jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Emmanuel Ekene Okere | Ebrahiema Arendse | Alemayehu Ambaw Tsige | W.J. Perold | Umezuruike Linus Opara
Source: Agriculture (review, 2022-11-28)
Field: Chemistry — Analytical Chemistry

Pomegranate Quality Evaluation Using Non-Destructive Approaches: A Review Pomegranate (Punica granatum L.) is one of the most healthful and popular fruits in the world. The increasing demand for pomegranate has resulted in it being processed into different food products and food supplements. Researchers over the years have shown interest in exploring non-destructive techniques as alternative approaches for quality assessment of the harvest at the on-farm point to the retail level. The approaches of non-destructive techniques are more efficient, inexpensive, faster and yield more accurate results. This paper provides a comprehensive review of recent applications of non-destructive technology for the quality evaluation of pomegranate fruit. Future trends and challenges of using non-destructive techniques for quality evaluation are highlighted in this review paper. Some of the highlighted techniques include computer vision, imaging-based approaches, spectroscopy-based approaches, the electronic nose and the hyperspectral imaging technique. Our findings show that most of the applications are focused on the grading of pomegranate fruit using machine vision systems and the electronic nose. Measurements of total soluble solids (TSS), titratable acidity (TA) and pH as well as other phytochemical quality attributes have also been reported. Value-added products of pomegranate fruit such as fresh-cut and dried arils, pomegranate juice and pomegranate seed oil have been non-destructively investigated for their numerous quality attributes. This information is expected to be useful not only for those in the grower/processing industries but also for other agro-food commodities. Punica ; Aril ; Electronic nose ; Titratable acid ; Agricultural engineering ; Quality (philosophy) ; Food products ; Mathematics ; Biotechnology ; Food science ; Computer science ; Horticulture ; Engineering ; Artificial intelligence ; Biology Spectroscopy and Chemometric Analyses ; Pomegranate: compositions and health benefits ; Essential Oils and Antimicrobial Activity
Authors: Luis Concha | Ana Luíza Resende Pires | Ângela Maria Moraes | Elizabeth Mas‐Hernández | Stefan Berres | Jacobo Hernández‐Montelongo
Source: Polymers (article, 2022-03-10)
Field: Materials Science — Surfaces, Coatings and Films

Cost Function Analysis Applied to Different Kinetic Release Models of Arrabidaea chica Verlot Extract from Chitosan/Alginate Membranes This work focuses on the mathematical analysis of the controlled release of a standardized extract of <i>A. chica</i> from chitosan/alginate (C/A) membranes, which can be used for the treatment of skin lesions. Four different types of C/A membranes were tested: a dense membrane (CA), a dense and flexible membrane (CAS), a porous membrane (CAP) and a porous and flexible membrane (CAPS). The <i>Arrabidae chica</i> extract release profiles were obtained experimentally in vitro using PBS at 37 °C and pH 7. Experimental data of release kinetics were analyzed using five classical models from the literature: Zero Order, First Order, Higuchi, Korsmeyer-Peppas and Weibull functions. Results for the Korsmeyer-Peppas model showed that the release of <i>A. chica</i> extract from four membrane formulations was by a diffusion through a partially swollen matrix and through a water filled network mesh; however, the Weibull model suggested that non-porous membranes (CA and CAS) had fractal geometry and that porous membranes (CAP and CAPS) have highly disorganized structures. Nevertheless, by applying an explicit optimization method that employs a cost function to determine the model parameters that best fit to experimental data, the results indicated that the Weibull model showed the best simulation for the release profiles from the four membranes: CA, CAS and CAP presented Fickian diffusion through a polymeric matrix of fractal geometry, and only the CAPS membrane showed a highly disordered matrix. The use of this cost function optimization had the significant advantage of higher fitting sensitivity. Membrane ; Diffusion ; Chitosan ; Materials science ; Porosity ; Weibull distribution ; Matrix (chemical analysis) ; Kinetics ; Chemical engineering ; Fick's laws of diffusion ; Polymer chemistry ; Chemistry ; Chromatography ; Composite material ; Mathematics ; Thermodynamics ; Physics ; Engineering ; Biochemistry ; Statistics Polymer Surface Interaction Studies ; Nanocomposite Films for Food Packaging ; Marine Biology and Environmental Chemistry
Authors: Jin Wang | Yujia Huo | Yutong Wang | Haoyu Zhao | Kai Li | Li Liu | Yinggang Shi
Source: PLoS ONE (article, 2022-08-04)
Field: Chemistry — Analytical Chemistry

Grading detection of “Red Fuji” apple in Luochuan based on machine vision and near-infrared spectroscopy A quality detection system for the "Red Fuji" apple in Luochuan was designed for automatic grading. According to the Chinese national standard, the grading principles of apple appearance quality and Brix detection were determined. Based on machine vision and image processing, the classifier models of apple defect, contour, and size were constructed. And then, the grading thresholds were set to detect the defective pixel ratio t, aspect ratio λ, and the cross-sectional diameter Wp in the image of the apple. Spectral information of apples in the wavelength range of 400 nm~1000 nm was collected and the multiple scattering correction (MSC) and standard normal variable (SNV) transformation methods were used to preprocess spectral reflectance data. The competitive adaptive reweighted sampling (CARS) algorithm and the successive projections algorithm (SPA) were used to extract characteristic wavelength points containing Brix information, and the CARS-PLS (partial least squares) algorithm was used to establish a Brix prediction model. Apple defect, contour, size, and Brix were combined as grading indicators. The apple quality online grading detection platform was built, and apple's comprehensive grading detection algorithm and upper computer software were designed. The experiments showed that the average accuracy of apple defect, contour, and size grading detection was 96.67%, 95.00%, and 94.67% respectively, and the correlation coefficient Rp of the Brix prediction set was 0.9469. The total accuracy of apple defect, contour, size, and Brix grading was 96.67%, indicating that the detection system designed in this paper is feasible to classify "Red Fuji" apple in Luochuan. Brix ; Artificial intelligence ; Grading (engineering) ; Mathematics ; Pixel ; Pattern recognition (psychology) ; Computer science ; Computer vision ; Engineering ; Chemistry ; Sugar ; Food science Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Xiaodong Gao | Pingchuan Dong | Jiawei Cui | Qichao Gao
Source: Energies (article, 2022-03-21)
Field: Chemistry — Analytical Chemistry

Prediction Model for the Viscosity of Heavy Oil Diluted with Light Oil Using Machine Learning Techniques Due to the presence of asphaltene, the flow assurance of high viscosity crude oil becomes more challenging and costly to produce in wellbores and pipelines. One of the most effective ways to reduce viscosity is to blend heavy oil with light oil. However, the viscosity measurement of diluted heavy crude is either time-consuming or inaccurate. This work aims to develop a more accurate viscosity model of diluted heavy crude based on machine learning techniques. A multilayer neural network is used to predict the viscosity of heavy oil diluted with lighter oil. The input data used in the training include temperature, light oil viscosity, heavy oil viscosity, and dilution ratio. In this modeling process, 156 datasets were retrieved from the available iterature of various heavy-oil fields in China. Part of the data (80%) is used to train the developed models using Adam optimizer algorithms, while the other part of the data (20%) is used to predict the viscosity of heavy oil diluted with lighter. The performance and accuracy of the machine learning models were tested and compared with the existing viscosity models. It was found that the new model can predict the viscosity of diluted heavy oil with higher accuracy, and it performs better than other models. The absolute average relative error is 10.44%, the standard deviation of the relative error is 8.45%, and the coefficient of determination is R2 = 0.95. The viscosity predicted by the neural network outperformed existing correlations by the statistical analysis used for the datasets available in the literature. Therefore, the method proposed in this paper can better estimate the viscosity of diluted heavy crude oil and has important promotion value. Viscosity ; Light crude oil ; Oil viscosity ; Approximation error ; Artificial neural network ; Asphaltene ; Dilution ; Petroleum engineering ; Work (physics) ; Computer science ; Materials science ; Machine learning ; Environmental science ; Thermodynamics ; Chemistry ; Algorithm ; Engineering ; Physics ; Composite material ; Chemical engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Tyler D. R. Vance | Patrick Yip | Elisabet Jiménez | Sheng Li | Diana Gawol | James Byrnes | Isabel Usón | Ahmed Ziyyat | Jeffrey E. Lee
Source: Communications Biology (article, 2022-09-17)
Field: Materials Science — Materials Chemistry

SPACA6 ectodomain structure reveals a conserved superfamily of gamete fusion-associated proteins SPACA6 is a sperm-expressed surface protein that is critical for gamete fusion during mammalian sexual reproduction. Despite this fundamental role, little is known about how SPACA6 specifically functions. We elucidated the crystal structure of the SPACA6 ectodomain at 2.2-Å resolution, revealing a two-domain protein containing a four-helix bundle and Ig-like β-sandwich connected via a quasi-flexible linker. This structure is reminiscent of IZUMO1, another gamete fusion-associated protein, making SPACA6 and IZUMO1 founding members of a superfamily of fertilization-associated proteins, herein dubbed the IST superfamily. The IST superfamily is defined structurally by its distorted four-helix bundle and a pair of disulfide-bonded CXXC motifs. A structure-based search of the AlphaFold human proteome identified more protein members to this superfamily; remarkably, many of these proteins are linked to gamete fusion. The SPACA6 structure and its connection to other IST-superfamily members provide a missing link in our knowledge of mammalian gamete fusion. Ectodomain ; Gamete ; SUPERFAMILY ; Fusion protein ; Biology ; Cell biology ; Protein structure ; Protein domain ; Proteome ; Genetics ; Human fertilization ; Receptor ; Biochemistry ; Recombinant DNA ; Gene Enzyme Structure and Function ; Sperm and Testicular Function ; Protein Structure and Dynamics
Authors: Florian A. Rosenberger | Marvin Thielert | Maximilian T. Strauss | Constantin Ammar | Sophia C. Mädler | Lisa Schweizer | Andreas Metousis | Patricia Skowronek | Maria Wahle | Janine Gote-Schniering | Anna Semenova | Herbert B. Schiller | Edwin H. Rodriguez | Thierry M. Nordmann | Andreas Mund | Matthias Mann
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-12-03)
Field: Chemistry — Spectroscopy

Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome Abstract Single-cell proteomics by mass spectrometry (MS) is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion of the method to complex tissues would greatly enhance biological insights. Here we describe single-cell Deep Visual Proteomics (scDVP), a technology that integrates high-content imaging, laser microdissection and multiplexed MS. scDVP resolves the context-dependent, spatial proteome of murine hepatocytes at a current depth of 1,700 proteins from a slice of a cell. Half of the proteome was differentially regulated in a spatial manner, with protein levels changing dramatically in proximity to the central vein. We applied machine learning to proteome classes and images, which subsequently inferred the spatial proteome from imaging data alone. scDVP is applicable to healthy and diseased tissues and complements other spatial proteomics or spatial omics technologies. Proteome ; Proteomics ; Laser capture microdissection ; Computational biology ; Context (archaeology) ; Mass spectrometry ; Mass spectrometry imaging ; Biology ; Cell ; Cell biology ; Bioinformatics ; Chemistry ; Biochemistry ; Gene expression ; Chromatography Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Mass Spectrometry Techniques and Applications
Authors: Konstantin Pikula | Seyed Ali Johari | Ralph Santos‐Oliveira | Kirill S. Golokhvast
Source: International Journal of Molecular Sciences (article, 2022-01-17)
Field: Materials Science — Materials Chemistry

Individual and Binary Mixture Toxicity of Five Nanoparticles in Marine Microalga Heterosigma akashiwo The investigation of the combined toxic action of different types of nanoparticles (NPs) and their interaction between each other and with aquatic organisms is an important problem of modern ecotoxicology. In this study, we assessed the individual and mixture toxicities of cadmium and zinc sulfides (CdS and ZnS), titanium dioxide (TiO<sub>2</sub>), and two types of mesoporous silicon dioxide (with no inclusions (SMB3) and with metal inclusions (SMB24)) by a microalga growth inhibition bioassay. The counting and size measurement of microalga cells and NPs were performed by flow cytometry. The biochemical endpoints were measured by a UV-VIS microplate spectrophotometer. The highest toxicity was observed for SMB24 (EC50, 3.6 mg/L) and CdS (EC50, 21.3 mg/L). A combined toxicity bioassay demonstrated that TiO<sub>2</sub> and the SMB3 NPs had a synergistic toxic effect in combinations with all the tested samples except SMB24, probably caused by a "Trojan horse effect". Sample SMB24 had antagonistic toxic action with CdS and ZnS, which was probably caused by metal ion scavenging. Heterosigma akashiwo ; Toxicity ; Algal bloom ; Chemistry ; Environmental chemistry ; Biology ; Phytoplankton ; Ecology Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Advanced Nanomaterials in Catalysis
Authors: Yoonho Lee | Geon-Il Kim | Kyung-Min Kim | Sang-Jin Ko | Woocheol Kim | Jung-Gu Kim
Source: Materials (article, 2022-03-02)
Field: Materials Science — Metals and Alloys

Localized Corrosion Occurrence in Low-Carbon Steel Pipe Caused by Microstructural Inhomogeneity In this study, the cause of failure of a low-carbon steel pipe meeting standard KS D 3562 (ASTM A135), in a district heating system was investigated. After 6 years of operation, the pipe failed prematurely due to pitting corrosion, which occurred both inside and outside of the pipe. Pitting corrosion occurred more prominently outside the pipe than inside, where water quality is controlled. The analysis indicated that the pipe failure occurred due to aluminum inclusions and the presence of a pearlite inhomogeneous phase fraction. Crevice corrosion occurred in the vicinity around the aluminum inclusions, causing localized corrosion. In the large pearlite fraction region, cementite in the pearlite acted as a cathode to promote dissolution of surrounding ferrite. Therefore, in the groundwater environment outside of the pipe, localized corrosion occurred due to crevice corrosion by aluminum inclusions, and localized corrosion was accelerated by the large fraction of pearlite around the aluminum inclusions, leading to pipe failure. Pearlite ; Cementite ; Materials science ; Metallurgy ; Corrosion ; Pitting corrosion ; Carbon steel ; Crevice corrosion ; Ferrite (magnet) ; Aluminium ; Microstructure ; Composite material ; Austenite Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability
Authors: Rodrigo Calvo | Andreas Thon | Asís Saad | Antonio Salvador-Mátar | Miguel Manso‐Silván | Óscar Ahumada | Valerio Pini
Source: Scientific Reports (article, 2022-10-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Size characterization of plasmonic nanoparticles with dark-field single particle spectrophotometry Plasmonic nanoparticles are widely used in multiple scientific and industrial applications. Although many synthesis methods have been reported in the literature throughout the last decade, controlling the size and shape of large populations still remains as a challenge. As size and shape variations have a strong impact in their plasmonic properties, the need to have metrological techniques to accurately characterize their morphological features is peremptory. We present a new optical method referred as Dark-Field Single Particle Spectrophotometry which is able to measure the individual sizes of thousands of particles with nanometric accuracy in just a couple of minutes. Our method also features an easy sample preparation, a straightforward experimental setup inspired on a customized optical microscope, and a measurement protocol simple enough to be carried out by untrained technicians. As a proof of concept, thousands of spherical nanoparticles of different sizes have been measured, and after a direct comparison with metrological gold standard electron microscopy, a discrepancy of 3% has been attested. Although its feasibility has been demonstrated on spherical nanoparticles, the true strengthness of the method is that it can be generalized also to nanoparticles with arbitrary shapes and geometries, thus representing an advantageous alternative to the gold-standard electron microscopy. Metrology ; Dark field microscopy ; Characterization (materials science) ; Plasmon ; Nanoparticle ; Particle (ecology) ; Colloidal gold ; Nanotechnology ; Particle size ; Materials science ; Electron microscope ; Optical microscope ; Microscopy ; Optics ; Scanning electron microscope ; Physics ; Chemistry ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research
Authors: Pingshi Wang | Hao Ma | Sen Yan | Xinyu Lu | Hui Tang | Xiao-Han Xi | Xiaohui Peng | Yajun Huang | Yi‐Fan Bao | Maofeng Cao | Huimeng Wang | Jinglin Huang | Guokun Liu | Xiang Wang | Bin Ren
Source: Chemical Science (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Correlation coefficient-directed label-free characterization of native proteins by surface-enhanced Raman spectroscopy Investigation of proteins in their native state is the core of proteomics towards better understanding of their structures and functions. Surface-enhanced Raman spectroscopy (SERS) has shown its unique advantages in protein characterization with fingerprint information and high sensitivity, which makes it a promising tool for proteomics. It is still challenging to obtain SERS spectra of proteins in the native state and evaluate the native degree. Here, we constructed 3D physiological hotspots for a label-free dynamic SERS characterization of a native protein with iodide-modified 140 nm Au nanoparticles. We further introduced the correlation coefficient to quantitatively evaluate the variation of the native degree, whose quantitative nature allows us to explicitly investigate the Hofmeister effect on the protein structure. We realized the classification of a protein of SARS-CoV-2 variants in 15 min, which has not been achieved before. This study offers an effective tool for tracking the dynamic structure of proteins and biomedical research. Raman spectroscopy ; Characterization (materials science) ; Correlation coefficient ; Surface-enhanced Raman spectroscopy ; Spectroscopy ; Chemistry ; Materials science ; Analytical Chemistry (journal) ; Biological system ; Nanotechnology ; Computer science ; Raman scattering ; Biology ; Chromatography ; Physics ; Optics ; Machine learning Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Daniel Cozzolino
Source: Foods (review, 2022-01-24)
Field: Chemistry — Analytical Chemistry

An Overview of the Successful Application of Vibrational Spectroscopy Techniques to Quantify Nutraceuticals in Fruits and Plants Vibrational spectroscopy techniques are the most used techniques in the routine analysis of foods. This technique is widely utilised to measure and monitor the proximate chemical composition (e.g., protein, dry matter, fat and fibre) in an array of agricultural commodities, food ingredients and products. Developments in optics, instrumentation and hardware concomitantly with data analytics, have allowed for the progress in novel applications of these technologies in the field of nutraceutical and bio compound analysis. In recent years, several studies have demonstrated the capability of vibrational spectroscopy to evaluate and/or measure these nutraceuticals in a broad selection of fruit and plants as alternative to classical analytical approaches. This article highlights, as well as discusses, the challenges and opportunities that define the successful application of vibrational spectroscopy techniques, and the advantages that these techniques have to offer to evaluate and quantify nutraceuticals in fruits and plants. Nutraceutical ; Analytics ; Biochemical engineering ; Instrumentation (computer programming) ; Spectroscopy ; Nanotechnology ; Biotechnology ; Computer science ; Chemistry ; Engineering ; Materials science ; Data science ; Biology ; Food science ; Physics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Ruben F. Kranenburg | Henk‐Jan Ramaker | Arian van Asten
Source: Forensic Science International (article, 2022-09-23)
Field: Chemistry — Spectroscopy

Portable near infrared spectroscopy for the isomeric differentiation of new psychoactive substances Rapid and efficient identification of the precise isomeric form of new psychoactive substances (NPS) by forensic casework laboratories is a relevant challenge in the forensic field. Differences in legal status occur for ring-isomeric species of the same class, thus leading to different penalties and judicial control. Portable systems such as near-infrared (NIR) spectroscopy recently emerged as suitable techniques for the on-scene identification of common drugs of abuse such as cocaine, MDMA and amphetamine. This way, the overall forensic process becomes more efficient as relevant information on substance identity becomes available directly at the scene of crime. Currently, no NIR-based applications exist for the rapid, on-scene detection of NPS isomers. Herein, we present the differentiation of cathinone and phenethylamine-type NPS analogues based on their NIR spectrum recorded in 2 seconds on a portable 1350 - 2600 nm spectrometer. A prior developed data analysis model was found suitable for the identification of the methylmethcathinone (MMC) isomers 2-MMC, 3-MMC and 4-MMC. In 51 mixtures and 22 seized casework samples, the correct isomeric form was detected in all cases except for a few mixtures with an active ingredient content of 10 wt%. These results show the feasibility of on-site NPS detection as presumptive test performed directly at the scene of crime with a small size NIR-spectrometer. Additionally, in the illicit drug analysis laboratory the combination of NIR and GC-MS analysis might be suitable for robust identification of NPS isomers and analogues. Street drugs ; Infrared spectroscopy ; Infrared ; Spectroscopy ; Designer drug ; Chemistry ; Medicine ; Pharmacology ; Physics ; Optics ; Organic chemistry ; Drug Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications ; Advanced Chemical Sensor Technologies