jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ziming Xin | Qianqian He | Shuangao Wang | Xiaoyu Han | Zhongtian Fu | Xinxin Xu | Xin Zhao
Source: Applied Sciences (review, 2022-08-07)
Field: Materials Science — Materials Chemistry

Recent Progress in ZnO-Based Nanostructures for Photocatalytic Antimicrobial in Water Treatment: A Review Advances in nanotechnology have led to the development of antimicrobial technology of nanomaterials. In recent years, photocatalytic antibacterial disinfection methods with ZnO-based nanomaterials have attracted extensive attention in the scientific community. In addition, recently widely and speedily spread viral microorganisms, such as COVID-19 and monkeypox virus, have aroused global concerns. Traditional methods of water purification and disinfection are inhibited due to the increased resistance of bacteria and viruses. Exploring new and effective antimicrobial materials and methods has important practical application value. This review is a comprehensive overview of recent progress in the following: (i) preparation methods of ZnO-based nanomaterials and comparison between methods; (ii) types of nanomaterials for photocatalytic antibacterials in water treatment; (iii) methods for studying the antimicrobial activities and (iv) mechanisms of ZnO-based antibacterials. Subsequently, the use of different doping strategies to enhance the photocatalytic antibacterial properties of ZnO-based nanomaterials is also emphatically discussed. Finally, future research and practical applications of ZnO-based nanomaterials for antibacterial activity are proposed. Nanomaterials ; Nanotechnology ; Antimicrobial ; Photocatalysis ; Water disinfection ; Materials science ; Chemistry ; Environmental science ; Environmental engineering ; Catalysis Nanoparticles: synthesis and applications ; Advanced Photocatalysis Techniques ; Advanced Nanomaterials in Catalysis
Authors: Marc T. M. Koper | Adam Z. Weber | Karen Chan | Jun Cheng
Source: Chemical Reviews (review, 2022-06-22)
Field: Chemistry — Electrochemistry

Introduction: Computational Electrochemistry ADVERTISEMENT RETURN TO ISSUEEditorialNEXTIntroduction: Computational ElectrochemistryMarc T. M. Koper*Marc T. M. KoperLeiden University, PO Box 9502, Leiden 2300 RA, Netherlands*Email: [email protected]More by Marc T. M. Koperhttps://orcid.org/0000-0001-6777-4594, Adam Z. Weber*Adam Z. WeberLawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 70-108B, Berkeley, California 94720, United States*Email: [email protected]More by Adam Z. Weberhttps://orcid.org/0000-0002-7749-1624, Karen Chan*Karen ChanTechnical University of Denmark, Building 311, Room 004, Lyngby 2800, Denmark*Email: [email protected]More by Karen Chanhttps://orcid.org/0000-0002-6897-1108, and Jun Cheng*Jun ChengXiamen University, 422 Siming South Road, Xiamen 361005, China*Email: [email protected]More by Jun Chenghttps://orcid.org/0000-0001-6971-0797Cite this: Chem. Rev. 2022, 122, 12, 10579–10580Publication Date (Web):June 22, 2022Publication History Published online22 June 2022Published inissue 22 June 2022https://pubs.acs.org/doi/10.1021/acs.chemrev.2c00368https://doi.org/10.1021/acs.chemrev.2c00368editorialACS PublicationsCopyright © Published 2022 by American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views8022Altmetric-Citations1LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (634 KB) Get e-AlertscloseSUBJECTS:Batteries,Charge transfer,Electrochemistry,Electrolytes,Layers Get e-Alerts Citation ; Library science ; Web of science ; World Wide Web ; Computer science ; MEDLINE ; Chemistry Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Mehmet Emin Ergün
Source: BioResources (article, 2022-12-21)
Field: Materials Science — Polymers and Plastics

Activated carbon and cellulose-reinforced biodegradable chitosan foams Chitosan foams with promising mechanical properties, heat-insulating ability, and flame retardancy were produced through oven drying. The chitosan foams were reinforced with cellulose, boric acid, and different ratios of activated carbon. The foams showed desirable low density (80.2 to 109.8 kg/m3) and compression properties. The compression resistance and compression modulus of foams ranged between 53.6 and 98.5 KPa and 214 to 394 KPa, respectively. Thermal conductivity tests revealed that the foams endowed low thermal conductivity values (0.035 to 0.051 W/mK). The limiting oxygen index (LOI) of the foams was as high as 32.9% for activated carbon (20 g/L). The activated carbon reinforcement produced higher thermal properties and decreased the mass loss 48.1% at 600 °C. The produced foams exhibited good biodegradability (39% degradation in 15 days). The overall test results showed that the chitosan foams can be utilized as a promising environmentally friendly material in thermal insulation fields. Materials science ; Composite material ; Chitosan ; Limiting oxygen index ; Activated carbon ; Thermal conductivity ; Cellulose ; Compressive strength ; Biodegradation ; Chemical engineering ; Adsorption ; Organic chemistry ; Chemistry ; Pyrolysis ; Char Polymer Foaming and Composites ; Aerogels and thermal insulation ; Flame retardant materials and properties
Authors: Maryam Darvish | Navid Nasrabadi | Farnoush Fotovat | Setareh Khosravi | Mehrdad Khatami | Samira Jamali | Elnaz Mousavi | Siavash Iravani | Abbas Rahdar
Source: Scientific Reports (article, 2022-06-08)
Field: Chemistry — Organic Chemistry

RETRACTED ARTICLE: Biosynthesis of Zn-doped CuFe2O4 nanoparticles and their cytotoxic activity Zn-doped CuFe<sub>2</sub>O<sub>4</sub> nanoparticles (NPs) were eco-friendly synthesized using plant extract. These nanoparticles were characterized by X-ray diffraction, Fourier-transform infrared spectroscopy, scanning electron microscope (SEM), energy-dispersive X-ray spectroscopy and thermal gravimetric analysis (TGA). SEM image showed spherical NPs with size range less than 30 nm. In the EDS diagram, the elements of zinc, copper, iron, and oxygen are shown. The cytotoxicity and anticancer properties of Zn-doped CuFe<sub>2</sub>O<sub>4</sub> NPs were evaluated on macrophage normal cells and A549 lung cancer cells. The cytotoxic effects of Zn-doped CuFe<sub>2</sub>O<sub>4</sub> and CuFe<sub>2</sub>O<sub>4</sub> NPs on A549 cancer cell lines were analyzed. The Zn-doped CuFe<sub>2</sub>O<sub>4</sub> and CuFe<sub>2</sub>O<sub>4</sub> NPs demonstrated IC<sub>50</sub> values 95.8 and 278.4 µg/mL on A549 cancer cell, respectively. Additionally, Zn-doped CuFe<sub>2</sub>O<sub>4</sub> and CuFe<sub>2</sub>O<sub>4</sub> NPs had IC<sub>80</sub> values of 8.31 and 16.1 µg/mL on A549 cancer cell, respectively. Notably, doping Zn on CuFe<sub>2</sub>O<sub>4</sub> NPs displayed better cytotoxic effects on A549 cancer cells compared with the CuFe<sub>2</sub>O<sub>4</sub> NPs alone. Also spinel nanocrystals of Zn-doped CuFe<sub>2</sub>O<sub>4</sub> (~ 13 nm) had a minimum toxicity (CC<sub>50</sub> = 136.6 µg/mL) on macrophages J774 Cell Line. Cytotoxic T cell ; Biosynthesis ; Nanoparticle ; Chemistry ; Computational biology ; Biochemistry ; Biology ; Nanotechnology ; Materials science ; Gene ; In vitro Nanomaterials for catalytic reactions ; Nanoparticle-Based Drug Delivery ; Magnetic Properties and Synthesis of Ferrites
Authors: Afsaneh Ghahari | ‪Heidar Raissi | Samaneh Pasban | Farzaneh Farzad
Source: npj Clean Water (article, 2022-07-07)
Field: Materials Science — Materials Chemistry

Proposing two-dimensional covalent organic frameworks material for the capture of phenol molecules from wastewaters Abstract Industrial wastewater organic pollutants such as phenol can be treated through adsorption on active surfaces. Herein, the adsorption mechanism and dynamic behaviors of phenol molecules onto covalent organic frameworks (COFs) with well-defined supramolecular structures are investigated via molecular dynamics and well-tempered metadynamics simulations under various external electric fields. The Lenard–Jones interaction is predominant during the adsorption process, while NH and OH groups in COFs and phenol, respectively, can increase the adsorption due to the electrostatic interaction. Besides, the adsorption affinity of phenol on COFs is weakened by increasing the electric field strength. In addition, the free energy values for the complexes with and without the external electric field at their global minima reached at about −264.68, −248.33, and −290.13 (for 1, 0.5, and 0 V nm −1 ) kJ mol −1 , respectively. The obtained results confirmed the COFs as prominent adsorbents for loading phenol and its removal from the water-contaminated environment. Adsorption ; Phenol ; Metadynamics ; Covalent bond ; Supramolecular chemistry ; Molecule ; Chemistry ; Chemical engineering ; Electric field ; Molecular dynamics ; Organic chemistry ; Computational chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Inzir Raupov | Рамис Нурутдинович Бурханов | Azat Lutfullin | Alexander Valeryevich Maksyutin | А. Б. Лебедев | Elena Safiullina
Source: Energies (article, 2022-05-16)
Field: Chemistry — Analytical Chemistry

Experience in the Application of Hydrocarbon Optical Studies in Oil Field Development This article reviews the results of measurement of optical properties of oil, such as polarimetry, refractometric, luminescent-bituminological research, IR-spectrometry and UV-visible-NIR spectrometry used to solve geo-bituminology development of hydrocarbon deposits. The authors pay special attention to optical research in the field of UV-visible-NIR electromagnetic radiation, the results of which allow us to estimate the residual oil reserves, separate production for each formation during the operation of multi-layer objects, determine the producing gas-oil ratio, density and content of hydrocarbons, efficiency of hydraulic fracturing, flow-reducing technologies, and injection of solvents of heavy oil sediments, etc. The published approaches to methods of optical research, which are carried out by laboratories or in-well devices, have been analyzed. This article analyzes the main advantages and disadvantages of current technologies for determining the optical properties of oil. The authors propose wellhead devices for determining the optical properties of oil in UV-visible-NIR radiation (190–1100 nm) and their functional schemes, with a description of the operating principle. Wellhead ; Petroleum engineering ; Fossil fuel ; Hydrocarbon ; Light crude oil ; Oil field ; Environmental science ; Process engineering ; Residual oil ; Materials science ; Chemistry ; Geology ; Waste management ; Engineering ; Organic chemistry Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Oil and Gas Production Techniques
Authors: Tiago L. P. Galvão | Inês Ferreira | Alena Kuznetsova | Gerard Novell-Leruth | Ci Song | Christian Feiler | Sviatlana V. Lamaka | Cláudia Rocha | Frederico Maia | Mikhail L. Zheludkevich | José R. B. Gomes | João Tedim
Source: npj Materials Degradation (article, 2022-06-17)
Field: Materials Science — Materials Chemistry

CORDATA: an open data management web application to select corrosion inhibitors Abstract The large amount of corrosion inhibition efficiencies in literature, calls for a more efficient way to organize, access and compare this information. The CORDATA open data management application ( https://datacor.shinyapps.io/cordata/ ) can help select appropriate corrosion inhibitors for application specific challenges. Corrosion ; Computer science ; Open source ; World Wide Web ; Data science ; Information retrieval ; Software ; Chemistry ; Operating system Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Man Ding | Weifeng He | Jiahe Wang | Jinpeng Wang
Source: Polymers (article, 2022-06-03)
Field: Materials Science — Materials Chemistry

Performance Evaluation of Cross-Linked Polyethylene Insulation of Operating 110 kV Power Cables The ageing characteristic of XLPE insulation of operating a 110 kV power cable with different service time is studied in this paper. The microscopic morphology of XLPE films from different cables were characterized by using Differential Scanning Calorimetry (DSC), X-ray Diffraction method (XRD), and Fourier Transform Infrared Spectroscopy (FTIR) methods, and the dielectric, mechanical, and electrical properties of XLPE were also measured. The relationship of several typical property parameters with the cable service time were established, and the ageing mechanism of XLPE insulation of the operating cable was also analyzed. It was found that XLPE insulation would endure a recrystallization process in the initial operation stage during which the microscopic morphology would become more perfect with higher crystallinity and denser crystal structure. Then, the thermal oxidation would dominate the ageing process of XLPE with the molecular chains broken and more micromolecular products generated after the cable had operated for more than 10-15 years. The AC breakdown strength decreases with the increase of cable service time, with lower decreasing rate in the initial operation stage and a larger rate after 10-15 years. The Pearson correlation coefficient between the cable service time with the characteristic parameters were calculated, and some of them were found to be effective to be used as indicators for operation state detection of operating power cables. Crystallinity ; Materials science ; Composite material ; Cross-linked polyethylene ; Differential scanning calorimetry ; Polyethylene ; Fourier transform infrared spectroscopy ; Power cable ; Service life ; Dielectric ; Operating temperature ; Forensic engineering ; Electrical engineering ; Optoelectronics ; Optics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Sara Ranjbari | Mahdieh Darroudi | Behnaz Hatamluyi | Reza Arefinia | Seyed Hamid Aghaee‐Bakhtiari | Majid Rezayi | Majid Khazaei
Source: Frontiers in Bioengineering and Biotechnology (review, 2022-08-24)
Field: Materials Science — Materials Chemistry

Application of MXene in the diagnosis and treatment of breast cancer: A critical overview Breast cancer is the second most common cancer worldwide. Prognosis and timely treatment can reduce the illness or improve it. The use of nanomaterials leads to timely diagnosis and effective treatment. MXenes are a 2D material with a unique composition of attributes, containing significant electrical conductance, high optical characteristics, mechanical consistency, and excellent optical properties. Current advances and insights show that MXene is far more promising in biotechnology applications than current nanobiotechnology systems. MXenes have various applications in biotechnology and biomedicine, such as drug delivery/loading, biosensor, cancer treatment, and bioimaging programs due to their high surface area, excellent biocompatibility, and physicochemical properties. Surface modifications MXenes are not only biocompatible but also have multifunctional properties, such as aiming ligands for preferential agglomeration at the tumor sites for photothermal treatment. Studies have shown that these nanostructures, detection, and breast cancer therapy are more acceptable than present nanosystems in <i>in vivo</i> and <i>in vitro</i>. This review article aims to investigate the structure of MXene, its various synthesis methods, its application to cancer diagnosis, cytotoxicity, biodegradability, and cancer treatment by the photothermal process (<i>in-vivo</i> and <i>in-vitro</i>). MXenes ; Photothermal therapy ; Nanotechnology ; Breast cancer ; Biocompatibility ; Materials science ; Cancer ; Biocompatible material ; Biomedical engineering ; Medicine MXene and MAX Phase Materials ; Graphene and Nanomaterials Applications ; Advanced biosensing and bioanalysis techniques
Authors: Na Luo | Yunlong Li | Baohua Yang | Biyun Liu | Qianying Dai
Source: Agriculture (article, 2022-08-25)
Field: Chemistry — Analytical Chemistry

Prediction Model for Tea Polyphenol Content with Deep Features Extracted Using 1D and 2D Convolutional Neural Network The content of tea polyphenols (TP) is one of the important indicators for judging the quality of tea. Accurate and non-destructive estimation technology for tea polyphenol content has attracted more and more attention, which has become a key technology for tea production, quality identification, grading and so on. Hyperspectral imaging technology is a fusion of spectral analysis and image processing technology, which has been proven to be an efficient technology for predicting tea polyphenol content. To make full use of spectral and spatial features, a prediction model of tea polyphenols based on spectral-spatial deep features extracted using convolutional neural network (CNN) was proposed, which not only broke the limitations of traditional shallow features, but also innovated the technical path of integrated deep learning in non-destructive detection for tea. Firstly, one-dimensional convolutional neural network (1D-CNN) and two-dimensional convolutional neural network (2D-CNN) models were constructed to extract the spectral deep features and spatial deep features of tea hyperspectral images, respectively. Secondly, spectral deep features, spatial deep features, and spectral-spatial deep features are used as input variables of machine learning models, including Partial Least Squares Regression (PLSR), Support Vector Regression (SVR) and Random Forest (RF). Finally, the training, testing and evaluation were realized using the self-built hyperspectral dataset of green tea from different grades and different manufacturers. The results showed that the model based on spectral-spatial deep features had the best prediction performance among the three machine learning models (R2 = 0.949, MAE = 0.533 for training sets, R2 = 0.938, MAE = 0.799 for test sets). Moreover, the visualization of estimation results of tea polyphenol content further demonstrated that the model proposed in this study had strong estimation ability. Therefore, the deep features extracted using CNN can provide new ideas for estimation of the main components of tea, which will provide technical support for the estimation tea quality estimation. Hyperspectral imaging ; Convolutional neural network ; Artificial intelligence ; Deep learning ; Computer science ; Partial least squares regression ; Pattern recognition (psychology) ; Artificial neural network ; Random forest ; Support vector machine ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Remote-Sensing Image Classification
Authors: Dengshan Li | Lina Li
Source: Sensors (article, 2022-08-03)
Field: Chemistry — Analytical Chemistry

Detection of Water pH Using Visible Near-Infrared Spectroscopy and One-Dimensional Convolutional Neural Network pH is an important parameter for water quality detection. This study proposed a novel calibration regression strategy based on a one-dimensional convolutional neural network (1D-CNN) for water pH detection using visible near-infrared (Vis-NIR) spectroscopy. Two groups of Vis-NIR spectral analysis experiments of water pH detection were employed to evaluate the performance of 1D-CNN. Two conventional multivariate regression calibration methods, including partial least squares (PLS) and least squares support vector machine (LS-SVM), were introduced for comparative analysis with 1D-CNN. The successive projections algorithm (SPA) was adopted to select the feature variables. In addition, the learning mechanism of 1D-CNN was interpreted through visual feature maps by convolutional layers. The results showed that the 1D-CNN models obtained the highest prediction accuracy based on full spectra for the two experiments. For the spectrophotometer experiment, the root mean square error of prediction (RMSEP) was 0.7925, and the determination coefficient of prediction (Rp2) was 0.8515. For the grating spectrograph experiment, the RMSEP was 0.5128 and the Rp2 was 0.9273. The convolutional layers could automatically preprocess the spectra and effectively extract the spectra features. Compared with the traditional regression methods, 1D-CNN does not need complex spectra pretreatment and variable selection. Therefore, 1D-CNN is a promising regression approach, with higher prediction accuracy and better modeling convenience for rapid water pH detection using Vis-NIR spectroscopy. Partial least squares regression ; Convolutional neural network ; Calibration ; Pattern recognition (psychology) ; Artificial intelligence ; Support vector machine ; Mean squared error ; Feature selection ; Computer science ; Near-infrared spectroscopy ; Feature (linguistics) ; Root mean square ; Spectroscopy ; Biological system ; Mathematics ; Statistics ; Machine learning ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Tao Zhang | Hua Bai | Shuyu Sun
Source: Processes (article, 2022-02-21)
Field: Materials Science — Materials Chemistry

Intelligent Natural Gas and Hydrogen Pipeline Dispatching Using the Coupled Thermodynamics-Informed Neural Network and Compressor Boolean Neural Network Natural gas pipelines have attracted increasing attention in the energy industry thanks to the current demand for green energy and the advantages of pipeline transportation. A novel deep learning method is proposed in this paper, using a coupled network structure incorporating the thermodynamics-informed neural network and the compressor Boolean neural network, to incorporate both functions of pipeline transportation safety check and energy supply predictions. The deep learning model is uniformed for the coupled network structure, and the prediction efficiency and accuracy are validated by a number of numerical tests simulating various engineering scenarios, including hydrogen gas pipelines. The trained model can provide dispatchers with suggestions about the number of phases existing during the transportation as an index showing safety, while the effects of operation temperature, pressure and compositional purity are investigated to suggest the optimized productions. Compressor station ; Artificial neural network ; Pipeline (software) ; Pipeline transport ; Gas compressor ; Natural gas ; Computer science ; Energy (signal processing) ; Artificial intelligence ; Engineering ; Mechanical engineering ; Mathematics Material Properties and Failure Mechanisms ; Hydrogen embrittlement and corrosion behaviors in metals ; Structural Integrity and Reliability Analysis