jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nannan Wang | Wenhui Zou | Xinyue Li | Yaqi Liang | Peng Wang
Source: RSC Advances (review, 2022-01-01)
Field: Chemistry — Organic Chemistry

Study and application status of the nonthermal effects of microwaves in chemistry and materials science – a brief review Microwaves (MWs) are widely known and used in human life and production activities based on their thermal effects. In contrast, their nonthermal effects are still under debate. Fortunately, the nonthermal effects of MWs have been investigated by an increasing number of researchers and have shown great potential in industrial production. In this review, typical studies that demonstrate the nonthermal effects of MWs in chemistry and materials science are introduced and discussed, and the applications of and the harms that are caused by these effects are summarized to facilitate the safe use of these MW effects. The mechanisms of the nonthermal effects of MWs that have been proposed by researchers with various backgrounds are presented. Because some researchers did not detect nonthermal effects of MWs, four typical relevant studies are identified and introduced. Various types of MW reactors (single-mode and multimode reactors and reactors without a MW cavity) are summarized and compared. Finally, possible directions for future research on the nonthermal effects of MWs are proposed. Microwave ; Chemistry ; Nonthermal plasma ; Engineering physics ; Process engineering ; Physics ; Engineering ; Plasma ; Nuclear physics Microwave-Assisted Synthesis and Applications ; Multicomponent Synthesis of Heterocycles ; Nanomaterials for catalytic reactions
Authors: Ashutosh Giri | C. Jaymes Dionne | Patrick E. Hopkins
Source: npj Computational Materials (article, 2022-04-04)
Field: Materials Science — Materials Chemistry

Atomic coordination dictates vibrational characteristics and thermal conductivity in amorphous carbon Abstract We discuss the role of atomic coordination in dictating the vibrational characteristics and thermal conductivity in amorphous carbon. Our systematic atomistic simulations on amorphous carbon structures at varying mass densities show the significant role played by the ratio of sp 2 to sp 3 hybridized bonds in dictating the contributions from propagating (phonon-like) and non-propagating vibrational modes and their influence on the overall thermal conductivities of the structures. Specifically, our results show that as the concentration of sp 3 -bonded carbon atoms increases, the thermal conductivity can be increased by four fold, which is attributed to enhanced contributions from propagating modes in these amorphous structures. Our results shed more light into the role of atomic coordination on dictating heat transfer mechanisms in amorphous materials, and also provide a deeper understanding of the ability to tune the thermal conductivity of amorphous carbon structures through the control of the local atomic coordination. Amorphous carbon ; Thermal conductivity ; Amorphous solid ; Phonon ; Materials science ; Molecular vibration ; Coordination number ; Carbon fibers ; Chemical physics ; Thermal ; Atomic mass ; Crystallography ; Chemistry ; Molecule ; Condensed matter physics ; Atomic physics ; Thermodynamics ; Composite material ; Organic chemistry ; Physics ; Composite number Thermal properties of materials ; Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials
Authors: Alsha Subash | Minoo Naebe | Xungai Wang | Balasubramanian Kandasubramanian
Source: Environmental Science Advances (article, 2022-12-28)
Field: Materials Science — Biomaterials

Fabrication of biodegradable fibrous systems employing electrospinning technology for effluent treatment The fabrication of biopolymer fibers through electrospinning technology with the elucidation of the adsorption and biodegradation mechanism of the employed electrospun biopolymer fibers in wastewater treatment for the removal of toxic entities. Biopolymer ; Electrospinning ; Biodegradation ; Fabrication ; Adsorption ; Materials science ; Sewage treatment ; Effluent ; Fiber ; Nanotechnology ; Chemical engineering ; Waste management ; Composite material ; Chemistry ; Polymer ; Engineering ; Organic chemistry Electrospun Nanofibers in Biomedical Applications ; Microplastics and Plastic Pollution ; biodegradable polymer synthesis and properties
Authors: Hongyu Tang | Sergey G. Menabde | Tarique Anwar | Hak Jun Kim | Min Seok Jang | Giulia Tagliabue
Source: Nanophotonics (review, 2022-01-13)
Field: Materials Science — Materials Chemistry

Photo‐modulated optical and electrical properties of graphene Photo-modulation is a promising strategy for contactless and ultrafast control of optical and electrical properties of photoactive materials. Graphene is an attractive candidate material for photo-modulation due to its extraordinary physical properties and its relevance to a wide range of devices, from photodetectors to energy converters. In this review, we survey different strategies for photo-modulation of electrical and optical properties of graphene, including photogating, generation of hot carriers, and thermo-optical effects. We briefly discuss the role of nanophotonic strategies to maximize these effects and highlight promising fields for application of these techniques. Graphene ; Materials science ; Ultrashort pulse ; Nanophotonics ; Photodetector ; Modulation (music) ; Nanomaterials ; Optoelectronics ; Nanotechnology ; Optical modulator ; Phase modulation ; Optics ; Physics ; Laser Graphene research and applications ; Plasmonic and Surface Plasmon Research ; 2D Materials and Applications
Authors: Junidah Lamaming | Sariah Saalah | Mariani Rajin | Noor Maizura Ismail | Abu Zahrim Yaser
Source: International Journal of Chemical Engineering (review, 2022-01-01)
Field: Materials Science — Biomaterials

A Review on Bamboo as an Adsorbent for Removal of Pollutants for Wastewater Treatment Water and wastewater treatment are very important for obtaining clean and sanitary water as well as protecting the environment from toxic pollutants. Not only enriched with cellulose and carbon but the abundant resources of bamboo also make it suitable to be utilized as an adsorbent. With the right processing technologies and improvements, the potential of bamboo is unlimited. This study review provides knowledge on the use of bamboo‐based adsorbents for the removal of contaminants and pollutants in wastewater in the form of activated carbon, biochar, and aerogel. This review highlighted bamboo utilization and its relevance as an adsorbent for wastewater treatment. The technologies for the processing and improvement of bamboo as well as the performance of the bamboo‐based adsorbents are also discussed in this study. The adsorption capacity of bamboo has shown improvement with modification and good adsorption capacity achieved with some of the adsorbent being able to be recycled and reused. Bamboo ; Adsorption ; Wastewater ; Biochar ; Activated carbon ; Waste management ; Pollutant ; Bamboo charcoal ; Sewage treatment ; Environmental science ; Pulp and paper industry ; Water treatment ; Chemistry ; Environmental engineering ; Materials science ; Engineering ; Composite material ; Organic chemistry Advanced Cellulose Research Studies ; Adsorption and biosorption for pollutant removal
Authors: Muhammad Faisal Manzoor | Abid Hussain | Nenad Naumovski | Muhammad Modassar Ali Nawaz Ranjha | Nazir Ahmad | Emad Karrar | Bin Xu | Salam A. Ibrahim
Source: Frontiers in Nutrition (review, 2022-07-19)
Field: Chemistry — Analytical Chemistry

A Narrative Review of Recent Advances in Rapid Assessment of Anthocyanins in Agricultural and Food Products Anthocyanins (ACNs) are plant polyphenols that have received increased attention recently mainly due to their potential health benefits and applications as functional food ingredients. This has also created an interest in the development and validation of several non-destructive techniques of ACN assessments in several food samples. Non-destructive and conventional techniques play an important role in the assessment of ACNs in agricultural and food products. Although conventional methods appear to be more accurate and specific in their analysis, they are also associated with higher costs, the destruction of samples, time-consuming, and require specialized laboratory equipment. In this review article, we present the latest findings relating to the use of several spectroscopic techniques (fluorescence, Raman, Nuclear magnetic resonance spectroscopy, Fourier-transform infrared spectroscopy, and near-infrared spectroscopy), hyperspectral imaging, chemometric-based machine learning, and artificial intelligence applications for assessing the ACN content in agricultural and food products. Furthermore, we also propose technical and future advancements of the established techniques with the need for further developments and technique amalgamations. Hyperspectral imaging ; Agriculture ; Biochemical engineering ; Food products ; Computer science ; Nanotechnology ; Biotechnology ; Environmental science ; Artificial intelligence ; Materials science ; Chemistry ; Food science ; Engineering ; Biology Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Metabolomics and Mass Spectrometry Studies
Authors: Lukáš Klein | David Seidl | Jan Fulneček | Lukáš Prokop | Stanislav Mišák | Jiří Dvorský
Source: Expert Systems with Applications (article, 2022-10-01)
Field: Materials Science — Materials Chemistry

