jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pimonpan Kaewprachu | Chalalai Jaisan
Source: Polymers (article, 2023-06-26)
Field: Materials Science — Biomaterials

Physicochemical Properties of Chitosan from Green Mussel Shells (Perna viridis): A Comparative Study Green mussel shells (<i>Perna viridis</i>) are generated in huge amounts and discarded as waste materials. Such waste may be used to produce biopolymer materials such as chitosan. The physicochemical properties of chitosan prepared from different sizes of green mussel shells (small size (CHS): ≤5.00 cm in length and big size (CHB): >5.01 cm in length) were characterized and compared with commercial chitosan (CH). Furthermore, the mechanical and physicochemical properties of the blended films were also investigated. The results of the physicochemical properties showed that CHS and CHB were quite different from CH. The degree of deacetylation of CHS, CHB, and CH was found to be 32.71%, 52.56%, and 70.42%, respectively (<i>p</i> < 0.05). The water- and fat-binding capacities of CH were higher than those of CHS and CHB. Structural differences between CHS, CHB, and CH were studied using Fourier transform infrared spectroscopy (FTIR) and X-ray powder diffraction (XRD). Significant increases in thickness, water vapor permeability, and strength of the blended films were found when the extracted chitosan was added (<i>p</i> < 0.05). However, further study is needed to improve the chitosan extraction process, which can enhance the physicochemical properties of the obtained chitosan and be widely used in many industries. Perna viridis ; Mussel ; Chitosan ; Fishery ; Chemistry ; Biology ; Organic chemistry Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Seaweed-derived Bioactive Compounds
Authors: Penghan Zhang | Maurizio Piergiovanni | Pietro Franceschi | Fulvio Mattivi | Urška Vrhovšek | Silvia Carlin
Source: Analytica—A Journal of Analytical Chemistry and Chemical Analysis (article, 2023-08-15)
Field: Chemistry — Spectroscopy

Application of Comprehensive 2D Gas Chromatography Coupled with Mass Spectrometry in Beer and Wine VOC Analysis To meet consumer demand for fermented beverages with a wide range of flavors, as well as for quality assurance, it is important to characterize volatiles and their relationships with raw materials, microbial and fermentation processes, and the aging process. Sample preparation techniques coupled with comprehensive 2D gas chromatography (GC×GC) and mass spectrometry (MS) are proven techniques for the identification and quantification of various volatiles in fermented beverages. A few articles discuss the application of GC×GC for the measurement of fermented beverage volatiles and the problems faced in the experimental analysis. This review critically discusses each step of GC×GC-MS workflow in the specific context of fermented beverage volatiles’ research, including the most frequently applied volatile extraction techniques, GC×GC instrument setup, and data handling. The application of novel sampling techniques to shorten preparation times and increase analytical sensitivity is discussed. The pros and cons of thermal and flow modulators are evaluated, and emphasis is given to the use of polar-semipolar configurations to enhance detection limits. The most relevant Design of Experiment (DoE) strategies for GC×GC parameter optimization as well as data processing procedures are reported and discussed. Finally, some consideration of the current state of the art and future perspective, including the crucial role of AI and chemometrics. Wine ; Mass spectrometry ; Process engineering ; Chemometrics ; Gas chromatography ; Chromatography ; Chemistry ; Context (archaeology) ; Gas chromatography–mass spectrometry ; Biochemical engineering ; Food science ; Engineering Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Fermentation and Sensory Analysis
Authors: Ana Rita Silva | Manuel Ayuso | Oludemi Taofiq | Alexandre Gonçalves | Bruno Melgar | Lillian Barros
Source: Separation and Purification Technology (article, 2023-10-12)
Field: Chemistry — Organic Chemistry

Response surface methodology and artificial neural network modeling as predictive tools for phenolic compounds recovery from olive pomace This study optimized the extraction of three major phenolic compounds (oleuropein, tyrosol, and verbascoside) from olive pomace using microwave- and ultrasonic-assisted methods. Screening factorial design (SFD) and central composite design (CCD) were employed, and response surface methodology (RSM) and artificial neural networks (ANN) were used for data modeling. The microwave-assisted method in the SFD yielded higher compound amounts, with verbascoside showing a four-fold increase compared to the ultrasonic-assisted method. Factors like vessel diameter, ultrasonic power using UAE, and solvent acidity in both techniques had minimally impacted extractability. CCD-RSM revealed temperaturés significantly affect on oleuropein, but improved tyrosol recovery, with the effect on verbascoside being influenced by the temperature range. RSM and ANN integration enhanced understanding and prediction of factor behavior. Microwave-assisted extraction at 113 °C for 26 min, with minimum ramp time of 7.7 min, yielded 67.4, 57, and 5.1 mg of oleuropein, tyrosol, and verbascoside per gram of extract, respectively, with a prediction error ranging from 0.83 to 15.19. Verbascoside ; Response surface methodology ; Tyrosol ; Oleuropein ; Central composite design ; Extraction (chemistry) ; Pomace ; Factorial experiment ; Box–Behnken design ; Chromatography ; Chemistry ; Pulp and paper industry ; Materials science ; Phenols ; Computer science ; Food science ; Machine learning ; Organic chemistry ; Olive oil ; Engineering Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities
Authors: Taesoo Lee | Minhyung Lee | Hansol Seo | Minjun Kim | Beomsoo Chun | Jeonghun Kwak
Source: Small Methods (review, 2023-05-14)
Field: Materials Science — Materials Chemistry

Top‐Emitting Quantum Dot Light‐Emitting Diodes: Theory, Optimization, and Application The superior optical properties of colloidal quantum dots (QDs) have garnered significant broad interest from academia and industry owing to their successful application in self-emitting QD-based light-emitting diodes (QLEDs). In particular, active research is being conducted on QLEDs with top-emission device architectures (TQLEDs) owing to their advantages such as easy integration with conventional backplanes, high color purity, and excellent light extraction. However, due to the complicated optical phenomena and their highly sensitive optoelectrical properties to experimental variations, TQLEDs cannot be optimized easily for practical use. This review summarizes previous studies that have investigated top-emitting device structures and discusses ways to advance the performance of TQLEDs. First, theories relevant to the optoelectrical properties of TQLEDs are introduced. Second, advancements in device optimization are presented, where the underlying theories for each are considered. Finally, multilateral strategies for TQLEDs to enable their wider application to advanced industries are discussed. This work believes that this review can provide valuable insights for realizing commercial TQLEDs applicable to a broad range of applications. Light-emitting diode ; Quantum dot ; Diode ; Materials science ; Computer science ; Optoelectronics ; Nanotechnology ; Engineering physics ; Engineering Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications ; Carbon and Quantum Dots Applications
Authors: Peng Fu | Shuo Du | Wenze Lan | Leyong Hu | Yiqing Wu | Zhenfei Li | Xin Huang | Yang Guo | Weiren Zhu | Junjie Li | Baoli Liu | Changzhi Gu
Source: Communications Physics (article, 2023-09-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning enabled topological design of exceptional points for multi-optical-parameter control Abstract Metasurfaces are 2D artificial nanostructures that exhibit fascinating optical phenomena and flexible capabilities. Multi-optical-parameter metasurfaces have advantages over single-function or single-dimensional metasurfaces, especially in practical applications like holography, sub-diffraction imaging, and vectorial fields. However, achieving multi-optical-parameter control is challenging due to a lack of design strategy, limited manipulation channels, and signal-to-noise ratio problems. Exceptional points (EPs) possess inherent polarization decoupling properties and allow for amplitude and wavelength modulation, opening up research prospects for multi-optical-parameter electromagnetic field modulation and developing compact integrated devices. Leveraging deep learning, we observe topological charge conservation and utilize the topologically protected optical parameter distribution around scattered EPs. Based on these, we introduce amplitude-phase multiplexing and wavelength division multiplexing devices. Our work allows rapid and precise discovery of EPs topology, offers a powerful tool for digging related physics, and provides a paradigm for multi-optical parametric manipulation with high performance and less crosstalk, which is critical for imaging, encryption, and information storage applications. Multiplexing ; Computer science ; Parametric statistics ; Topology (electrical circuits) ; Decoupling (probability) ; Holography ; Physics ; Optics ; Telecommunications ; Engineering ; Mathematics ; Electrical engineering Metamaterials and Metasurfaces Applications ; Quantum Mechanics and Non-Hermitian Physics ; Orbital Angular Momentum in Optics
Authors: Chen‐Feng Long | Zhi-Dong Wen | Yang‐Jun Deng | Hu Tian | Jinling Liu | Xinghui Zhu
Source: Agronomy (article, 2023-09-17)
Field: Chemistry — Analytical Chemistry

Locality Preserved Selective Projection Learning for Rice Variety Identification Based on Leaf Hyperspectral Characteristics Rice has an important position in China as well as in the world. With the wide application of rice hybridization technology, the problem of mixing between individual varieties has become more and more prominent, so the variety identification of rice is important for the agricultural production, the phenotype collection, and the scientific breeding. Traditional identification methods are highly subjective and time-consuming. To address this issue, we propose a novel locality preserved selective projection learning (LPSPL) method for non-destructive rice variety identification based on leaf hyperspectral characteristics. The proposed LPSPL method can select the most discriminative spectral features from the leaf hyperspectral characteristics of rice, which is helpful to distinguish different rice varieties. In the experiments, support vector machine (SVM) is adopted to conduct the rice variety identification based on the selected spectral features. The experimental results show that the proposed method here achieves higher identification rates, 96% for the early rice and 98% for the late rice, respectively, which are superior to some state-of-the-art methods. Hyperspectral imaging ; Identification (biology) ; Discriminative model ; Support vector machine ; Artificial intelligence ; Rice plant ; Pattern recognition (psychology) ; Locality ; Variety (cybernetics) ; Computer science ; Projection (relational algebra) ; Biology ; Agronomy ; Botany ; Algorithm Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing and Land Use
Authors: P. Pathmanaban | B. K. Gnanavel | Shanmuga Sundaram Anandan
Source: Data in Brief (article, 2023-08-10)
Field: Chemistry — Analytical Chemistry

Comprehensive guava fruit data set: Digital and thermal images for analysis and classification Guava (Psidium guajava) is a nutritious fruit known for its origin in Mexico, Central or South America, and the Caribbean. Its production faces declining levels, infections, and disease outbreaks. This dataset focuses on the early identification of guava diseases using image processing and computer vision. Farmers can detect and address diseases promptly by developing an expert system, increasing yields and reducing economic losses. The technology behind this dataset enables sustainable guava farming and disease prevention. This dataset consists of digital and thermal images of guava fruits, including healthy, damaged, and various diseased conditions such as wilt, Anthracnose, canker, and rot. The images are categorized based on the fruit's maturity level (mature, half-mature, and mature) and captured under different drop heights. The dataset also includes information on the damage-inducing methods, storage conditions, image capture schedule, and specific diseases present. The thermal images were acquired using hot air with controlled temperature and velocity. Canker ; Psidium ; Agriculture ; Horticulture ; Schedule ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Essential Oils and Antimicrobial Activity
Authors: Anna Špačková | Ondřej Vávra | Tomáš Raček | Václav Bazgier | David Sehnal | Jiřı́ Damborský | Radka Svobodová Vařeková | David Bednář | Karel Berka
Source: Nucleic Acids Research (article, 2023-10-23)
Field: Chemistry — Spectroscopy

ChannelsDB 2.0: a comprehensive database of protein tunnels and pores in AlphaFold era ChannelsDB 2.0 is an updated database providing structural information about the position, geometry and physicochemical properties of protein channels-tunnels and pores-within deposited biomacromolecular structures from PDB and AlphaFoldDB databases. The newly deposited information originated from several sources. Firstly, we included data calculated using a popular CAVER tool to complement the data obtained using original MOLE tool for detection and analysis of protein tunnels and pores. Secondly, we added tunnels starting from cofactors within the AlphaFill database to enlarge the scope of the database to protein models based on Uniprot. This has enlarged available channel annotations ∼4.6 times as of 1 September 2023. The database stores information about geometrical features, e.g. length and radius, and physico-chemical properties based on channel-lining amino acids. The stored data are interlinked with the available UniProt mutation annotation data. ChannelsDB 2.0 provides an excellent resource for deep analysis of the role of biomacromolecular tunnels and pores. The database is available free of charge: https://channelsdb2.biodata.ceitec.cz. UniProt ; Database ; Protein Data Bank (RCSB PDB) ; Protein Data Bank ; Biology ; Complement (music) ; Computer science ; Protein structure ; Biochemistry Advanced Proteomics Techniques and Applications ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: Jiming Yuan | Zeming Li | Benfeng Yuan | Guoping Xiao | Tao Li | Jian‐Qiang Wang
Source: Energies (article, 2023-03-10)
Field: Materials Science — Materials Chemistry

Optimization of High-Temperature Electrolysis System for Hydrogen Production Considering High-Temperature Degradation Solid oxide electrolysis cells (SOECs) have great application prospects because of their excellent performance, but the long-term applications of the stacks are restricted by the structural degradation under the high-temperature conditions. Therefore, an SOEC degradation model is developed and embedded in a process model of the high-temperature steam electrolysis (HTSE) system to investigate the influence of the stack degradation at the system level. The sensitivity analysis and optimization were carried out to study the influence factors of the stack degradation and system hydrogen production efficiency and search for the optimal operating conditions to improve the hydrogen production efficiency and mitigate the stack degradation. The analysis results show that the high temperature and large current density can accelerate the stack degradation but improve the hydrogen production efficiency, while the high temperature gradually becomes unfavorable in the late stage. The low air-to-fuel feed ratio is beneficial to both the degradation rate and hydrogen production efficiency. The results show that the optimization method can improve the hydrogen production efficiency and inhibit the stack degradation effectively. Moreover, part of the hydrogen production efficiency has to be sacrificed in order to obtain a lower stack degradation rate. Stack (abstract data type) ; Hydrogen production ; Degradation (telecommunications) ; Hydrogen ; High-temperature electrolysis ; Electrolysis ; Materials science ; Process engineering ; High-pressure electrolysis ; Chemical engineering ; Chemistry ; Computer science ; Engineering ; Electrolyte ; Electrode Advancements in Solid Oxide Fuel Cells ; Chemical Looping and Thermochemical Processes ; Molten salt chemistry and electrochemical processes
Authors: Qiu-Shi Chen | Jianqi Li | Qing‐Wei Zhang
Source: Pharmaceutical Fronts (article, 2023-03-01)
Field: Chemistry — Inorganic Chemistry

Application of Chiral Piperidine Scaffolds in Drug Design Chiral piperidine scaffolds are prevalent as the common cores of a large number of active pharmaceuticals in medical chemistry. This review outlined the diversity of chiral piperidine scaffolds in recently approved drugs, and also covers the scaffolds in leads and drug candidates. The significance of chiral piperidine scaffolds in drug design is also discussed in this article. With the introduction of chiral piperidine scaffolds into small molecules, the exploration of drug-like molecules can be benefitted from the following aspect: (1) modulating the physicochemical properties; (2) enhancing the biological activities and selectivity; (3) improving pharmacokinetic properties; and (4) reducing the cardiac hERG toxicity. Given above, chiral piperidine-based discovery of small molecules will be a promising strategy to enrich our molecules' library to fight against diseases. Piperidine ; Chemistry ; Combinatorial chemistry ; hERG ; Drug ; Small molecule ; Drug discovery ; Molecule ; Nanotechnology ; Pharmacology ; Stereochemistry ; Organic chemistry ; Materials science ; Medicine ; Biophysics ; Biochemistry ; Biology Asymmetric Hydrogenation and Catalysis ; Asymmetric Synthesis and Catalysis ; Chemical Synthesis and Analysis
Authors: Maria Ameziane | Joonatan Huhtasalo | Lukáš Flajšman | Rhodri Mansell | Sebastiaan van Dijken
Source: Nano Letters (article, 2023-04-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Solid-State Lithium Ion Supercapacitor for Voltage Control of Skyrmions Ionic control of magnetism gives rise to high magnetoelectric coupling efficiencies at low voltages, which is essential for low-power magnetism-based nonconventional computing technologies. However, for on-chip applications, magnetoionic devices typically suffer from slow kinetics, poor cyclability, impractical liquid architectures, or strong ambient effects. As a route to overcoming these problems, we demonstrate a LiPON-based solid-state ionic supercapacitor with a magnetic Pt/Co<sub>40</sub>Fe<sub>40</sub>B<sub>20</sub>/Pt thin-film electrode which enables voltage control of a magnetic skyrmion state. Skyrmion nucleation and annihilation are caused by Li ion accumulation and depletion at the magnetic interface under an applied voltage. The skyrmion density can be controlled through dc applied voltages or through voltage pulses. The skyrmions are nucleated by single 60 μs voltage pulses, and devices are cycled 750000 times without loss of electrical performance. Our results demonstrate a simple and robust approach to ionic control of magnetism in spin-based devices. Skyrmion ; Magnetism ; Voltage ; Materials science ; Lithium (medication) ; Optoelectronics ; Ion ; Nucleation ; Supercapacitor ; Nanotechnology ; Condensed matter physics ; Electrode ; Physics ; Capacitance Supercapacitor Materials and Fabrication ; Magnetic properties of thin films ; Multiferroics and related materials
Authors: Xintao Yuan | Xiao Zhang | Nannan Zhang | Rui Ma | Daidi He | Hao Bao | Wujun Sun
Source: Agriculture (article, 2023-09-07)
Field: Chemistry — Analytical Chemistry

