jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sabrina Hamla | Pierre‐Yves Sacré | Allison Derenne | Kheiro-Mouna Derfoufi | Ben Cowper | Claire I. Butré | Arnaud Delobel | Erik Goormaghtigh | Philippe Hubert | Eric Ziémons
Source: The Analyst (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

A new alternative tool to analyse glycosylation in pharmaceutical proteins based on infrared spectroscopy combined with nonlinear support vector regression Almost 60% of commercialized pharmaceutical proteins are glycosylated. Glycosylation is considered a critical quality attribute, as it affects the stability, bioactivity and safety of proteins. Hence, the development of analytical methods to characterise the composition and structure of glycoproteins is crucial. Currently, existing methods are time-consuming, expensive, and require significant sample preparation steps, which can alter the robustness of the analyses. In this work, we suggest the use of a fast, direct, and simple Fourier transform infrared spectroscopy (FT-IR) combined with a chemometric strategy to address this challenge. In this context, a database of FT-IR spectra of glycoproteins was built, and the glycoproteins were characterised by reference methods (MALDI-TOF, LC-ESI-QTOF and LC-FLR-MS) to estimate the mass ratio between carbohydrates and proteins and determine the composition in monosaccharides. The FT-IR spectra were processed first by Partial Least Squares Regression (PLSR), one of the most used regression algorithms in spectroscopy and secondly by Support Vector Regression (SVR). SVR has emerged in recent years and is now considered a powerful alternative to PLSR, thanks to its ability to flexibly model nonlinear relationships. The results provide clear evidence of the efficiency of the combination of FT-IR spectroscopy, and SVR modelling to characterise glycosylation in therapeutic proteins. The SVR models showed better predictive performances than the PLSR models in terms of RMSECV, RMSEP, <i>R</i>2CV, <i>R</i>2Pred and RPD. This tool offers several potential applications, such as comparing the glycosylation of a biosimilar and the original molecule, monitoring batch-to-batch homogeneity, and in-process control. Glycosylation ; Infrared spectroscopy ; Nonlinear regression ; Infrared ; Support vector machine ; Chemistry ; Nonlinear system ; Regression ; Biological system ; Analytical Chemistry (journal) ; Regression analysis ; Computer science ; Mathematics ; Chromatography ; Organic chemistry ; Machine learning ; Statistics ; Biochemistry ; Physics ; Biology ; Optics Spectroscopy and Chemometric Analyses ; Viral Infectious Diseases and Gene Expression in Insects ; Analytical Chemistry and Chromatography
Authors: Shenli Zhang | I-Ting Chiu | Min‐Han Lee | Brandon Gunn | Mingzhen Feng | Tae Joon Park | Padraic Shafer | Alpha T. N’Diaye | Fanny Rodolakis | Shriram Ramanathan | Alex Frañó | Iván K. Schuller | Yayoi Takamura | Giulia Galli
Source: Chemistry of Materials (article, 2022-02-23)
Field: Materials Science — Materials Chemistry

Determining the Oxygen Stoichiometry of Cobaltite Thin Films Transition metal oxides (TMOs) are promising materials to realize low-power neuromorphic devices. Their physical properties critically depend on their oxygen vacancy concentration, whose experimental determination remains a challenging task. Here, we focus on cobaltites, in particular La1–xSrxCoO3−δ (LSCO), and present a strategy to identify fingerprints of oxygen vacancies in X-ray absorption (XA) spectra. Using a combination of experiments and theory, we show that the variation of the oxygen vacancy concentration in the perovskite phase of LSCO is correlated with the change in the relative peak positions of the O K-edge XA spectra. We also identify an additional geometrical fingerprint that captures both the changes in the Co–O bond length and Co–O–Co bond angle in the material due to the presence of oxygen vacancies. Finally, we predict the oxygen vacancy concentration of experimental samples and show how the resistivity of the oxide material may be tuned as a function of the defect concentration, in the absence of any structural transformation. Our study shows that, in order to predict the complex transport properties of TMOs, it is crucial to gain a detailed understanding of their oxygen defect density. Cobaltite ; Oxygen ; Materials science ; Stoichiometry ; Perovskite (structure) ; Phase (matter) ; Oxide ; Vacancy defect ; Density functional theory ; Electrical resistivity and conductivity ; Chemical physics ; Analytical Chemistry (journal) ; Chemistry ; Crystallography ; Physical chemistry ; Computational chemistry ; Physics ; Metallurgy Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials ; Advanced Memory and Neural Computing
Authors: Tingting Wang | Sining Dong | Chong Li | Wencheng Yue | Yang-Yang Lyu | Chenguang Wang | Changkun Zeng | Zixiong Yuan | Wei Zhu | Zhili Xiao | Xiaoli Lu | Bin Liu | Hai Lu | Huabing Wang | Peiheng Wu | W. K. Kwok | Yong-Lei Wang
Source: Applied Physics Letters (article, 2022-08-29)
Field: Materials Science — Materials Chemistry

<i>In situ</i> tunable giant electrical anisotropy in a grating gated AlGaN/GaN two-dimensional electron gas Materials with in-plane electrical anisotropy have great potential for designing artificial synaptic devices. However, natural materials with strong intrinsic in-plane electrical anisotropy are rare. We introduce a simple strategy to produce extremely large electrical anisotropy via grating gating of a semiconductor two-dimensional electron gas (2DEG) of AlGaN/GaN. We show that periodically modulated electric potential in the 2DEG induces in-plane electrical anisotropy, which is significantly enhanced in a magnetic field, leading to an ultra large electrical anisotropy. This is induced by a giant positive magnetoresistance and a giant negative magnetoresistance under two orthogonally oriented in-plane current flows, respectively. This giant electrical anisotropy is in situ tunable by tailoring both the grating gate voltage and the magnetic field. Our semiconductor device with controllable giant electrical anisotropy will stimulate new device applications, such as multi-terminal memtransistors and bionic synapses. Materials science ; Anisotropy ; Magnetoresistance ; Optoelectronics ; Semiconductor ; Condensed matter physics ; Grating ; Electric field ; Magnetic field ; Optics ; Physics 2D Materials and Applications ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Bin Li | Hai Yin | Yang A-kun | Aiguo Ouyang
Source: Journal of Spectroscopy (article, 2022-05-31)
Field: Chemistry — Analytical Chemistry

Detection of Adulteration of Panax Notoginseng Powder by Terahertz Technology The combined terahertz time-domain spectroscopy (THz-TDS) and chemometric technology is used to detect the adulteration of similar substances in Panax notoginseng powder. Four kinds of samples are prepared in the experiment, three kinds of adulterated samples are Panax notoginseng powder adulterating with zedoary turmeric powder, Panax notoginseng powder adulterating with wheat flour, and Panax notoginseng powder adulterating with rice flour, respectively. The values of adulterated concentration are from 5% to 60%, the interval of adulterated concentration is 5%, and the other sample is pure Panax notoginseng powder. The modeling and prediction sets are divided by 3 : 1 by class. The feature information of models is extracted by elimination of uninformative variable (UVE) method and successive projection algorithm (SPA); combining with back propagation neural network (BPNN), the UVE-BPNN and SPA-BPNN qualitative models are established, respectively. The model’s results show that the UVE-BPNN model is better; the classification accuracy of the prediction set of UVE-BPNN is 95%. Then, the least square support vector machine (LS-SVM) algorithm and partial least square (PLS) algorithm are used to establish the quantitative analysis model. The model’s results show that the LS-SVM model is better among the quantitative analysis models of zedoary turmeric powder and wheat flour, the correlation coefficient of prediction (RP) is 0.90 and 0.93 of LS-SVM, respectively, and the root mean square error of prediction (RMSEP) of LS-SVM is 0.072 and 0.068, respectively. Among the quantitative analysis models for rice noodles, the PLS model is better, with the RP of 0.94 and RMSEP of 0.06. The results show that the combined THz-TDS and chemometric technology can be used to determine the adulteration of similar substances in Panax notoginseng powder quickly, accurately, and nondestructively. Panax notoginseng ; Variable elimination ; Mathematics ; Correlation coefficient ; Support vector machine ; Partial least squares regression ; Artificial intelligence ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Advanced Chemical Sensor Technologies
Authors: Qinghai Jiang | Song Mei | Caixue Zhan | Ren Cai-hong | Zhiyu Song | Shengpeng WANG
Source: Food Science and Technology (article, 2022-11-07)
Field: Chemistry — Analytical Chemistry

Fast and nondestructive discrimination of fresh tea leaves at different altitudes based on near infrared spectroscopy and various chemometrics methods Near infrared spectroscopy (NIRS) combined with various chemometrics methods was tried to identify the fresh tea leaves at different altitudes quickly and nondestructively. Three kinds of samples were collected, then scanning NIRS, conducting spectral preprocessing to remove noise information, using backward interval partial least squares to screen characteristic spectral intervals, going on principal component analysis, respectively. Finally, least squares support vector machine method (LS-SVM) was applied to establish NIRS models, whose robustness was tested by prediction set samples. The best pretreated method was the combination of multivariate scattering correction and the first derivative. Six characteristic spectral intervals were screened, and the corresponding spectral wavenumbers were 4821.2-5091.2 cm-1, 5368.9-5638.8 cm-1, 6190.4-6460.4 cm-1, 7011.9-7281.9 cm-1, 8924.9-9191.1 cm-1 and 9734.9-10000 cm-1. The cumulative contribution rate of the first three principal components was 99.92%. The root mean square error of the cross validation and the determination coefficient of the calibration set model were 0.027 and 0.973, respectively. The root mean square error and the determination coefficient of the prediction set model were 0.034 and 0.968, respectively. The discrimination accuracy in prediction set was 100%. The results showed NIRS combined with LS-SVM can realize fast and nondestructive discrimination of fresh tea leaves at different altitudes. Chemometrics ; Principal component analysis ; Partial least squares regression ; Mathematics ; Mean squared error ; Coefficient of determination ; Near-infrared spectroscopy ; Calibration ; Analytical Chemistry (journal) ; Second derivative ; Statistics ; Biological system ; Chemistry ; Chromatography ; Optics ; Physics ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote Sensing and Land Use
Authors: Weixin Jiang | Kai Li | Trevor Spreadbury | Eric Schwenker | Oliver Cossairt | Maria K. Y. Chan
Source: Digital Discovery (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Plot2Spectra: an automatic spectra extraction tool Scientists cannot easily make use of numerical data encoded in plot images, such as of spectroscopy data, in scientific literature. Plot2Spectra was developed to use computer vision tools to automatically digitize plot images. Plot (graphics) ; Computer science ; Computer graphics (images) ; Artificial intelligence ; Extraction (chemistry) ; Pattern recognition (psychology) ; Computer vision ; Mathematics ; Chemistry ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Mineral Processing and Grinding
Authors: Xiang Xiao | Guo Zheng Zhou | Ke Qing Wang | Feng Xi | Kun Zeng
Source: Sensors (article, 2022-10-07)
Field: Materials Science — Materials Chemistry

Study on In-Service Inspection of Nuclear Fuel Assembly Failure Using Ultrasonic Plate Wave As protection for nuclear power plants is quite necessary, the nuclear fuel is sealed in zirconium alloy thin wall cladding. During service, fuel rods might be damaged caused by wall-thickness thinning, cladding corrosion and cracking, etc. This will cause the coolant to enter into the fuel rod, which may lead to the failure of the fuel assembly. However, current diagnostic methods have limitations due to the special structure of the fuel assembly and the underwater and radioactive environment. In this paper, a novel inspection method is proposed to recognize the failure of a fuel rod. The fuel rod failure can be detected based on the presence or absence of coolant inside the fuel rod by using an ultrasonic plate wave. The inspection model and process algorithm are proposed for in-service inspection. The relationship between signal and scanning position is established and analyzed. Both ultrasound field simulation and experiment have been carried out for validation. The corresponding results illustrate that the failed nuclear fuel rod of the whole fuel assembly (including the internal rods) can be effectively detected without the influence of the near-field region by using the proposed method. Rod ; Cladding (metalworking) ; Coolant ; Nuclear fuel ; Ultrasonic testing ; Ultrasonic sensor ; Nuclear engineering ; Spent nuclear fuel ; VVER ; Materials science ; Nuclear power plant ; Service life ; Mechanical engineering ; Engineering ; Acoustics ; Composite material Nuclear Materials and Properties ; Nuclear and radioactivity studies ; Nuclear Engineering Thermal-Hydraulics
Authors: Assad Farooq | Nayab Khan | Farida Irshad | Usama Nasir
Source: TEKSTİL VE KONFEKSİYON (article, 2022-07-07)
Field: Materials Science — Polymers and Plastics

Predictive Modeling of Yarn Quality at Ring Spinning Machine using Resilient Back Propagation Neural Networks The final attenuation and twisting of fiber take place at ring spinning machine and hence its optimized performance is very crucial in terms of yarn quality. Drafting at ring spinning machine has a decisive effect on quality. There exist many influencing parameters in the spinning geometry that have to be optimized for manufacturing of quality yarn. The present research work was carried out to develop the Artificial neural networks (ANN) based prediction model for the polyester/cotton blended ring spun yarns by using these influencing parameters as inputs. ANN prediction model was developed using resilient backpropogation algorithm. Yarn quality parameters like yarn evenness, hairiness and tensile parameters were predicted. The low mean absolute error values for the yarn quality parameters proved that it is possible to predict the yarn quality on the basis of spinning geometry for cotton/polyester blended ring spun yarns using Resilient Back Propogation Neural Networks. Yarn ; Spinning ; Artificial neural network ; Fiber ; Ring (chemistry) ; Polyester ; Composite material ; Ultimate tensile strength ; Quality (philosophy) ; Materials science ; Engineering ; Artificial intelligence ; Computer science ; Physics Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Color Science and Applications
Authors: Michael G. Leeming | Ching‐Seng Ang | Shuai Nie | Swati Varshney | Nicholas A. Williamson
Source: Bioinformatics Advances (article, 2022-12-19)
Field: Chemistry — Spectroscopy

Simulation of mass spectrometry-based proteomics data with Synthedia Supplementary data are available at <i>Bioinformatics Advances</i> online. Computer science ; Mass spectrometry ; Software ; Proteomics ; Data acquisition ; Field (mathematics) ; Isobaric labeling ; Raw data ; Experimental data ; Data mining ; Chromatography ; Chemistry ; Tandem mass spectrometry ; Protein mass spectrometry ; Mathematics Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Zitong Guo | Zhongde Shan | Jihua Huang | Debo Xue
Source: Chinese Journal of Mechanical Engineering (article, 2022-06-02)
Field: Materials Science — Polymers and Plastics

Study on the Distribution of Frictional Forces on Z-yarn Continuous Implanted Preforms and Their Applications Abstract To improve the quality and efficiency of Z -directional 3D preform forming, the Z -yarn frictional force distribution model of the preform and its wear mechanism were investigated. In this study, a tensile force measuring device was designed to measure the force required to replace the guide sleeve, which is equivalent to the Z -yarn frictional forces. The frictional force is proportional to the number of preform layers and is applied to the preform decreased from the corner, edge, sub-edge, and middle in order. A back propagation neural network model was established to predict the friction at different positions of the preform with different layers, and the error was within 1.9%. The wear of Z -yarn was studied at different frictional positions and after different times of successive implantation into the preform. The results showed that with an increase in the number of Z -yarn implantations and frictional forces, the amount of carbon fiber bundle hairiness gradually increased, and the tensile fracture strength damage of the fiber was increasingly affected by the frictional forces. In the corner position of the preform, when the number of implantations was 25, the fiber fracture strength decreased non-linearly and substantially; in order to avoid fiber fracturing in the implantation process, the Z -yarn needs to be replaced in time after 20–25 cycles of continuous implantation. This study solves the problem of difficulty in measuring the force required for individual replacements owing to the excessive number of guide sleeves, puts forward the relationship between fiber wear, preform position, and implantation times, solves the phenomenon of fracture in the preform during Z -direction fiber implantation, and realizes the continuous implantation of fibers. Materials science ; Composite material ; Ultimate tensile strength ; Yarn ; Enhanced Data Rates for GSM Evolution ; Fiber ; Fracture (geology) ; Bundle ; Engineering Textile materials and evaluations ; Mechanical Behavior of Composites ; Mechanical stress and fatigue analysis
Authors: Ainara López-Maestresalas | Carlos López-Molina | Gil Alfonso Oliva-Lobo | Carmen Jarén | José Ignacio Ruíz de Galarreta | Carlos Miguel Peraza-Alemán | Silvia Arazuri Garín
Source: Frontiers in Nutrition (article, 2022-10-17)
Field: Chemistry — Analytical Chemistry

Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude The potato ( Solanum tuberosum L.) is the world’s fifth most important staple food with high socioeconomic relevance. Several potato cultivars obtained by selection and crossbreeding are currently on the market. This diversity causes tubers to exhibit different behaviors depending on the processing to which they are subjected. Therefore, it is interesting to identify cultivars with specific characteristics that best suit consumer preferences. In this work, we present a method to classify potatoes according to their cooking or frying as crisps aptitude using NIR hyperspectral imaging (HIS) combined with a Partial Least Squares Discriminant Analysis (PLS-DA). Two classification approaches were used in this study. First, a classification model using the mean spectra of a dataset composed of 80 tubers belonging to 10 different cultivars. Then, a pixel-wise classification using all the pixels of each sample of a small subset of samples comprised of 30 tubers. Hyperspectral images were acquired using fresh-cut potato slices as sample material placed on a mobile platform of a hyperspectral system in the NIR range from 900 to 1,700 nm. After image processing, PLS-DA models were built using different pre-processing combinations. Excellent accuracy rates were obtained for the models developed using the mean spectra of all samples with 90% of tubers correctly classified in the external dataset. Pixel-wise classification models achieved lower accuracy rates between 66.62 and 71.97% in the external validation datasets. Moreover, a forward interval PLS (iPLS) method was used to build pixel-wise PLS-DA models reaching accuracies above 80 and 71% in cross-validation and external validation datasets, respectively. Best classification result was obtained using a subset of 100 wavelengths (20 intervals) with 71.86% of pixels correctly classified in the validation dataset. Classification maps were generated showing that false negative pixels were mainly located at the edges of the fresh-cut slices while false positive were principally distributed at the central pith, which has singular characteristics. Hyperspectral imaging ; Remote sensing ; Aptitude ; Chemical imaging ; Computer science ; Environmental science ; Artificial intelligence ; Computer vision ; Psychology ; Geology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Abdulrouf Trish | Wael Elhrari | Hussein Etmimi | Abdalah Klash
Source: Journal of Engineering and Applied Science (article, 2022-12-01)
Field: Materials Science — Polymers and Plastics

Analysis of variance of injection moulding process parameters and clay particles size effects on impact strength, shrinkage and warpage of polyethylene/kaolin clay composites Abstract A correlation effect between particle size of kaolin clay and injection moulding process parameters on impact strength, shrinkage and warpage of high-density polyethylene/kaolin clay (HDPE/KC) composites was carried out using analysis of variance (ANOVA). Kaolin clay with particle sizes of &lt; 75, 75–106 and 106–150 μm was used. The process parameters that were taken into consideration were injection temperature, packing pressure and packing time. In general, experimental results showed that the impact strength and shrinkage of the HDPE/KC composites clearly depended on the injection temperature. However, no clear dependency of the warpage on the injection temperature was observed. Furthermore, clay particle size showed to have an influence only on the shrinkage of the composites, where smaller clay particle size led to injected composite parts with relatively less shrinkage. ANOVA showed that the effect of injection temperature on shrinkage of composites containing clay particle sizes of &lt; 75 and 106–150 μm was statistically significant ( p = 0.01 and 0.04, respectively). However, the effect of injection temperature on shrinkage of the composite made with clay having particle sizes of 75–106 μm was not significant ( p = 0.07). ANOVA also indicated that the injection temperature effect on the impact strength of composites that contain clays with particle sizes of &lt; 75 μm and 75–106 μm was significant ( p = 0.03 and p = 0.01, respectively), whereas the injection temperature effect on the impact strength of the composite containing clay with a particle size of 106–150 μm was not significant ( p = 0.17). Contrary to shrinkage and impact strength, the effect of the studied parameters on the warpage was not statistically significant, which was in good agreement with the experimental observations. Shrinkage ; Materials science ; Composite material ; Particle size ; High-density polyethylene ; Composite number ; Particle (ecology) ; Polyethylene ; Izod impact strength test ; Ultimate tensile strength ; Chemistry Natural Fiber Reinforced Composites ; Polymer Foaming and Composites ; Polymer crystallization and properties