Antenna contactless partial discharges detection in covered conductors using ensemble stacking neural networks High impedance faults caused by vegetation are difficult to detect when covered conductors in medium voltage overhead power lines are used. Long-term contact of XLPE insulation with vegetation causes partial discharges (PDs) which damage the insulation. Although a cheap and easy to install, contactless detection method was developed using an antenna, there is a lack of classification algorithms for this method. Only two custom machine learning algorithms have been tested so far, and both rendered unsatisfactory results for the real application. This work investigates the use of neural network algorithms for this problem and the application of heterogeneous stacking ensembles using neural networks. We used real data collected from a number of detection stations in the Czech Republic. Also, we limited ourselves to supporting edge computing using devices such as Edge TPU. We propose the application of a heterogeneous stacking ensemble neural network to classify PDs obtained by the contactless method. The algorithm we propose is based on a stacking ensemble with a novel combination of base learners, and the Wide and Deep neural network is used as a meta-learner. We compared the results of our algorithm with other algorithms designated for time series classification. Also, an ablation study of the ensemble was conducted, and satisfactory results were obtained using the proposed algorithm. The ensemble outperformed all algorithms tested and is usable on the edge using AI HW accelerator as the ensemble is only feedforward and contains only well-used and known layers. This research improves our understanding of the classification of PDs using the contactless PD detection method and also introduces a stacking ensemble of convolutional neural network and autoencoders for a time series classification for the first time. Computer science ; Artificial neural network ; Overhead (engineering) ; Ensemble learning ; Stacking ; Artificial intelligence ; Algorithm ; Enhanced Data Rates for GSM Evolution ; Feedforward neural network ; Machine learning High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Silvia Grassi | Maria Tarapoulouzi | Alessandro D’Alessandro | Sofia Agriopoulou | Lorenzo Strani | Theodoros Varzakas
Source: Foods (review, 2022-12-27)
Field: Chemistry — Analytical Chemistry

How Chemometrics Can Fight Milk Adulteration Adulteration and fraud are amongst the wrong practices followed nowadays due to the attitude of some people to gain more money or their tendency to mislead consumers. Obviously, the industry follows stringent controls and methodologies in order to protect consumers as well as the origin of the food products, and investment in these technologies is highly critical. In this context, chemometric techniques proved to be very efficient in detecting and even quantifying the number of substances used as adulterants. The extraction of relevant information from different kinds of data is a crucial feature to achieve this aim. However, these techniques are not always used properly. In fact, training is important along with investment in these technologies in order to cope effectively and not only reduce fraud but also advertise the geographical origin of the various food and drink products. The aim of this paper is to present an overview of the different chemometric techniques (from clustering to classification and regression applied to several analytical data) along with spectroscopy, chromatography, electrochemical sensors, and other on-site detection devices in the battle against milk adulteration. Moreover, the steps which should be followed to develop a chemometric model to face adulteration issues are carefully presented with the required critical discussion. Chemometrics ; Context (archaeology) ; Computer science ; Order (exchange) ; Biochemical engineering ; Cluster analysis ; Business ; Artificial intelligence ; Machine learning ; Engineering ; Geography Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Zheli Wang | Wenqian Huang | Xi Tian | Long Yuan | Lianjie Li | Shuxiang Fan
Source: Frontiers in Plant Science (article, 2022-05-10)
Field: Chemistry — Analytical Chemistry

Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods The aged seeds have a significant influence on seed vigor and corn growth. Therefore, it is vital for the planting industry to identify aged seeds. In this study, hyperspectral reflectance imaging (1,000–2,000 nm) was employed for identifying aged maize seeds using seeds harvested in different years. The average spectra of the embryo side, endosperm side, and both sides were extracted. The support vector machine (SVM) algorithm was used to develop classification models based on full spectra to evaluate the potential of hyperspectral imaging for maize seed detection and using the principal component analysis (PCA) and ANOVA to reduce data dimensionality and extract feature wavelengths. The classification models achieved perfect performance using full spectra with an accuracy of 100% for the prediction set. The performance of models established with the first three principal components was similar to full spectrum models, but that of PCA loading models was worse. Compared to other spectra, the two-band ratio (1,987 nm/1,079 nm) selected by ANOVA from embryo-side spectra achieved a better classification accuracy of 95% for the prediction set. The image texture features, including histogram statistics (HS) and gray-level co-occurrence matrix (GLCM), were extracted from the two-band ratio image to establish fusion models. The results demonstrated that the two-band ratio selected from embryo-side spectra combined with image texture features achieved the classification of maize seeds harvested in different years with an accuracy of 97.5% for the prediction set. The overall results indicated that combining the two wavelengths with image texture features could detect aged maize seeds effectively. The proposed method was conducive to the development of multi-spectral detection equipment. Principal component analysis ; Hyperspectral imaging ; Pattern recognition (psychology) ; Artificial intelligence ; Endosperm ; Mathematics ; Support vector machine ; Dimensionality reduction ; Biological system ; Computer science ; Statistics ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Ailian Jiang | Ryozo Noguchi | Tofael Ahamed
Source: Sensors (article, 2022-03-07)
Field: Chemistry — Analytical Chemistry

Tree Trunk Recognition in Orchard Autonomous Operations under Different Light Conditions Using a Thermal Camera and Faster R-CNN In an orchard automation process, a current challenge is to recognize natural landmarks and tree trunks to localize intelligent robots. To overcome low-light conditions and global navigation satellite system (GNSS) signal interruptions under a dense canopy, a thermal camera may be used to recognize tree trunks using a deep learning system. Therefore, the objective of this study was to use a thermal camera to detect tree trunks at different times of the day under low-light conditions using deep learning to allow robots to navigate. Thermal images were collected from the dense canopies of two types of orchards (conventional and joint training systems) under high-light (12-2 PM), low-light (5-6 PM), and no-light (7-8 PM) conditions in August and September 2021 (summertime) in Japan. The detection accuracy for a tree trunk was confirmed by the thermal camera, which observed an average error of 0.16 m for 5 m, 0.24 m for 15 m, and 0.3 m for 20 m distances under high-, low-, and no-light conditions, respectively, in different orientations of the thermal camera. Thermal imagery datasets were augmented to train, validate, and test using the Faster R-CNN deep learning model to detect tree trunks. A total of 12,876 images were used to train the model, 2318 images were used to validate the training process, and 1288 images were used to test the model. The mAP of the model was 0.8529 for validation and 0.8378 for the testing process. The average object detection time was 83 ms for images and 90 ms for videos with the thermal camera set at 11 FPS. The model was compared with the YOLO v3 with same number of datasets and training conditions. In the comparisons, Faster R-CNN achieved a higher accuracy than YOLO v3 in tree truck detection using the thermal camera. Therefore, the results showed that Faster R-CNN can be used to recognize objects using thermal images to enable robot navigation in orchards under different lighting conditions. Trunk ; Tree (set theory) ; Orchard ; Artificial intelligence ; Thermal ; Computer science ; Computer vision ; Computer graphics (images) ; Mathematics ; Physics ; Meteorology ; Horticulture ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Pietro Galizia | Diletta Sciti
Source: Composites Part B Engineering (article, 2022-10-26)
Field: Materials Science — Ceramics and Composites

Disclosing residual thermal stresses in UHT fibre-reinforced ceramic composites and their effect on mechanical behaviour and damage evolution Materials science ; Composite material ; Flexural strength ; Thermal shock ; Thermal expansion ; Bending ; Porosity ; Ceramic matrix composite ; Stiffness ; Residual stress ; Ceramic ; Anisotropy ; Modulus Advanced ceramic materials synthesis ; Advanced materials and composites ; MXene and MAX Phase Materials
Authors: Arun kumar Subramani | Amuthalakshmi Sivaperuman | Ramalakshmi Natarajan | Richie R. Bhandare | Afzal B. Shaik
Source: Molecules (article, 2022-03-11)
Field: Chemistry — Organic Chemistry

QSAR and Molecular Docking Studies of Pyrimidine-Coumarin-Triazole Conjugates as Prospective Anti-Breast Cancer Agents Cancer is a life-threatening disease and is the second leading cause of death worldwide. Although many drugs are available for the treatment of cancer, survival outcomes are very low. Hence, rapid development of newer anticancer agents is a prime focus of the medicinal chemistry community. Since the recent past, computational methods have been extensively employed for accelerating the drug discovery process. In view of this, in the present study we performed 2D-QSAR (Quantitative Structure-Activity Relationship) analysis of a series of compounds reported with potential anticancer activity against breast cancer cell line MCF7 using QSARINS software. The best four models exhibited a <i>r</i><sup>2</sup> value of 0.99. From the generated QSAR equations, a series of pyrimidine-coumarin-triazole conjugates were designed and their MCF7 cell inhibitory activities were predicted using the QSAR equations. Furthermore, molecular docking studies were carried out for the designed compounds using AutoDock Vina against dihydrofolate reductase (DHFR), colchicine and vinblastine binding sites of tubulin, the key enzyme targets in breast cancer. The most active compounds identified through these computational studies will be useful for synthesizing and testing them as prospective novel anti-breast cancer agents. Dihydrofolate reductase ; Quantitative structure–activity relationship ; Docking (animal) ; Breast cancer ; Coumarin ; Chemistry ; AutoDock ; Stereochemistry ; Pyrimidine ; Drug discovery ; Vinblastine ; Combinatorial chemistry ; Computational biology ; Pharmacology ; Enzyme ; Biochemistry ; Cancer ; Biology ; Medicine ; In silico ; Organic chemistry ; Chemotherapy Synthesis and biological activity ; Click Chemistry and Applications ; Computational Drug Discovery Methods
Authors: Jinhie Skarda | Rahul Trivedi | Logan Su | Diego Ahmad-Stein | Hyounghan Kwon | Seung-Hoon Han | Shanhui Fan | Jelena Vučković
Source: npj Computational Materials (article, 2022-04-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Low-overhead distribution strategy for simulation and optimization of large-area metasurfaces Abstract Fast and accurate electromagnetic simulation of large-area metasurfaces remains a major obstacle in automating their design. In this paper, we propose a metasurface simulation distribution strategy which achieves a linear reduction in the simulation time with the number of compute nodes. Combining this distribution strategy with a GPU-based implementation of the Transition-matrix method, we perform accurate simulations and adjoint sensitivity analysis of large-area metasurfaces. We demonstrate ability to perform a distributed simulation of large-area metasurfaces (over 600 λ × 600 λ ), while accurately accounting for scatterer-scatterer interactions significantly beyond the locally periodic approximation. Overhead (engineering) ; Computer science ; Obstacle ; Distribution (mathematics) ; Reduction (mathematics) ; Sensitivity (control systems) ; Electromagnetic simulation ; Matrix (chemical analysis) ; Electronic engineering ; Algorithm ; Computational science ; Mathematics ; Engineering ; Geometry ; Materials science Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis