessment were limited to 0.0044, 1.012%, and 0.0078, respectively. Conclusions The proposed method is efficient in handling heterogeneous monitoring data and extracting comprehensive quality information of post-harvest grape as a robust shelf-life indicator. It can reasonably guide post-harvest quality management to reduce food loss and improve economic benefits. Quality (philosophy) ; Computer science ; Environmental science ; Physics Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management
Authors: Kieran A. Murphy | Dani S. Bassett
Source: Proceedings of the National Academy of Sciences (article, 2024-03-18)
Field: Materials Science — Materials Chemistry

Information decomposition in complex systems via machine learning One of the fundamental steps toward understanding a complex system is identifying variation at the scale of the system's components that is most relevant to behavior on a macroscopic scale. Mutual information provides a natural means of linking variation across scales of a system due to its independence of functional relationship between observables. However, characterizing the manner in which information is distributed across a set of observables is computationally challenging and generally infeasible beyond a handful of measurements. Here, we propose a practical and general methodology that uses machine learning to decompose the information contained in a set of measurements by jointly optimizing a lossy compression of each measurement. Guided by the distributed information bottleneck as a learning objective, the information decomposition identifies the variation in the measurements of the system state most relevant to specified macroscale behavior. We focus our analysis on two paradigmatic complex systems: a Boolean circuit and an amorphous material undergoing plastic deformation. In both examples, the large amount of entropy of the system state is decomposed, bit by bit, in terms of what is most related to macroscale behavior. The identification of meaningful variation in data, with the full generality brought by information theory, is made practical for studying the connection between micro- and macroscale structure in complex systems. Information bottleneck method ; Computer science ; Observable ; Theoretical computer science ; Lossy compression ; Information theory ; Bottleneck ; Entropy (arrow of time) ; Complex system ; Mutual information ; Variation (astronomy) ; Generality ; Granularity ; Set (abstract data type) ; Data mining ; Algorithm ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Tian-Xing Yang | Peng Dou
Source: Materials Characterization (article, 2024-04-06)
Field: Materials Science — Materials Chemistry

Prediction of hardness or yield strength for ODS steels based on machine learning Materials science ; High-resolution transmission electron microscopy ; Transmission electron microscopy ; Scanning electron microscope ; Yield (engineering) ; Oxide ; Microstructure ; Metallurgy ; Dispersion (optics) ; Grain size ; Composite material ; Nanotechnology Fusion materials and technologies ; Nuclear Materials and Properties ; Metal and Thin Film Mechanics
Authors: Suzan Elsheikh | Nathan Coles | Ojodomo J. Achadu | Panagiota S. Filippou | Ahmad A. Khundakar
Source: Biosensors (review, 2024-01-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advancing Brain Research through Surface-Enhanced Raman Spectroscopy (SERS): Current Applications and Future Prospects Surface-enhanced Raman spectroscopy (SERS) has recently emerged as a potent analytical technique with significant potential in the field of brain research. This review explores the applications and innovations of SERS in understanding the pathophysiological basis and diagnosis of brain disorders. SERS holds significant advantages over conventional Raman spectroscopy, particularly in terms of sensitivity and stability. The integration of label-free SERS presents promising opportunities for the rapid, reliable, and non-invasive diagnosis of brain-associated diseases, particularly when combined with advanced computational methods such as machine learning. SERS has potential to deepen our understanding of brain diseases, enhancing diagnosis, monitoring, and therapeutic interventions. Such advancements could significantly enhance the accuracy of clinical diagnosis and further our understanding of brain-related processes and diseases. This review assesses the utility of SERS in diagnosing and understanding the pathophysiological basis of brain disorders such as Alzheimer's and Parkinson's diseases, stroke, and brain cancer. Recent technological advances in SERS instrumentation and techniques are discussed, including innovations in nanoparticle design, substrate materials, and imaging technologies. We also explore prospects and emerging trends, offering insights into new technologies, while also addressing various challenges and limitations associated with SERS in brain research. Surface-enhanced Raman spectroscopy ; Nanotechnology ; Medicine ; Computer science ; Neuroscience ; Raman spectroscopy ; Materials science ; Psychology ; Raman scattering Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Electrochemical Analysis and Applications
Authors: Zhiqi Wang | Jianan Su | Duo Feng | Yufang Yao | Yujing Yan | Yanjie Cui | Gian‐Marco Rignanese | Hideo Hosono | Junjie Wang
Source: Small (article, 2024-09-30)
Field: Materials Science — Materials Chemistry

Discovery of Bimetallic Hexagonal MBene Mo<sub>2</sub>ErB<sub>3</sub>T<sub>2.5</sub> (T = O, F, and Cl) Exfoliation from quaternary hexagonal MAB (h-MAB) phases has been suggested as a method for producing 2D in-plane ordered MBenes (i-MBenes) with the general formula (M'<sub>2/3</sub>M″<sub>1/3</sub>)<sub>2</sub>AB<sub>2</sub>. However, experimental realization of defect-free i-MBenes has not been achieved yet due to the absence of a suitable parent quaternary h-MAB phase. In this study, a machine learning (ML) model is used to predict the stability of 15771 quaternary h-MAB phases generated by considering 33 transition metals for the M site and 16 p-block elements for the A site. Out of these compounds, only 195 are identified as potentially stable. Subsequent high-precision first-principles calculations confirm that 47 of them exhibit both thermodynamic and dynamic stability. Their potential for exfoliation into bimetallic i-MBenes is investigated by bonding analysis. Leveraging these theoretical insights, a bimetallic i-MBene is successfully synthesized, namely 2D Mo<sub>2</sub>ErB<sub>3</sub>T<sub>2.5</sub> (T = F, Cl and O). Further experimental scrutiny reveals its excellent performance for the hydrogen evolution reaction (HER), highlighting the application potential of bimetallic i-MBenes. Bimetallic strip ; Materials science ; Crystallography ; Exfoliation joint ; Hexagonal crystal system ; Realization (probability) ; Phase (matter) ; Nanotechnology ; Physical chemistry ; Chemistry ; Metal ; Graphene ; Metallurgy MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Mohit Verma | Ankita Kumari | Gaurav Bahuguna | Vikas Singh | Vishakha Pareek | Anandita Dhamija | Shubhendra Shukla | Dibyajyoti Ghosh | Ritu Gupta
Source: ACS Applied Nano Materials (article, 2024-04-16)
Field: Chemistry — Electrochemistry

SnO<sub>2</sub>–MWCNT and SnO<sub>2</sub>–rGO Nanocomposites for Selective Electrochemical Detection in a Mixture of Heavy Metal Ions Metal oxide–carbon nanocomposites offer an interesting platform for electrochemical sensing due to the synergistic effect of a highly active semiconducting surface and conducting carbon as the supporting backbone. In this work, the in situ synthesis of SnO2 with reduced graphene oxide (rGO) led to the formation of small, uniform SnO2 nanoparticles, measuring 10–20 nm in size, whereas the inclusion of multiwalled carbon nanotubes (MWCNT) resulted in the formation of (200) oriented SnO2 nanoplatelets of ∼200 nm. X-ray photoelectron spectroscopy (XPS) demonstrates a chemical interaction between Sn and C rather than physical adherence. The cyclic voltammograms (CVs) of SnO2–rGO and SnO2–MWCNT display high peak current density and small ΔE in comparison to SnO2, signifying fast electron transfer, reversibility, and enhanced electrochemically active sites. Under optimized experimental conditions of square wave anodic stripping voltammetry (SWASV), the nanocomposites demonstrate high sensitivity (3.9, 9.9, 45.5, and 25.4 mA cm–1 ppb–1) and a low detection limit (in ppb) toward Cd2+, Pb2+, Cu2+, and Hg2+, respectively. The high selectivity of SnO2–rGO for Cd2+ and Pb2+ ions and SnO2–MWCNT for Hg2+ and Cu2+ in a complex metal ion environment is encouraging and is probed by using density functional theory (DFT). Additionally, an artificial neural network (ANN)-based model justifies the sensor’s accuracy and precision for real-time, on-site detection of heavy metal ions directly in tap water. Graphene ; Materials science ; Nanocomposite ; X-ray photoelectron spectroscopy ; Oxide ; Electrochemistry ; Carbon nanotube ; Metal ions in aqueous solution ; Metal ; Detection limit ; Cyclic voltammetry ; Electrochemical gas sensor ; Chemical engineering ; Analytical Chemistry (journal) ; Inorganic chemistry ; Nanotechnology ; Electrode ; Chemistry ; Physical chemistry ; Organic chemistry Electrochemical Analysis and Applications ; Gas Sensing Nanomaterials and Sensors ; Analytical Chemistry and Sensors
Authors: Lena Pilz | Meike Koenig | Matthias Schwotzer | Hartmut Gliemann | Christof Wöll | Manuel Tsotsalas
Source: Advanced Functional Materials (article, 2024-06-03)
Field: Chemistry — Inorganic Chemistry

Enhancing the Quality of MOF Thin Films for Device Integration Through Machine Learning: A Case Study on HKUST‐1 SURMOF Optimization Abstract Metal–organic Frameworks (MOFs), especially as thin films, are increasingly recognized for their potential in device integration, notably in sensors and photo detectors. A critical factor in the performance of many MOF‐based devices is the quality of the MOF interfaces. Achieving MOF thin films with smooth surfaces and low defect densities is essential. Given the extensive parameter space governing MOF thin film deposition, the use of machine learning (ML) methods to optimize deposition conditions is highly beneficial. Combined with robotic fabrication, ML can more effectively explore this space than traditional methods, simultaneously varying multiple parameters to improve optimization efficiency. Importantly, ML can provide deeper insights into the synthesis of MOF thin films, an essential area of research. This study focuses on refining an HKUST‐1 SURMOF (surface‐mounted MOF) to achieve minimal surface roughness and high crystallinity, including a quantitative analysis of the importance of the various synthesis parameters. Using the SyCoFinder ML technique, thin film surface quality is markedly enhanced in just three generations created by a genetic algorithm, covering 30 distinct parameter sets. This method greatly reduces the need for extensive experimentation. Moreover, the results enhance the understanding of the vast synthesis parameter space in HKUST‐1 SURMOF growth and broaden the applications of MOF thin films in electronic and optoelectronic devices. Materials science ; Quality (philosophy) ; Thin film ; Nanotechnology Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Machine Learning in Materials Science
Authors: Xiang Li | Jia-Cheng Huang | Guang-Ze Zhang | Hao-En Li | Zhu-Ping Shen | Chen Zhao | Jun Li | Han‐Shi Hu
Source: The Journal of Chemical Physics (article, 2024-06-17)
Field: Materials Science — Materials Chemistry

Improved optimization for the neural-network quantum states and tests on the chromium dimer The advent of Neural-network Quantum States (NQS) has significantly advanced wave function ansatz research, sparking a resurgence in orbital space variational Monte Carlo (VMC) exploration. This work introduces three algorithmic enhancements to reduce computational demands of VMC optimization using NQS: an adaptive learning rate algorithm, constrained optimization, and block optimization. We evaluate the refined algorithm on complex multireference bond stretches of H2O and N2 within the cc-pVDZ basis set and calculate the ground-state energy of the strongly correlated chromium dimer (Cr2) in the Ahlrichs SV basis set. Our results achieve superior accuracy compared to coupled cluster theory at a relatively modest CPU cost. This work demonstrates how to enhance optimization efficiency and robustness using these strategies, opening a new path to optimize large-scale restricted Boltzmann machine-based NQS more effectively and marking a substantial advancement in NQS's practical quantum chemistry applications. Ansatz ; Robustness (evolution) ; Computer science ; Basis set ; Quantum computer ; Quantum Monte Carlo ; Monte Carlo method ; Artificial neural network ; Mathematical optimization ; Quantum ; Computational science ; Algorithm ; Artificial intelligence ; Mathematics ; Density functional theory ; Physics ; Quantum mechanics ; Chemistry Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies