jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kazuya Kanasugi | Keita Arimura | Ali Alanazi | Yasuharu Ohgoe | Yoshinobu Manome | Masanori Hiratsuka | Kenji Hirakuri
Source: Bioengineering (article, 2022-09-26)
Field: Materials Science — Materials Chemistry

UV Sterilization Effects and Osteoblast Proliferation on Amorphous Carbon Films Classified Based on Optical Constants Optical classification methods that distinguish amorphous carbon films into six types based on refractive index and extinction coefficient have garnered increasing attention. In this study, five types of amorphous carbon films were prepared on Si substrates using different plasma processes, including physical and chemical vapor deposition. The refractive index and extinction coefficient of the amorphous carbon films were measured using spectroscopic ellipsometry, and the samples were classified into five amorphous carbon types-amorphous, hydrogenated amorphous, tetrahedral amorphous, polymer-like, and graphite-like carbon-based on optical constants. Each amorphous carbon type was irradiated with 253.7 nm UV treatment; the structure and surface properties of each were investigated before and after UV treatment. No significant changes were observed in film structure nor surface oxidation after UV sterilization progressed at approximately the same level for all amorphous carbon types. Osteoblast proliferation associated with amorphous carbon types was evaluated in vitro. Graphite-like carbon, which has relatively high surface oxidation levels, was associated with higher osteoblast proliferation levels than the other carbon types. Our findings inform the selection of suitable amorphous carbon types based on optical constants for use in specific medical devices related to osteoblasts, such as artificial joints and dental implants. Amorphous carbon ; Materials science ; Carbon film ; Amorphous solid ; Graphite ; Refractive index ; Chemical engineering ; Analytical Chemistry (journal) ; Nanotechnology ; Composite material ; Thin film ; Organic chemistry ; Optoelectronics ; Chemistry Diamond and Carbon-based Materials Research ; Dental materials and restorations ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Tatiana Osheter | Salvatore Campisi‐Pinto | Maysa Teixeira Resende | Charles Linder | Zeev Wiesman
Source: Molecules (article, 2022-09-16)
Field: Chemistry — Analytical Chemistry

1H LF-NMR Self-Diffusion Measurements for Rapid Monitoring of an Edible Oil’s Food Quality with Respect to Its Oxidation Status The food quality of edible oils is dependent on basic chemical and structural changes that can occur by oxidation during preparation and storage. A rapid and efficient analytical method of the different steps of oil oxidation is described using a time-domain nuclear magnetic resonance (TD-NMR) sensor for measuring signals related to the chemical and physical properties of the oil. The degree of thermal oxidation of edible oils at 80 °C was measured by the conventional methodologies of peroxide and aldehyde analysis. Intact non-modified samples of the same oils were more rapidly analyzed for oxidation using a TD-NMR sensor for 2D T<sub>1</sub>-T<sub>2</sub> and self-diffusion (D) measurements. A good linear correlation between the D values and the conventional chemical analysis was achieved, with the highest correlation of R<sup>2</sup> = 0.8536 for the D vs. the aldehyde concentrations during the thermal oxidation of poly-unsaturated linseed oils, the oil most susceptible to oxidation. A good correlation between the D and aldehyde levels was also achieved for all the other oils. The possibility to simplify and minimize the time of oxidative analysis using the TD NMR sensors D values is discussed as an indicator of the oil's oxidation quality, as a rapid and accurate methodology for the oil industry. Aldehyde ; Chemistry ; Linseed oil ; Peroxide value ; Peroxide ; Thermal oxidation ; Diffusion ; Proton NMR ; Carbon-13 NMR ; Analytical Chemistry (journal) ; Organic chemistry ; Catalysis ; Layer (electronics) ; Thermodynamics Spectroscopy and Chemometric Analyses ; NMR spectroscopy and applications ; Fermentation and Sensory Analysis
Authors: R. Kažys | Liudas Mažeika | Vykintas Samaitis | Reimondas Šliteris | Peter Merck | Žydrius Viliūnas
Source: Materials (article, 2022-06-28)
Field: Materials Science — Metals and Alloys

Comparative Analysis of Ultrasonic NDT Techniques for the Detection and Characterisation of Hydrogen-Induced Cracking The article is devoted to the investigation of ultrasonic inspection techniques suitable for detecting hydrogen-induced cracking (HIC) and a high-temperature hydrogen attack (HTHA), which are of great importance in petrochemical and refinery industries. Four techniques were investigated: total focusing method (TFM), advanced velocity ratio (AVR) measurement, advanced ultrasonic backscatter technique (AUBT) and time of flight diffraction method using ultra low angle ultrasonic transducers (TULA). The experimental investigation has been carried out on two carbon steel samples cut off from a heat exchanger of an oil refinery and potentially affected by HIC. It was shown that the AVR technique did not reveal any damage and was not effective in the case of the investigated samples due to a thin damage zone with respect to the total thickness of the samples. The AUBT method enabled us to indicate and classify the presence of the hydrogen-induced damage; however, it is complicated to use in practise due to the need perform measurements exactly at the same position using two transducers of different frequencies. The method is more suitable for the verification of damage at a particular position, rather than for scanning. Both other methods-TFM and TULA-enabled us to identify the presence of HIC in large areas of samples. The obtained results have been verified using a metallographic analysis of the section cut from the side of the sample. The results of metallographic examinations have been compared with indications observed using above mentioned techniques and a good correspondence was obtained. It was demonstrated, that the TFM method can detect cracks with dimensions close to 200 µm, while larger cracks of 2 mm were observed very evidently using a 7.5 MHz phased array. Overall, the results suggested that the TULA method is the most suitable method for the primary detection of hydrogen-induced cracking, while the TFM is recommended for the precise assessment of the extent of the detected cracking. Nondestructive testing ; Cracking ; Materials science ; Ultrasonic sensor ; Ultrasonic testing ; Hydrogen ; Composite material ; Forensic engineering ; Metallurgy ; Acoustics ; Engineering ; Chemistry ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Material Properties and Failure Mechanisms
Authors: Hehuan Peng | Chang Zhang | Zhizhong Sun | Tong Sun | Dong Hu | Zidong Yang | Jinshuang Wang
Source: Frontiers in Plant Science (article, 2022-04-25)
Field: Chemistry — Analytical Chemistry

Optical Property Mapping of Apples and the Relationship With Quality Properties This paper reports on the measurement of optical property mapping of apples at the wavelengths of 460, 527, 630, and 710 nm using spatial-frequency domain imaging (SFDI) technique, for assessing the soluble solid content (SSC), firmness, and color parameters. A laboratory-based multispectral SFDI system was developed for acquiring SFDI of 140 "Golden Delicious" apples, from which absorption coefficient (<i>μ <sub><i>a</i></sub> </i> ) and reduced scattering coefficient (<i>μ <sub><i>s</i></sub> '</i>) mappings were quantitatively determined using the three-phase demodulation coupled with curve-fitting method. There was no noticeable spatial variation in the optical property mapping based on the resulting effect of different sizes of the region of interest (ROI) on the average optical properties. Support vector machine (SVM), multiple linear regression (MLR), and partial least square (PLS) models were developed based on <i>μ <sub><i>a</i></sub> </i> , <i>μ <sub><i>s</i></sub> '</i> and their combinations (<i>μ <sub><i>a</i></sub> </i> × <i>μ <sub><i>s</i></sub> '</i> and <i>μ <sub><i>eff</i></sub> </i> ) for predicting apple qualities, among which SVM outperformed the best. Better prediction results for quality parameters based on the <i>μ <sub><i>a</i></sub> </i> were observed than those based on the <i>μ <sub><i>s</i></sub> '</i>, and the combinations further improved the prediction performance, compared to the individual <i>μ <sub><i>a</i></sub> </i> or <i>μ <sub><i>s</i></sub> '</i>. The best prediction models for SSC and firmness parameters [slope, flesh firmness (FF), and maximum force (Max.F)] were achieved based on the <i>μ <sub><i>a</i></sub> </i> × <i>μ <sub><i>s</i></sub> '</i>, whereas those for color parameters of b* and C* were based on the <i>μ <sub><i>eff</i></sub> </i> , with the correlation coefficients of prediction as 0.66, 0.68, 0.73, 0.79, 0.86, and 0.86, respectively. Correlation coefficient ; Mathematics ; Support vector machine ; Property (philosophy) ; Multispectral image ; Biological system ; Artificial intelligence ; Correlation ; Linear regression ; Demodulation ; Pattern recognition (psychology) ; Computer science ; Statistics ; Geometry Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Postharvest Quality and Shelf Life Management
Authors: Nidhal Ben Khedher | Fatih Selımefendıgıl | Lioua Kolsi | Walid Aich | Lotfi Ben Said | Ismail Boukholda
Source: Mathematics (article, 2022-03-27)
Field: Materials Science — Materials Chemistry

Performance Optimization of a Thermoelectric Device by Using a Shear Thinning Nanofluid and Rotating Cylinder in a Cavity with Ventilation Ports The combined effects of using a rotating cylinder and shear thinning nanofluid on the performance improvements of a thermoelectric generator (TEG)-installed cavity with multiple ventilation ports are numerically assessed. An optimization algorithm is used to find the best location, rotational speed and size of the cylinder to deliver the highest power generation of the TEG. The power generation features with varying Rew are different for the first nanofluid (NF1) when compared to the second one (NF2). The power rises with higher Rew when NF1 is used, and up to 49% enhancement is obtained. The output power variation between nanofluids NF1 and NF2 is the highest at Rew = 0, which is obtained as 68.5%. When the cylinder location is varied, the change in the output power becomes 61% when NF2 is used. The optimum case has 11.5%- and 161%-higher generated power when compared with the no-object case with NF1 and NF2. The computational effort of using the high-fidelity coupled system is reduced when optimization is considered. Nanofluid ; Cylinder ; Materials science ; Mechanics ; Power (physics) ; Thermoelectric generator ; Mechanical engineering ; Nuclear engineering ; Thermoelectric effect ; Engineering ; Physics ; Heat transfer ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Adsorption and Cooling Systems
Authors: Danial Molavitabrizi | Haiyang Yu | S. Mousavi
Source: Engineering Fracture Mechanics (article, 2022-09-09)
Field: Materials Science — Materials Chemistry

Hydrogen embrittlement in micro-architectured materials Hydrogen embrittlement is a classical problem in bulk materials while it is rather untouched for advanced materials such as micro-architectured materials. This can be a barrier to industrial adoption of these materials where hydrogen is present as a popular source of energy. In this study, we developed a numerical scheme to assess the hydrogen degradation in metallic micro-architectured materials. The numerical scheme is built on the concept of elastoplastic homogenization and two hydrogen embrittlement theories, i.e. hydrogen enhanced decohesion (HEDE) and hydrogen enhanced localized plasticity (HELP). The use of homogenization allows for explicit definition of a unit-cell, drastically improving the computation time. The hydrogen degradation loci of two specific micro-architectured materials, that is cubic (with 10%, 20% and 30% relative densities) and body-center cubic (with 20% relative density), are numerically characterized. Additionally, the influence of unit-cell topology, relative density, and trap hydrogen on the degradation of homogenized macroscopic material is determined. Finally, a unique failure locus is provided for generic cubic unit-cell with arbitrary relative densities. This degradation law is independent of the relative density and can be interpreted as a material property, contributing to the material design charts. Materials science ; Homogenization (climate) ; Hydrogen embrittlement ; Hydrogen ; Embrittlement ; Plasticity ; Composite material ; Physics Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Mechanical stress and fatigue analysis
Authors: Bin Li | Feng Zhang | Yande Liu | Hai Yin | Ji‐ping Zou | Aiguo Ouyang
Source: RSC Advances (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Quantitative study of impact damage on yellow peaches based on reflectance, absorbance and Kubelka–Munk spectral data Impact damage is one of the main forms of damage during the postharvest transportation and processing of yellow peaches. Thus, a quantitative prediction of the impact damage degree of yellow peaches is significant for their postharvest grading. In the present study, mechanical parameters such as the damage area, absorbed energy and maximum force were obtained based on a single pendulum collision device and an intelligent data acquisition system. The reflection spectra (R) of the damaged areas of yellow peaches were collected by a hyperspectral imaging system and transformed into absorbance (A) spectra and Kubelka-Munk (K-M) spectra. The <i>R</i>, <i>A</i> and K-M spectra were preprocessed by standard normal variables (SNV), moving average (MA) and Gaussian filtering (GF). Partial least squares regression (PLSR) models and support vector regression (SVR) models based on original and preprocessed spectra were established, respectively. By comparative analysis, the spectral data with better prediction performance (raw or preprocessed spectra) were selected from all spectra, and the characteristic wavelengths were selected by competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE). The PLSR and SVR models based on characteristic wavelengths were established. The results revealed that the prediction performance of the K-M-GF-CARS-PLSR model is the best. For the damage area, absorbed energy and maximum force, the <i>R</i> <sub>P</sub> <sup>2</sup> and RMSEP of the K-M-GF-CARS-PLSR model were 0.870 and 77.865 mm<sup>2</sup>, 0.772 and 1.065 J, 0.895 and 47.996 N, respectively. Furthermore, the values of their RPD were 2.700, 1.768 and 3.050, respectively. The characteristic wavelengths of the model were 18.8%, 10.2% and 21.6%, respectively. The results of this study showed that there was a strong correlation between the mechanical parameters and K-M spectrum, which demonstrates the feasibility of quantitatively predicting the damage degree of yellow peaches based on the K-M spectrum. Therefore, the results of this work not only provide theoretical guidance for the postharvest grading of fruits, but also enrich the theoretical system of biomechanics. Absorbance ; Reflectivity ; Remote sensing ; Materials science ; Chemistry ; Optics ; Geology ; Physics ; Chromatography Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Food composition and properties
Authors: Olga Valencia | M.C. Ortiz | Santiago Ruiz | M.S. Sánchez | L.A. Sarabia
Source: Chemometrics and Intelligent Laboratory Systems (article, 2022-06-29)
Field: Chemistry — Analytical Chemistry

Simultaneous class-modelling in chemometrics: A generalization of Partial Least Squares class modelling for more than two classes by using error correcting output code matrices The paper presents a new methodology within the framework of the so-called compliant class-models, PLS2-CM, designed with the purpose of improving the performance of class-modelling in a setting with more than two classes. The improvement in the class-models is achieved through the use of multi-response PLS models with the classes encoded via Error-Correcting Output Codes (ECOC), instead of the traditional class indicator variables used in chemometrics. The proposed PLS2-CM entails a decomposition of a class-modelling problem into a series of binary learners, based on a family of code matrices with different code length, which are evaluated to obtain simultaneous compliant class-models with the best performance. The methodology develops both a new encoding system, based on multi-criteria optimization to search for optimal coding matrices, and a new decoding system, based on probability thresholds to assign objects to class-models. The whole procedure implies that the characteristics of the dataset at hand affect the final selection of the coding matrix and therefore of built class-models, thus giving rise to a data-driven strategy. The application of PLS2-CM to a variety of cases (controlled data, experimental data and repository datasets) results in an enhanced class-modelling performance by means of the suggested procedure, as measured by the DMCEN (Diagonal Modified Confusion Entropy) index and by sensitivity-specificity matrices. The predictive ability of the compliant class-models has been evaluated. Computer science ; Coding (social sciences) ; Chemometrics ; Partial least squares regression ; Class (philosophy) ; Algorithm ; Data mining ; Mathematics ; Artificial intelligence ; Machine learning ; Statistics Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Chemical Sensor Technologies
Authors: Shymaa S. Soliman | Alaadin E. El-Haddad | Ghada A. Sedik | Mohamed R. Elghobashy | Hala E. Zaazaa | Ahmed S. Saad
Source: RSC Advances (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Experimentally designed chemometric models for the assay of toxic adulterants in turmeric powder Turmeric is an indispensable culinary spice in different cultures and a principal component in traditional remedies. Toxic metanil yellow (MY), acid orange 7 (AO) and lead chromate (LCM) are deliberately added to adulterate turmeric powder. This work compares the ability of multivariate chemometric models with those of artificial intelligent networks to enhance the selectivity of spectral data for the rapid assay of these three adulterants in turmeric powder. Using a custom experimental design, we provide a data-driven optimization for the sensitive parameters of the partial least squares model (PLS), artificial neural network (ANN) and genetic algorithm (GA). The optimized models are validated using sets of genuine turmeric samples from five different geographical regions spiked with standard adulterant concentrations. The optimized GA-PLS and GA-ANN models reduce the root mean square error of prediction by 18.4%, 31.1% and 55.3% and 25.0%, 69.9% and 88.4% for MY, AO and LCM, respectively. Adulterant ; Partial least squares regression ; Chemometrics ; Artificial neural network ; Principal component analysis ; Mathematics ; Biological system ; Chemistry ; Computer science ; Statistics ; Artificial intelligence ; Chromatography ; Biology Spectroscopy and Chemometric Analyses ; Curcumin's Biomedical Applications ; Dye analysis and toxicity
Authors: Komal Sharma | Ganesh Kumar Sethi | Rajesh K. Bawa
Source: Journal of Scientific Research (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

A Hybrid Approach of Preprocessing and Segmentation Techniques in Automatic Rice Variety Identification System Image processing techniques play an important role with various images such as rice grain identification, wheat, fruits, medical, vehicle, and digital text images in image acquisition, image preprocessing, clustering, segmentation, and classification techniques. In the application of object detection and classification images, preprocessing and segmentation techniques are used. This paper delves into the specifics of automated segmentation processes, focusing on rice variety identification and classification images. The aim is to talk about the issues that come up when segmenting digital images and the relative merits and drawbacks of the various methods for preprocessing and segmenting images that are currently available. In this paper, we propose a hybrid approach of Preprocessing and Segmentation techniques to develop an automatic rice variety identification system. Preprocessor ; Artificial intelligence ; Computer science ; Segmentation ; Identification (biology) ; Pattern recognition (psychology) ; Image segmentation ; Cluster analysis ; Computer vision ; Image processing ; Scale-space segmentation ; Market segmentation ; Segmentation-based object categorization ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: John A. Cormican | Wai Tuck Soh | Michele Mishto | Juliane Liepe
Source: PROTEOMICS (article, 2022-10-03)
Field: Chemistry — Spectroscopy

iBench: A ground truth approach for advanced validation of mass spectrometry identification method The discovery of many noncanonical peptides detectable with sensitive mass spectrometry inside, outside, and on cells shepherded the development of novel methods for their identification, often not supported by a systematic benchmarking with other methods. We here propose iBench, a bioinformatic tool that can construct ground truth proteomics datasets and cognate databases, thereby generating a training court wherein methods, search engines, and proteomics strategies can be tested, and their performances estimated by the same tool. iBench can be coupled to the main database search engines, allows the selection of customized features of mass spectrometry spectra and peptides, provides standard benchmarking outputs, and is open source. The proof-of-concept application to tryptic proteome digestions, immunopeptidomes, and synthetic peptide libraries dissected the impact that noncanonical peptides could have on the identification of canonical peptides by Mascot search with rescoring via Percolator (Mascot+Percolator). Mascot ; Benchmarking ; Identification (biology) ; Proteome ; Proteomics ; Computer science ; Database search engine ; Construct (python library) ; Mass spectrometry ; Ground truth ; Computational biology ; Data mining ; Chemistry ; Artificial intelligence ; Chromatography ; Information retrieval ; Search engine ; Biology ; Biochemistry ; Programming language Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Mass Spectrometry Techniques and Applications