jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Phạm Thị | Thanh Ngoc Pham | Viorel Chihaia | Quang Anh Vu | Thuat T. Trinh | Trung T. Pham | Thang Van Le | Do Ngoc Son
Source: RSC Advances (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

How do the doping concentrations of N and B in graphene modify the water adsorption? Understanding the interaction of water and graphene is crucial for various applications such as water purification, desalination, and electrocatalysis. Experimental and theoretical studies have already investigated water adsorption on N- and B-doped graphene. However, there are no reports available that elucidate the influences of the N and B doping content in graphene on the microscopic geometrical structure and the electronic properties of the adsorbed water. Thus, this work is devoted to solving this problem using self-consistent van der Waals density functional theory calculations. The N and B doping contents of 0.0, 3.1, 6.3, and 9.4% were considered. The results showed that the binding energy of water increases almost linearly as a function of doping content at all concentrations for N-doped graphene but below 6.3% for B-doped graphene. In the linear range, the binding energy increases by approximately 30 meV for each increment of the doping ratio. Analyses of the geometric and electronic structures explained the enhancement of the water-graphene interaction with the variation in doping percentage. Graphene ; Adsorption ; Doping ; Chemical engineering ; Chemical physics ; Chemistry ; Materials science ; Nanotechnology ; Physical chemistry ; Optoelectronics Graphene research and applications ; Nanopore and Nanochannel Transport Studies ; Solar-Powered Water Purification Methods
Authors: Sanaz Zarei | Amin Khavasi
Source: IEEE Access (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse Design of On-Chip Thermally Tunable Varifocal Metalens Based on Silicon Metalines High-contrast transmitarray (HCTA) metasurfaces, named as metalines in this article, a category of lithographically defined on-chip diffractive elements with minimized scattering loss, enables parallel on-chip optical signal processing. While high-performance devices with different functionalities are reported, most of them are static, and their optical functions are fixed after fabrication. However, dynamically tunable metasurfaces are of essential need in many modern optical systems. Here, we employ an inverse design method, combining FDTD parameter sweep with Fourier-optics simulations, to design thermally-controlled varifocal metalenses. Thermal tunability is accomplished through the thermo-optical effect of silicon. Maximum focal length tunability of 12% and 24% are demonstrated for 400 μm and 200 μm aperture varifocal metalens doublets at the wavelength of 1.55 μm, respectively. The focusing efficiency is more than 74% for the whole temperature range. The 400 μm and 200 μm aperture metalenses are able to concentrate the input beam to spot sizes less than one wavelength and two wavelengths, respectively. This paves the way for dynamically controlled silicon photonics computational chips. Materials science ; Optics ; Silicon ; Numerical aperture ; Aperture (computer memory) ; Photonics ; Optoelectronics ; Fabrication ; Silicon photonics ; Wavelength ; Nanophotonics ; Focal length ; Finite-difference time-domain method ; Lens (geology) ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Photonic Crystals and Applications
Authors: Jian-E Dong | Ji Zhang | Tao Li | Yuanzhong Wang
Source: Frontiers in Microbiology (article, 2021-11-25)
Field: Chemistry — Analytical Chemistry

The Storage Period Discrimination of Bolete Mushrooms Based on Deep Learning Methods Combined With Two-Dimensional Correlation Spectroscopy and Integrative Two-Dimensional Correlation Spectroscopy Boletes are favored by consumers because of their delicious taste and high nutritional value. However, as the storage period increases, their fruiting bodies will grow microorganisms and produce substances harmful to the human body. Therefore, we need to identify the storage period of boletes to ensure their quality. In this article, two-dimensional correlation spectroscopy (2DCOS) images are directly used for deep learning modeling, and the complex spectral data analysis process is transformed into a simple digital image processing problem. We collected 2,018 samples of boletes. After laboratory cleaning, drying, grinding, and tablet compression, their Fourier transform mid-infrared (FT-MIR) spectroscopy data were obtained. Then, we acquired 18,162 spectral images belonging to nine datasets which are synchronous 2DCOS, asynchronous 2DCOS, and integrative 2DCOS (i2DCOS) spectra of 1,750-400, 1,450-1,000, and 1,150-1,000 cm<sup>-1</sup> bands. For these data sets, we established nine deep residual convolutional neural network (ResNet) models to identify the storage period of boletes. The result shows that the accuracy with the train set, test set, and external validation set of the synchronous 2DCOS model on the 1,750-400-cm<sup>-1</sup> band is 100%, and the loss value is close to zero, so this model is the best. The synchronous 2DCOS model on the 1,150-1,000-cm<sup>-1</sup> band comes next, and these two models have high accuracy and generalization ability which can be used to identify the storage period of boletes. The results have certain practical application value and provide a scientific basis for the quality control and market management of bolete mushrooms. In conclusion, our method is novel and extends the application of deep learning in the food field. At the same time, it can be applied to other fields such as agriculture and herbal medicine. Convolutional neural network ; Biological system ; Artificial intelligence ; Two-dimensional nuclear magnetic resonance spectroscopy ; Data set ; Spectroscopy ; Mathematics ; Computer science ; Pattern recognition (psychology) ; Chemistry ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Fungal Biology and Applications ; Phytochemicals and Antioxidant Activities
Authors: Matthias Köck | Michael Reggelin | Stefan Immel
Source: Marine Drugs (article, 2021-05-21)
Field: Chemistry — Spectroscopy

Model-Free Approach for the Configurational Analysis of Marine Natural Products The NMR-based configurational analysis of complex marine natural products is still not a routine task. Different NMR parameters are used for the assignment of the relative configuration: NOE/ROE, homo- and heteronuclear <i>J</i> couplings as well as anisotropic parameters. The combined distance geometry (DG) and distance bounds driven dynamics (DDD) method allows a model-free approach for the determination of the relative configuration that is invariant to the choice of an initial starting structure and does not rely on comparisons with (DFT) calculated structures. Here, we will discuss the configurational analysis of five complex marine natural products or synthetic derivatives thereof: the <i>cis</i>-palau'amine derivatives <b>1a</b> and <b>1b</b>, tetrabromostyloguanidine (<b>1c</b>), plakilactone H (<b>2</b>), and manzamine A (<b>3</b>). The certainty of configurational assignments is evaluated in view of the accuracy of the NOE/ROE data available. These case studies will show the prospective breadth of application of the DG/DDD method. Heteronuclear molecule ; Chemistry ; Invariant (physics) ; Computational chemistry ; Biological system ; Stereochemistry ; Nuclear magnetic resonance spectroscopy ; Mathematics Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Advanced NMR Techniques and Applications
Authors: Julius Krause | Heinrich Grüger | Lucie Gebauer | Xiaorong Zheng | Jens Knobbe | Tino Pügner | Anna Kicherer | Robin Gruna | Thomas Längle | Jürgen Beyerer
Source: Sensors (article, 2021-06-30)
Field: Chemistry — Analytical Chemistry

SmartSpectrometer—Embedded Optical Spectroscopy for Applications in Agriculture and Industry The ongoing digitization of industry and agriculture can benefit significantly from optical spectroscopy. In many cases, optical spectroscopy enables the estimation of properties such as substance concentrations and compositions. Spectral data can be acquired and evaluated in real time, and the results can be integrated directly into process and automation units, saving resources and costs. Multivariate data analysis is needed to integrate optical spectrometers as sensors. Therefore, a spectrometer with integrated artificial intelligence (AI) called <i>SmartSpectrometer</i> and its interface is presented. The advantages of the <i>SmartSpectrometer</i> are exemplified by its integration into a harvesting vehicle, where quality is determined by predicting sugar and acid in grapes in the field. Automation ; Spectrometer ; Digitization ; Spectroscopy ; Interface (matter) ; Process (computing) ; Precision agriculture ; Computer science ; Agriculture ; Process engineering ; Environmental science ; Remote sensing ; Engineering ; Telecommunications ; Optics ; Physics ; Geography ; Mechanical engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Alexandre Mallet | Cyrille Charnier | Éric Latrille | Ryad Bendoula | Jean‐Philippe Steyer | Jean‐Michel Roger
Source: Waste Management (article, 2021-01-19)
Field: Chemistry — Analytical Chemistry

Unveiling non-linear water effects in near infrared spectroscopy: A study on organic wastes during drying using chemometrics Chemometrics ; Water content ; Context (archaeology) ; Infrared spectroscopy ; Infrared ; Spectroscopy ; Calibration ; Near-infrared spectroscopy ; Chemistry ; Principal component analysis ; Analytical Chemistry (journal) ; Biological system ; Environmental science ; Environmental chemistry ; Optics ; Computer science ; Mathematics ; Chromatography ; Artificial intelligence ; Geology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Food Drying and Modeling
Authors: Ravi Raj Purohit Purushottam Raj Purohit | Thiebaud Richeton | Stéphane Berbenni | Lionel Germain | Nathalie Gey | Thomas Connolley | O. Castelnau
Source: Acta Materialia (article, 2021-02-21)
Field: Materials Science — Materials Chemistry

Estimating single-crystal elastic constants of polycrystalline β metastable titanium alloy: A Bayesian inference analysis based on high energy X-ray diffraction and micromechanical modeling Materials science ; Crystallite ; Titanium alloy ; Shear modulus ; Diffraction ; Elastic modulus ; Lattice constant ; Alloy ; Composite material ; Crystallography ; Thermodynamics ; Metallurgy ; Optics ; Physics ; Chemistry Titanium Alloys Microstructure and Properties ; Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels
Authors: Rei Sonobe | Yudai SUGIMOTO | Ryohei Kondo | Haruyuki Seki | Erika Sugiyama | Yoshikazu Kiriiwa | Katsumi Suzuki
Source: European Journal of Remote Sensing (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral wavelength selection for estimating chlorophyll content of muskmelon leaves Quantifying chlorophyll content, an effective indicator of disease as well as nutritional and environmental stresses on plants, may enable optimal fertilization while managing crops. Hyperspectral remote-sensing is commonly used to estimate chlorophyll content. In this context, the process of variable selection is crucial since it is necessary to identify variables relevant to chlorophyll and eliminate redundant variables. In this study, 14 wavelength selection methods based on partial least squares (PLS; namely, backward variable elimination, backward and forward interval-PLS, competitive adaptive reweighted sampling, genetic algorithm, iterative predictive weighting, loading-weights, PLS with Martens’ uncertainty test, regression coefficient, regularized elimination procedure, sparse-PLS, sub-window permutation analysis, uninformative variable elimination and variable importance in projection) were combined with one of five machine learning algorithms (Cubist, deep belief nets, random forests, stochastic gradient boosting and support vector machine) and then evaluated. According to the ratio of performance to deviation (RPD), the best combination of variable selection method and machine learning algorithm was regularized elimination procedure and Cubist achieving an RPD of 1.76 and an RMSE of 2.42 μg cm−2. Hyperspectral imaging ; Variable elimination ; Feature selection ; Partial least squares regression ; Weighting ; Context (archaeology) ; Artificial intelligence ; Computer science ; Boosting (machine learning) ; Random forest ; Mathematics ; Pattern recognition (psychology) ; Algorithm ; Machine learning ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Alok Mukherjee | Kingshuk Chatterjee | Tanmay Sarkar | S Dubey | A Jalal | A Danti | M Madgi | B Anami | A Antonelli | M Cocchi | P Fava | G Foca | G Franchini | D Manzini | A Ulrici | F Zhu | M Bosch | N Khanna | C Boushey | E Delp | J Farrugia | S Griffin | V Valdramidis | K Camilleri | O Falzon | F Lpez-Garca | G Andreu-Garca | J Blasco | N Aleixos | J.-M Valiente | A Gowen | C O'donnell | M Taghizadeh | P Cullen | J Frias | G Downey | Y Sun | K Wei | Q Liu | L Pan | K Tu | Y Liu | Y Chen | C Wang | D Chan | M Kim | Y Han | Z Liu | K Khoshelham | S Bai | B Zhu | L Jiang | Y Luo | Y Tao | B Peterson | A Tabb | A Bhargava | A Bansal | S Fan | J Li | Y Zhang | X Tian | Q Wang | X He | C Zhang | W Huang | L Wu | D Granato | J Santos | G Escher | B Ferreira | R Maggio | S Wang | G Ji | J Yang | J Wu | L Wei | P Tao | X Feng | C Wen | P Baranowski | W Mazurek | J Wozniak | U Majewska | J Zhao | Q Ouyang | Q Chen | J Wang | D Ariana | R Lu | D Guyer | W Tan | C Zhao | H Wu | J Naranjo-Torres | M Mora
Source: Biointerface Research in Applied Chemistry (article, 2021-06-18)
Field: Chemistry — Analytical Chemistry

Entropy-Aided Assessment of Amla (Emblica officinalis) Quality Using Principal Component Analysis The texture analysis-based scheme for identifying non-consumable amla fruit (Emblica officinalis) samples is proposed here. The method uses entropy analysis to detect wrinkles and irregularities developed on the fruit surface with progression in time. Since entropy is one of the major tools used to detect the randomness of data, it is used here to identify these surface irregularities, which are almost absent in fresh samples. Based on these features, the edibility of the samples is predicted. Principal component analysis (PCA) further analyzes these entropy features to enhance the most important directions of variations, followed by a threshold-based segmentation scheme to detect rotten samples. The method possesses less computational burden as it applies PCA and entropy only; it is highly efficient to yield a high detection accuracy of 93.33%; hence, it is easy for real-life implementation. Emblica officinalis ; Principal component analysis ; Randomness ; Entropy (arrow of time) ; Computer science ; Artificial intelligence ; Segmentation ; Mathematics ; Pattern recognition (psychology) ; Entropy estimation ; Data mining ; Statistics ; Traditional medicine ; Medicine ; Physics Spectroscopy and Chemometric Analyses ; Food Science and Nutritional Studies ; Postharvest Quality and Shelf Life Management
Authors: Lawrence Chai | Ping Zhu | Jin Chai | Changxu Pang | Babak Andi | Seán McSweeney | John Shanklin | Qun Liu
Source: Crystals (article, 2021-10-12)
Field: Materials Science — Materials Chemistry

AlphaFold Protein Structure Database for Sequence-Independent Molecular Replacement Crystallographic phasing recovers the phase information that is lost during a diffraction experiment. Molecular replacement is a commonly used phasing method for crystal structures in the protein data bank. In one form it uses a protein sequence to search a structure database to find suitable templates for phasing. However, sequence information is not always available, such as when proteins are crystallized with unknown binding partner proteins or when the crystal is of a contaminant. The recent development of AlphaFold published the predicted protein structures for every protein from twenty distinct species. In this work, we tested whether AlphaFold-predicted E. coli protein structures were accurate enough to enable sequence-independent phasing of diffraction data from two crystallization contaminants of unknown sequence. Using each of more than 4000 predicted structures as a search model, robust molecular replacement solutions were obtained, which allowed the identification and structure determination of YncE and YadF. Our results demonstrate the general utility of the AlphaFold-predicted structure database with respect to sequence-independent crystallographic phasing. Phaser ; Molecular replacement ; Protein Data Bank ; Sequence (biology) ; Protein structure ; Crystallography ; Protein crystallization ; Crystal structure ; Protein structure database ; Computational biology ; X-ray crystallography ; Database ; Protein sequencing ; Diffraction ; Crystallization ; Peptide sequence ; Computer science ; Chemistry ; Sequence database ; Biology ; Genetics ; Physics ; Biochemistry ; Gene ; Optics Enzyme Structure and Function ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Sedong Lee | P.L. Narayana | Bang Won Seok | Bharat B. Panigrahi | Su-Gun Lim | N.S. Reddy
Source: Journal of Materials Research and Technology (article, 2021-01-20)
Field: Materials Science — Materials Chemistry

Quantitative estimation of corrosion rate in 3C steels under seawater environment An artificial neural network method is proposed to correlate the relationship between the corrosion rate of 3C steels with seawater environment factors. The predictions with the unseen test data are in good agreement with experimental values. Further, the developed model used to simulate the combined effect of environmental factors (temperature, dissolved oxygen, salinity, pH values, and oxidation-reduction potential) on the corrosion rate. 3D mappings remarkably reveal the complex interrelationship between the input environmental parameters on the output corrosion rate. The quantitative estimation of corrosion by virtual addition/subtraction of environmental factors individually to a hypothetical system helps to understand the impact of each parameter. Corrosion ; Seawater ; Materials science ; Salinity ; Artificial neural network ; Estimation ; Metallurgy ; Biological system ; Artificial seawater ; Soil science ; Environmental science ; Computer science ; Artificial intelligence ; Oceanography ; Engineering ; Geology Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Non-Destructive Testing Techniques
Authors: Jiangping Qin | Yan Zhang | Huan Zhou | Yu Feng | Bo Sun | Qisheng Wang
Source: Crystals (article, 2021-02-04)
Field: Materials Science — Materials Chemistry

Protein Crystal Instance Segmentation Based on Mask R-CNN Protein crystallization is the bottleneck in macromolecular crystallography, and crystal recognition is a very important step in the experiment. To improve the recognition accuracy by image classification algorithms further, the Mask R-CNN model is introduced for the detection of protein crystals in this paper. Because the protein crystal image is greatly affected by backlight and precipitate, the contrast limit adaptive histogram equalization (CLAHE) is applied with Mask R-CNN. Meanwhile, the Transfer Learning method is used to optimize the parameters in Mask R-CNN. Through the comparison experiments between this combined algorithm and the original algorithm, it shows that the improved algorithm can effectively improve the accuracy of segmentation. Adaptive histogram equalization ; Protein crystallization ; Artificial intelligence ; Computer science ; Segmentation ; Pattern recognition (psychology) ; Bottleneck ; Crystal (programming language) ; Histogram equalization ; Image (mathematics) ; Limit (mathematics) ; Histogram ; Computer vision ; Algorithm ; Crystallization ; Mathematics ; Chemistry Enzyme Structure and Function ; Cell Image Analysis Techniques ; Image Processing Techniques and Applications
Authors: Hongzhe Jiang | Yi Yang | Minghong Shi
Source: Foods (article, 2021-09-09)
Field: Chemistry — Analytical Chemistry

Chemometrics in Tandem with Hyperspectral Imaging for Detecting Authentication of Raw and Cooked Mutton Rolls Authentication assurance of meat or meat products is critical in the meat industry. Various methods including DNA- or protein-based techniques are accurate for assessing meat authenticity, however, they are destructive, expensive, or laborious. This study explores the feasibility of chemometrics in tandem with hyperspectral imaging (HSI) for identifying raw and cooked mutton rolls substitution by pork and duck rolls. Raw or cooked samples (<i>n</i> = 180) of three meat species were prepared to collect hyperspectral images in range of 400-1000 nm. Spectra were extracted from representative regions of interest (ROIs), and spectral principal component analysis (PCA) revealed that PC<sub>1</sub> and PC<sub>2</sub> were effective for the identification. Different methods including standard normal variable (SNV), first and second derivatives, and normalization were individually employed for spectral preprocessing, and modeling methods of partial least squares-discriminant analysis (PLS-DA) and support vector machines (SVM) were also individually applied to develop classification models for both the raw and the cooked. Results showed that PLS-DA model developed by raw spectra presented the highest 100% correct classification rate (CCR) of success in all sets. After that, effective wavelengths selected by successive projections algorithm (SPA) built optimal simplified models which didn't influence the modeling results compared with full spectra regardless of the meat roll states. Therefore, SPA-PLS-DA models were subsequently used to visualize the raw and cooked meat rolls classification. As a consequence, the general meat species of both raw and cooked meat rolls were readily discernible in pixel-wise manner by generating classification maps. The results showed that HSI combined with chemometrics can be used to identify the authentication of raw and cooked mutton rolls substituted by pork and duck rolls accurately. This promising methodology provides a reference which can be extended to the classification or grading of other meat rolls. Hyperspectral imaging ; Chemometrics ; Principal component analysis ; Pattern recognition (psychology) ; Linear discriminant analysis ; Partial least squares regression ; Artificial intelligence ; Raw meat ; Support vector machine ; Preprocessor ; Normalization (sociology) ; Raw material ; Mathematics ; Food science ; Computer science ; Biological system ; Chemistry ; Machine learning ; Biology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies