jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hoang Hiep | Pham Tuan Anh | Van‐Duong Dao | Đặng Viết Quang
Source: Journal of Analytical Methods in Chemistry (article, 2023-07-11)
Field: Chemistry — Organic Chemistry

Greener Method for the Application of TiO2 Nanoparticles to Remove Herbicide in Water TiO<sub>2</sub> nanoparticles have emerged as a great photocatalyst to degrade organic contaminants in water; however, the nanoparticles dispersed in water could be difficult to be recovered and potentially become contaminant. Herbicide like 2,4-dichlorophenoxyacetic acid (2,4-D) used in agriculture usually ends up with a large fraction remaining in water and sediment, which may cause potential risk to human health and the ecosystem. This study proposes a greener method to utilize TiO<sub>2</sub> as photocatalyst to remove 2,4-D from water. Accordingly, TiO<sub>2</sub> nanoparticles (10-45 nm) were synthesized and grafted on lightweight fired clay to generate a TiO<sub>2</sub>-based floating photocatalyst. Experimental testing revealed that 60.2% of 2,4-D (0.1 mM) can be decomposed in 250 min under UV light with TiO<sub>2</sub>-grafted lightweight fired clay floating on water. Degradation fits well into the pseudo-first-order kinetic model. The floating photocatalysts can degrade approximately 50% 2,4-D in 250 min under sunlight and the degradation efficiency is stable for cycles. The results revealed that the fabrication of floating photocatalyst could be a promising and greener way to remove herbicide contaminants in water using TiO<sub>2</sub>. Nanoparticle ; Environmental science ; Nanotechnology ; Materials science Nanomaterials for catalytic reactions ; Advanced oxidation water treatment ; Water Quality Monitoring and Analysis
Authors: Piotr Dziurdzia | Piotr Bratek | Michał Markiewicz
Source: Preprints.org (preprint, 2023-11-23)
Field: Materials Science — Materials Chemistry

An Efficient Electrothermal Model of a Thermoelectric Converter for a Thermal Energy Harvesting Process Simulation and Electronic Circuits Powering The paper deals with an electrothermal model of a thermoelectric converter dedicated to performing simulations of coupled thermal and electrical phenomena taking place in harvesting processes. The proposed model is used to estimate the electrical energy gain from waste heat that would be sufficient to supply electronic circuits, in particular autonomous battery-less nodes of wireless sensor networks (WSN) and Internet of Things (IoT) devices. The developed model is not limited to low power electronic solutions such as WSN or IoT; it can also be scaled up and applied to simulations of considerably higher thermal power conversion. In the paper, a few practical case studies are presented that show the feasibility and suitability of the proposed model for complex simultaneous simulation processes, in both electrical and thermal domains. The first example deals with a combined simulation of the electrothermal model of a thermoelectric generator (TEG) and an electronic harvester circuit based on Analog Devices’ power management integrated circuit LTC3108. The second example relates to the thermalization effect in heat sink-less harvesting applications that could be mitigated by a pulse mode operation. Thermoelectric generator ; Heat sink ; Energy harvesting ; Electronic circuit ; Electronics ; Electrical engineering ; Thermoelectric effect ; Thermal energy ; Computer science ; Thermoelectric cooling ; Electronic component ; Electronic engineering ; Power (physics) ; Engineering ; Physics Advanced Thermoelectric Materials and Devices ; Energy Harvesting in Wireless Networks ; Innovative Energy Harvesting Technologies
Authors: Sinem Saar | Fatmanur Tuğcu-Demiröz
Source: Fabad Journal of Pharmaceutical Sciences (article, 2023-04-05)
Field: Materials Science — Biomaterials

Evaluation of Mechanical and Mucoadhesive Properties of Polyvinyl Alcohol Nanofibers As Vaginal Drug Delivery System Electrospinning is a versatile and inexpensive technique to produce nanofibers. Nanofibers can be a good alternative to classical dosage forms in vaginal applications due to high surface area/volume ratio, high encapsulation efficenciacy and mucoadhesive properties. Polyvinyl alcohol (PVA) is a biocompatible, easily degradable and flexible polymer with mucoadhesive properties used in industrial, commercial and medical applications. The scope of this study is to characterize electropsun nanofibers produced with different PVA types for vaginal use. PVA nanofibers were produced using the electrospinning method. Nanofiber formulations were prepared by dissolving PVA in dimethylformamide (DMF):distilled water (1:1) solvent system. Nanofibers were produced with three different types (PVA M 13/140, PVA G 26/140 and PVA G 40/140) PVA at 5%, 7.5% and 10% concentrations. The surface tension, viscosity and conductivity properties of the polymer mixtures were measured for electrospinning process and these parametres were found suitable for nanofiber production. While the viscosity increased with increasing polymer concentration, the surface tension values were found to be close to each other since the solvent system was the same. The mechanical and the mucoadhesive properties of nanofibers were examined and compared. Mucoadhesive and mechanical properties of nanofiber formulations differed depending on molecular weight and electrospinning process. The nanofiber formulations produced with PVA M 13/140 were found suitable for vaginal applications in terms of their mechanical and mucoadhesive properties. PVA nanofibers can be a good alternative as a drug delivery system in vaginal applications. Nanofiber ; Electrospinning ; Polyvinyl alcohol ; Materials science ; Polymer ; Chemical engineering ; Surface tension ; Polymer chemistry ; Composite material Electrospun Nanofibers in Biomedical Applications ; Cardiovascular Issues in Pregnancy
Authors: Thomas Dippong | Erika Andrea Levei | Iosif Grigore Deac | Mihaela D. Lazăr | Oana Cadar
Source: Nanomaterials (article, 2023-01-28)
Field: Materials Science — Materials Chemistry

Influence of SiO2 Embedding on the Structure, Morphology, Thermal, and Magnetic Properties of Co0.4Zn0.4Ni0.2Fe2O4 Particles (Co<sub>0.4</sub>Zn<sub>0.4</sub>Ni<sub>0.2</sub>Fe<sub>2</sub>O<sub>4</sub>)<sub>α</sub>(SiO<sub>2</sub>)<sub>(100-α)</sub> samples obtained by embedding Co<sub>0.4</sub>Zn<sub>0.4</sub>Ni<sub>0.2</sub>Fe<sub>2</sub>O<sub>4</sub> nanoparticles in SiO<sub>2</sub> in various proportions were synthesized by sol-gel process and characterized using thermal analysis, Fourier-transform infrared spectroscopy, X-ray diffraction, transmission electron microscopy, inductively coupled plasma optical emission spectrometry, and magnetic measurements. Poorly crystalline Co-Zn-Ni ferrite at low annealing temperatures (500 °C) and highly crystalline Co-Zn-Ni ferrite together with traces of crystalline Fe<sub>2</sub>SiO<sub>4</sub> (800 °C) and SiO<sub>2</sub> (tridymite and cristobalite) (1200 °C) were obtained. At 1200 °C, large spherical particles with size increasing with the ferrite content (36-120 nm) were obtained. Specific surface area increased with the SiO<sub>2</sub> content and decreased with the annealing temperature above 500 °C. Magnetic properties were enhanced with the increase in ferrite content and annealing temperature. Materials science ; Annealing (glass) ; Tridymite ; Ferrite (magnet) ; Transmission electron microscopy ; Analytical Chemistry (journal) ; Fourier transform infrared spectroscopy ; Diffraction ; Inductively coupled plasma ; Scanning electron microscope ; Mineralogy ; Cristobalite ; Composite material ; Chemical engineering ; Plasma ; Nanotechnology ; Optics ; Quartz ; Chemistry ; Chromatography Magnetic Properties and Synthesis of Ferrites ; Iron oxide chemistry and applications ; Electromagnetic wave absorption materials
Authors: Hanyu Zheng | Quan Liu | Ivan I. Kravchenko | Xiaomeng Zhang | Yuankai Huo | Jason Valentine
Source: arXiv (Cornell University) (preprint, 2023-06-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Intelligent Multi-channel Meta-imagers for Accelerating Machine Vision Rapid developments in machine vision have led to advances in a variety of industries, from medical image analysis to autonomous systems. These achievements, however, typically necessitate digital neural networks with heavy computational requirements, which are limited by high energy consumption and further hinder real-time decision-making when computation resources are not accessible. Here, we demonstrate an intelligent meta-imager that is designed to work in concert with a digital back-end to off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both angle and polarization multiplexing to create multiple information channels that perform positive and negatively valued convolution operations in a single shot. The meta-imager is employed for object classification, experimentally achieving 98.6% accurate classification of handwritten digits and 88.8% accuracy in classifying fashion images. With compactness, high speed, and low power consumption, this approach could find a wide range of applications in artificial intelligence and machine vision applications. Computer science ; Machine vision ; Artificial intelligence ; Computation ; Convolution (computer science) ; Energy consumption ; Artificial neural network ; Multiplexing ; Convolutional neural network ; Computer vision ; Computer engineering ; Engineering ; Electrical engineering ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Advanced Optical Imaging Technologies
Authors: Muhammad Ali | Seneke Chamith Chandrarathna | Seong‐Yeon Moon | Mohammad Sami Jana | Arooba Shafique | Hamdi Qraiqea | Jong‐Wook Lee
Source: Sensors (article, 2023-07-07)
Field: Materials Science — Materials Chemistry

A Compact and Efficient Boost Converter in a 28 nm CMOS with 90 mV Self-Startup and Maximum Output Voltage Tracking ZCS for Thermoelectric Energy Harvesting There are increasing demands for the Internet of Things (IoT), wearable electronics, and medical implants. Wearable devices provide various important daily applications by monitoring real-life human activities. They demand low-cost autonomous operation in a miniaturized form factor, which is challenging to realize using a rechargeable battery. One promising energy source is thermoelectric generators (TEGs), considered the only way to generate a small amount of electric power for the autonomous operation of wearable devices. In this work, we propose a compact and efficient converter system for energy harvesting from TEGs. The system consists of an 83.7% efficient boost converter and a 90 mV self-startup, sharing a single inductor. Innovated techniques are applied to adaptive maximum power point tracking (A-MPPT) and indirect zero current switching (I-ZCS) controllers for efficient operation. The startup circuit is realized using a gain-boosted tri-state buffer, which achieves 69.8% improved gain at the input <i>V</i><sub>IN</sub> = 200 mV compared to the conventional approach. To extract the maximum power, we use an A-MPPT controller based on a simple capacitive divider, achieving 95.2% tracking efficiency. To address the challenge of realizing accurate voltage or current sensors, we propose an I-ZCS controller based on a new concept of maximum output voltage tracking (MOVT). The integrated circuit (IC) is fabricated using a 28 nm CMOS in a compact chip area of 0.03 mm<sup>2</sup>. The compact size, which has not been obtained with previous designs, is suitable for wearable device applications. Measured results show successful startup operation at an ultralow input, <i>V</i><sub>IN</sub> = 90 mV. A peak conversion efficiency of 85.9% is achieved for the output of 1.07 mW. Maximum power point tracking ; Energy harvesting ; Computer science ; CMOS ; Inductor ; Controller (irrigation) ; Electrical engineering ; Voltage ; Boost converter ; Maximum power principle ; Photovoltaic system ; Capacitive sensing ; Power (physics) ; Wearable computer ; Electronic engineering ; Embedded system ; Engineering ; Inverter ; Physics Advanced Thermoelectric Materials and Devices ; Innovative Energy Harvesting Technologies ; Energy Harvesting in Wireless Networks
Authors: Zhihao Dong | Yujin Ji | Youyong Li
Source: Materials Express (article, 2023-04-01)
Field: Materials Science — Materials Chemistry

Accelerated prediction of perovskite material properties with classical machine learning and graph neural network Perovskite materials, possessing a plethora of exceptional properties, have garnered significant attention. Nonetheless, owing to their intricate structure and chemical composition, several obstacles remain in the preparation, characterization, and application of perovskite materials. The rapid advancement of machine learning technologies has expedited research on perovskite materials in recent years. This technique aids researchers in rapidly screening and optimizing the properties of perovskite materials, while also uncovering hidden patterns and trends from vast amounts of experimental and computational data. In this paper, we designed traditional machine learning models built upon manual descriptors, as well as structure-based graph neural network (GNN) models, to precisely forecast various characteristics of perovskite materials. Our results demonstrate that the end-to-end GNN model performs exceptionally well for various properties when sufficient data is available. This highlights the versatility and value of the method in expediting the development of novel perovskite materials. Expediting ; Perovskite (structure) ; Materials science ; Artificial neural network ; Artificial intelligence ; Machine learning ; Graph ; Characterization (materials science) ; Deep learning ; Computer science ; Nanotechnology ; Systems engineering ; Theoretical computer science ; Engineering ; Chemical engineering Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Yunsie Chung | William H. Green
Source: ChemRxiv (preprint, 2023-06-19)
Field: Materials Science — Materials Chemistry

Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates Fast and accurate prediction of solvent effects on reaction rates are crucial for kinetic modeling, chemical process design, and high-throughput solvent screening. Despite the recent advance in machine learning, a scarcity of reliable data has hindered the development of predictive models that are generalizable for diverse reactions and solvents. In this work, we generate a large set of data with the COSMO-RS method for over 28,000 neutral reactions and 295 solvents and train a machine learning model to predict the solvation free energy and solvation enthalpy of activation (ΔΔG‡solv, ΔΔH‡solv) for a solution phase reaction. On unseen reactions, the model achieves mean absolute errors of 0.71 and 1.03 kcal/mol for ΔΔG‡solv and ΔΔH‡solv, respectively, relative to the COSMO-RS calculations. The model also provides reliable predictions of relative rate constants within a factor of 4 when tested on experimental data. The presented model can provide nearly instantaneous predictions of kinetic solvent effects or relative rate constants for a broad range of neutral closed-shell or free radical reactions and solvents only based on atom-mapped reaction SMILES and solvent SMILES strings. Chemistry ; Solvation ; Reaction rate constant ; Solvent ; COSMO-RS ; Reaction rate ; Solvent effects ; Enthalpy ; Thermodynamics ; Computational chemistry ; Kinetics ; Organic chemistry ; Catalysis ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Chonghuan Zhang | Adarsh Arun | Alexei A. Lapkin
Source: ChemRxiv (preprint, 2023-07-20)
Field: Materials Science — Materials Chemistry

Completing and balancing database excerpted chemical reactions with a hybrid mechanistic - machine learning approach Computer Aided Synthesis Planning (CASP) development of reaction routes requires understanding of complete reaction structures. However, most reactions in the current databases are missing reaction co-participants. Although reaction prediction and atom mapping tools can predict major reaction participants and trace atom rearrangements in reactions, they fail to identify the missing molecules to complete reactions. This is because these approaches are data-driven models trained on the current reaction databases which comprise of incomplete reactions. In this work, a workflow was developed to tackle the reaction completion challenge. This includes a heuristic-based method to identify the balanced reactions from reaction databases and complete some imbalanced reactions by adding candidate molecules. A machine learning masked language model (MLM) was trained to learn from reaction SMILES sentences of these completed reactions. The model predicted missing molecules for the incomplete reactions; a workflow analogous to predicting missing words in sentences. The model is promising for prediction of small and middle size missing molecules in incomplete reaction records. The workflow combining both the heuristic and the machine learning methods completed more than half of the entire reaction space. Workflow ; Computer science ; Heuristic ; Missing data ; Artificial intelligence ; Database ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Tayyaba Mubashir | Mudassir Hussain Tahir | Zunaira Shafiq | Ahmed Z. Dewidar | Hosam O. Elansary | Muhammad Ramzan Saeed Ashraf Janjua
Source: Journal of Photochemistry and Photobiology A Chemistry (article, 2023-10-24)
Field: Materials Science — Polymers and Plastics

An efficient framework to design near-IR monomers for polymer solar cells with the help of machine learning, virtual screening and chemical space visualization Chemical space ; Computer science ; Visualization ; Organic solar cell ; Monomer ; Data visualization ; Similarity (geometry) ; Cluster analysis ; Artificial intelligence ; Machine learning ; Data mining ; Polymer ; Materials science ; Chemistry Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Electrochemical sensors and biosensors
Authors: Sayan Basak | M. Alzate Banguero | Lukasz Burzawa | F. Blair Simmons | Pavel Salev | Lionel Aigouy | M. M. Qazilbash | Iván K. Schuller | D. N. Basov | A. Zimmers | E. W. Carlson
Source: Physical review. B./Physical review. B (article, 2023-05-10)
Field: Materials Science — Polymers and Plastics

Deep learning Hamiltonians from disordered image data in quantum materials The capabilities of image probe experiments are rapidly expanding, providing new information about quantum materials on unprecedented length- and timescales. Many such materials feature inhomogeneous electronic properties with intricate pattern formation on the observable surface. This rich spatial structure contains information about interactions, dimensionality, and disorder---a spatial encoding of the Hamiltonian driving the pattern formation. Image recognition techniques from machine learning are an excellent tool for interpreting information encoded in the spatial relationships in such images. Here, we develop a deep learning framework for using the rich information available in these spatial correlations in order to discover the underlying Hamiltonian driving the patterns. We first vet the method on a known case, scanning near-field optical microscopy on a thin film of $\mathrm{V}{\mathrm{O}}_{2}$. We then apply our trained convolutional neural network architecture to new optical microscope images of a different $\mathrm{V}{\mathrm{O}}_{2}$ film as it goes through the metal-insulator transition. We find that a two-dimensional Hamiltonian with both interactions and random field disorder is required to explain the intricate, fractal intertwining of metal and insulator domains during the transition. This detailed knowledge about the underlying Hamiltonian paves the way for using the model to control the pattern formation via, e.g., tailored hysteresis protocols. We also introduce a distribution-based confidence measure on the results of a multilabel classifier, which does not rely on adversarial training. In addition, we propose a machine-learning-based criterion for diagnosing a physical system's proximity to criticality. Hamiltonian (control theory) ; Observable ; Computer science ; Curse of dimensionality ; Convolutional neural network ; Artificial intelligence ; Deep learning ; Classifier (UML) ; Fractal ; Statistical physics ; Quantum ; Criticality ; Pattern recognition (psychology) ; Physics ; Mathematics ; Quantum mechanics Transition Metal Oxide Nanomaterials ; Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing
Authors: Xiaoyuan Wang | Wanying Zhang | Weidong Zhang | Yibo Ai
Source: Materials (article, 2023-01-09)
Field: Materials Science — Metals and Alloys

A Machine Learning Method for Predicting Corrosion Weight Gain of Uranium and Uranium Alloys As an irreplaceable structural and functional material in strategic equipment, uranium and uranium alloys are generally susceptible to corrosion reactions during service, and predicting corrosion behavior has important research significance. There have been substantial studies conducted on metal corrosion research. Accelerated experiments can shorten the test time, but there are still differences in real corrosion processes. Numerical simulation methods can avoid radioactive experiments, but it is difficult to fully simulate a real corrosion environment. The modeling of real corrosion data using machine learning methods allows for effective corrosion prediction. This research used machine learning methods to study the corrosion of uranium and uranium alloys in air and established a corrosion weight gain prediction model. Eleven classic machine learning algorithms for regression were compared and a ten-fold cross validation method was used to choose the highest accuracy algorithm, which was the extra trees algorithm. Feature selection methods, including the extra trees and Pearson correlation analysis methods, were used to select the most important four factors in corrosion weight gain. As a result, the prediction accuracy of the corrosion weight gain prediction model was 96.8%, which could determine a good prediction of corrosion for uranium and uranium alloys. Corrosion ; Uranium ; Materials science ; Metallurgy ; Artificial intelligence ; Computer science Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Non-Destructive Testing Techniques