Hyperspectral Estimation of SPAD Value of Cotton Leaves under Verticillium Wilt Stress Based on GWO–ELM Rapid and non-destructive estimation of the chlorophyll content in cotton leaves is of great significance for the real-time monitoring of cotton growth under verticillium wilt (VW) stress. The spectral reflectance of healthy and VW cotton leaves was determined using hyperspectral technology, and the original spectra were processed using Savitzky–Golay (SG) smoothing, and on its basis through mean centering, standard normal variate (SG-SNV), multiplicative scatter correction (SG-MSC), reciprocal second-order differentiation, and logarithmic second-order differentiation ([lg(SG)]″) preprocessing operations. The characteristic bands were selected based on the correlation coefficient, vegetation index, successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS). The single-factor model, back propagation neural network of particle swarm optimization algorithm, and extreme learning machine (ELM) of a grey wolf optimizer (GWO) algorithm were constructed to compare and explore the ability of each model to estimate the soil plant analysis development (SPAD) value of cotton under VW stress. The results showed that spectral pretreatment could improve the correlation between characteristic bands and SPAD values. SG-MSC and SG-SNV showed better changes in the five pretreatments, and the maximum correlation coefficients of healthy and VW cotton leaves were higher than 0.74. Compared with SPA, the accuracy of model estimation based on CARS-extracted characteristic bands was higher, and the estimation accuracy of the multi-factor model was better than that of the single-factor model under each pretreatment. For healthy cotton leaves, [lg(SG)]″–CARS–GWO–ELM was the optimal model, with a modeling and validation set R2 of 0.956 and 0.887, respectively. For VW cotton leaves, SG-MSC–CARS–GWO–ELM was the optimal model, with a modeling and validation set R2 of 0.832 and 0.824, respectively. Therefore, the GWO–ELM model constructed under different pretreatments combined with characteristic extraction methods can be used for the estimation of leaf SPAD values under VW stress to dynamically monitor VW stress in cotton and provide a theoretical reference for precision agriculture. Hyperspectral imaging ; Mathematics ; Correlation coefficient ; Canopy ; Statistics ; Biological system ; Algorithm ; Botany ; Artificial intelligence ; Biology ; Computer science Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Water Quality Monitoring and Analysis
Authors: Yangfan He | Baojiang Fan | Lei Sun | Xiaofei Fan | Jun Zhang | Yuchao Li | Xuesong Suo
Source: Frontiers in Plant Science (article, 2023-08-17)
Field: Chemistry — Analytical Chemistry

Rapid appearance quality of rice based on machine vision and convolutional neural network research on automatic detection system The experimental results showed that the resolution of the system could reach 92.3%. In the detection process, the system designed with this method not only reduces the subjectivity problems caused by different detection environments, visual fatigue caused large sample size and the inspector's personal factors, but also significantly improves the detection time and accuracy, which further enhances the detection efficiency of rice appearance quality, and has positive significance for the development of the rice industry. Convolutional neural network ; Artificial intelligence ; Computer science ; Computer vision ; Machine vision ; RGB color model ; Process (computing) ; Image quality ; Artificial neural network ; Pattern recognition (psychology) ; Image processing ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies