jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tahani A. Alrebdi | Abdullah N. Alodhayb | Zoran Ristić | Miroslav D. Dramićanin
Source: Sensors (article, 2023-04-09)
Field: Materials Science — Materials Chemistry

Comparison of Performance between Single- and Multiparameter Luminescence Thermometry Methods Based on the Mn5+ Near-Infrared Emission Herein, we investigate the performance of single- and multiparametric luminescence thermometry founded on the temperature-dependent spectral features of Ca<sub>6</sub>BaP<sub>4</sub>O<sub>17</sub>:Mn<sup>5+</sup> near-infrared emission. The material was prepared by a conventional steady-state synthesis, and its photoluminescence emission was measured from 7500 to 10,000 cm<sup>-1</sup> over the 293-373 K temperature range in 5 K increments. The spectra are composed of the emissions from <sup>1</sup>E → <sup>3</sup>A<sub>2</sub> and <sup>3</sup>T<sub>2</sub> → <sup>3</sup>A<sub>2</sub> electronic transitions and Stokes and anti-Stokes vibronic sidebands at 320 cm<sup>-1</sup> and 800 cm<sup>-1</sup> from the maximum of <sup>1</sup>E → <sup>3</sup>A<sub>2</sub> emission. Upon temperature increase, the <sup>3</sup>T<sub>2</sub> and Stokes bands gained in intensity while the maximum of <sup>1</sup>E emission band is redshifted. We introduced the procedure for the linearization and feature scaling of input variables for linear multiparametric regression. Then, we experimentally determined accuracies and precisions of the luminescence thermometry based on luminescence intensity ratios between emissions from the <sup>1</sup>E and <sup>3</sup>T<sub>2</sub> states, between Stokes and anti-Stokes emission sidebands, and at the <sup>1</sup>E energy maximum. The multiparametric luminescence thermometry involving the same spectral features showed similar performance, comparable to the best single-parameter thermometry. Luminescence ; Photoluminescence ; Emission spectrum ; Materials science ; Emission intensity ; Infrared ; Intensity (physics) ; Redshift ; Analytical Chemistry (journal) ; Optoelectronics ; Optics ; Spectral line ; Chemistry ; Physics ; Astrophysics Luminescence Properties of Advanced Materials ; Optical properties and cooling technologies in crystalline materials ; Solid State Laser Technologies
Authors: Xuan Liu | Na Li | Yirui Huang | Xiujun Lin | Zhenhui Ren
Source: Frontiers in Plant Science (review, 2023-01-26)
Field: Chemistry — Analytical Chemistry

A comprehensive review on acquisition of phenotypic information of Prunoideae fruits: Image technology Fruit phenotypic information reflects all the physical, physiological, biochemical characteristics and traits of fruit. Accurate access to phenotypic information is very necessary and meaningful for post-harvest storage, sales and deep processing. The methods of obtaining phenotypic information include traditional manual measurement and damage detection, which are inefficient and destructive. In the field of fruit phenotype research, image technology is increasingly mature, which greatly improves the efficiency of fruit phenotype information acquisition. This review paper mainly reviews the research on phenotypic information of Prunoideae fruit based on three imaging techniques (RGB imaging, hyperspectral imaging, multispectral imaging). Firstly, the classification was carried out according to the image type. On this basis, the review and summary of previous studies were completed from the perspectives of fruit maturity detection, fruit quality classification and fruit disease damage identification. Analysis of the advantages and disadvantages of various types of images in the study, and try to give the next research direction for improvement. Multispectral image ; Hyperspectral imaging ; Phenotype ; Identification (biology) ; Phenomics ; Maturity (psychological) ; Phenotypic trait ; Computer science ; Artificial intelligence ; Computational biology ; Biology ; Computer vision ; Genomics ; Botany ; Gene ; Genetics ; Genome Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Xuewen Yan | Y. Bai | Wenwen Zhang | Chen Cheng | Jihong Liu
Source: Applied Sciences (article, 2023-09-16)
Field: Materials Science — Materials Chemistry

Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence This paper proposes a method for detecting and recognizing partial discharges in high-voltage (HV) equipment. The aim is to address issues commonly found in traditional systems, including complex operations, high computational demands, significant power consumption, and elevated costs. Various types of discharges were investigated in an HV laboratory environment. Discharge data were collected using a high-frequency current sensor and a microcontroller. Subsequently, this data underwent processing and transformation into feature sets using the phase-resolved partial discharge analysis technique. These features were then converted into grayscale map samples in PNG format. To achieve partial discharge classification, a convolutional neural network (CNN) was trained on these samples. After successful training, the network model was adapted for deployment on a microcontroller, facilitated by the STM32Cube.AI ecosystem, enabling real-time partial discharge recognition. The study also examined storage requirements across different CNN layers and their impact on recognition efficacy. To assess the algorithm’s robustness, recognition accuracy was tested under varying discharge voltages, insulation media thicknesses, and noise levels. The test results demonstrated that the algorithm could be effectively implemented on a microcontroller, achieving a recognition accuracy exceeding 98%. Partial discharge ; Microcontroller ; Computer science ; Artificial intelligence ; Robustness (evolution) ; Convolutional neural network ; Pattern recognition (psychology) ; Voltage ; Computer hardware ; Engineering ; Electrical engineering High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Power Transformer Diagnostics and Insulation
Authors: Hanheng Du | Chengliang Wu | Denghui Li | Wai Sze Yip | Zuankai Wang | Suet To
Source: Journal of Materials Research and Technology (article, 2023-05-26)
Field: Materials Science — Materials Chemistry

Feasibility study on ultraprecision micro-milling of the additively manufactured NiTi alloy for generating microstructure arrays Nickel-titanium (NiTi) alloy has unique functional properties in medical and microelectronics products. Recently, additive manufacturing has become an attractive way of fabricating NiTi alloy. However, the study of generating microstructure arrays on additively manufactured (AMed) NiTi alloy surfaces remains unclear. Motivated by this, this study proposes the ultraprecision micro-milling (UMM) process to generate microstructure arrays and perform a systematic investigation. First, selective laser melting, as a popular additive manufacturing way, was utilized to fabricate the AMed NiTi alloy. Then, the UMM process was carried out to machine these samples. After the machining experiments, a mirror surface with a surface roughness of 0.014 μm can be acquired, which demonstrates the good machinability of the AMed NiTi alloy. Moreover, the micro-groove array and micro-pillar array with low machining errors were machined on the AMed NiTi alloy surfaces, which verifies the effectiveness of the proposed machining process. The corresponding cutting forces, tool conditions, and chip morphologies were studied to fully understand the machining mechanism of the AMed NiTi alloy. Besides, the surface wettability of microstructure arrays was also quantitatively analyzed. Therefore, this study provides a facile and effective machining process for generating microstructure arrays on the AMed NiTi alloy surfaces. Materials science ; Microstructure ; Machinability ; Nickel titanium ; Machining ; Alloy ; Metallurgy ; Titanium alloy ; Shape-memory alloy Shape Memory Alloy Transformations ; Additive Manufacturing Materials and Processes ; High Entropy Alloys Studies
Authors: Giuseppe Bonifazi | Giuseppe Capobianco | Silvia Serranti | Oriana Trotta | Sergio Bellagamba | Sergio Malinconico | Federica Paglietti
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2023-11-20)
Field: Chemistry — Analytical Chemistry

Asbestos detection in construction and demolition waste by different classification methods applied to short-wave infrared hyperspectral images In this study, different multivariate classification methods were applied to hyperspectral images acquired, in the short-wave infrared range (SWIR: 1000-2500 nm), to define and evaluate quality control actions applied to construction and demolition waste (C&DW) flow streams, with particular reference to the detection of hazardous material as asbestos. Three asbestos fibers classes (i.e., amosite, chrysotile and crocidolite) inside asbestos-containing materials (ACM) were investigated. Samples were divided into two groups: calibration and validation datasets. The acquired hyperspectral images were first explored by Principal Component Analysis (PCA). The following multivariate classification methods were selected in order to verify and compare their efficiency and robustness: Hierarchical Partial Least Squares-Discriminant Analysis (Hi-PLSDA), Principal Component Analysis k-Nearest Neighbors (PCA-kNN) and Error Correcting Output Coding with Support Vector Machines (ECOC-SVM). The classification results obtained for the three models were evaluated by prediction maps and the values of performance parameters (Sensitivity and Specificity). Micro-X-ray fluorescence (micro-XRF) maps confirmed the correctness of classification results. The results demonstrate how SWIR-HSI technology, coupled with multivariate analysis modelling, is a promising approach to develop both "off-line" and "online" fast, reliable and robust quality control strategies, finalized to perform a quick assessment of ACM presence. Hyperspectral imaging ; Principal component analysis ; Chrysotile ; Computer science ; Linear discriminant analysis ; Support vector machine ; Pattern recognition (psychology) ; Artificial intelligence ; Robustness (evolution) ; Demolition waste ; Multivariate statistics ; Partial least squares regression ; Asbestos ; Demolition ; Machine learning ; Engineering ; Materials science ; Chemistry Spectroscopy and Chemometric Analyses ; Mineral Processing and Grinding ; Geochemistry and Geologic Mapping
Authors: Chi M. Phan
Source: ACS Omega (article, 2023-12-06)
Field: Chemistry — Organic Chemistry

Affinity of Amphiphilic Molecules to Air/Water Surface The affinity of amphiphiles to the water/air surface was modeled by adapting Eberhart's equation. The proposed method successfully describes surface tension for all amphiphilic structures, including alkanols, carboxylic acids, nonionic, ionic, and Gemini surfactants. The model is more effective than conventional analysis for amphiphiles with multiple ionic states. The prediction was consistently validated at different temperatures and nonaqueous solvents. The modeling results show a linear correlation between surface affinity and hydrophobicity/hydrophilicity. For alkanols, the affinity increment is 2.84 kJ/mol per CH<sub>2</sub> group, the same as the reported hydrophobic energy from monomer to aggregate for nonionic surfactants. For carboxylic acids, the affinity increment per CH<sub>2</sub> group is 3.18 kJ/mol, incorporating the degree of acid dissociation. The affinity-hydrophilicity correlation is approximately -0.22 kJ/mol per oxyethylene group. The affinity constant can be obtained for all classes of amphiphiles to clarify the relationship between the molecular structure and surface activity. Amphiphile ; Surface tension ; Chemistry ; Ionic bonding ; Monomer ; Molecule ; Dissociation constant ; Dissociation (chemistry) ; Carboxylic acid ; Organic chemistry ; Thermodynamics ; Copolymer ; Polymer ; Ion Surfactants and Colloidal Systems ; Spectroscopy and Quantum Chemical Studies ; Phase Equilibria and Thermodynamics
Authors: Yuanyin Jin | Chun Li | Zhengwei Huang | Ling Jiang
Source: Foods (article, 2023-11-26)
Field: Chemistry — Analytical Chemistry

Simultaneous Quantitative Determination of Low-Concentration Preservatives and Heavy Metals in Tricholoma Matsutakes Based on SERS and FLU Spectral Data Fusion As an ingredient of great economic value, Tricholoma matsutake has received widespread attention. However, heavy metal residues and preservatives in it will affect the quality of Tricholoma matsutake and endanger the health of consumers. Here, we present a method for the simultaneous detection of low concentrations of potassium sorbate and lead in Tricholoma matsutakes based on surface-enhanced Raman spectroscopy (SERS) and fluorescence (FLU) spectroscopy to test the safety of consumption. Data fusion strategies combined with multiple machine learning methods, including partial least-squares regression (PLSR), deep forest (DF) and convolutional neural networks (CNN) are used for model training. The results show that combined with reasonable band selection, the CNN prediction model based on decision-level fusion achieves the best performance, the correlation coefficients (<i>R</i><sup>2</sup>) were increased to 0.9963 and 0.9934, and the root mean square errors (<i>RMSE</i>) were reduced to 0.0712 g·kg<sup>-1</sup> and 0.0795 mg·kg<sup>-1</sup>, respectively. The method proposed in this paper accurately predicts preservatives and heavy metals remaining in Tricholoma matsutake and provides a reference for other food safety testing. Partial least squares regression ; Preservative ; Chemistry ; Mathematics ; Food science ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Charalampos Livas | Dionysios Raptis | Emmanuel Tylianakis | George E. Froudakis
Source: Molecules (article, 2023-03-31)
Field: Chemistry — Inorganic Chemistry

Multiscale Theoretical Study of Sulfur Dioxide (SO2) Adsorption in Metal–Organic Frameworks In the present work, we used DFT in order to study the interaction of SO<sub>2</sub> with 41 strategically functionalized benzenes that can be incorporated in MOF linkers. The interaction energy of phenyl phosphonic acid (-PO<sub>3</sub>H<sub>2</sub>) with SO<sub>2</sub> was determined to be the strongest (-10.1 kcal/mol), which is about 2.5 times greater than the binding energy with unfunctionalized benzene (-4.1 kcal/mol). To better understand the nature of SO<sub>2</sub> interactions with functionalized benzenes, electron redistribution density maps of the relevant complexes with SO<sub>2</sub> were created. In addition, three of the top performing functional groups were selected (-PO<sub>3</sub>H<sub>2</sub>, -CNH<sub>2</sub>NOH, -OSO<sub>3</sub>H) to modify the IRMOF-8 organic linker and calculate its SO<sub>2</sub> adsorption capacity with Grand Canonical Monte Carlo (GCMC) simulations. Our results showed a great increase in the absolute volumetric uptake at low pressures, indicating that the suggested functionalization technique can be used to enhance the SO<sub>2</sub> uptake capability not only in MOFs but in a variety of porous materials. Benzene ; Adsorption ; Chemistry ; Metal-organic framework ; Density functional theory ; Linker ; Binding energy ; Computational chemistry ; Sulfur dioxide ; Redistribution (election) ; Sulfur ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Sulfur Compounds in Biology
Authors: Elly Sufriadi | Rinaldi Idroes | Hesti Meilina | Agus Arip Munawar | Lelifajri | Gunawan Indrayanto
Source: ACS Omega (article, 2023-03-22)
Field: Chemistry — Analytical Chemistry

Partial Least Squares-Discriminant Analysis Classification for Patchouli Oil Adulteration Detection by Fourier Transform Infrared Spectroscopy in Combination with Chemometrics This study aims to test chemometrically partial least squares-discriminant analysis (PLS-DA) classification models to detect the adulteration of patchouli oil (PO) with gurjun balsam oil (GBO) by utilization of Fourier transform infrared spectroscopy. Unsupervised analysis was tested using the pattern recognition method using the principal component analysis model against the original spectrum at wavenumbers 4000-500 cm<sup>-1</sup> and at the fingerprint area (1800-600 cm<sup>-1</sup>). Model testing was also carried out on the spectrum that had been pre-processed using the standard normal variate, second derivative Savitzky-Golay, and normalization approaches. Variable Y samples used were certified reference material (CRM), PO, GBO, and PO forged with GBO (PGBO) with a counterfeiting ratio of 0.5 (v/v) to 10% (v/v) with an interval of 0.5%. The same treatment was carried out on testing of the PLS-DA model. In pattern recognition tests, the best separation of the original spectrum was obtained at wavenumbers 1800-600 cm<sup>-1</sup>. The model was further tested on PLS-DA by making assumptions or codes for CRM, PO, GBO, and PGBO as +2, +1, 0, and -1, respectively. The results of the model analysis showed that even at the lowest counterfeiting ratio (0.5%), the presence of counterfeiting material was detected by the PLS-DA model. The RMSEC value is close to zero with a value of 0.22, and the R square is close to 1, which is 0.954. This very significant separation is clearly illustrated in the loading plot and bi-plot due to the contribution of chemical compounds in the GBO that undergo vibration at wavenumbers 603, 786, and 1386 cm<sup>-1</sup>. Validation of the PLS-DA model was carried out strongly using the PLS model, and it showed that the difference between the calibration concentration and the prediction was very low (average 0.45) with an accuracy percent above 99%. The efficacy of the model is further substantiated by the consistent and precise values of sensitivity and selectivity, obtained from both the training set and test set. Chemometrics ; Linear discriminant analysis ; Partial least squares regression ; Patchouli ; Fourier transform infrared spectroscopy ; Fourier transform ; Chromatography ; Pattern recognition (psychology) ; Analytical Chemistry (journal) ; Mathematics ; Artificial intelligence ; Chemistry ; Statistics ; Computer science ; Engineering ; Essential oil Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Aurore Finco | V. Jacques
Source: APL Materials (article, 2023-10-01)
Field: Materials Science — Materials Chemistry

Single spin magnetometry and relaxometry applied to antiferromagnetic materials Despite the considerable interest for antiferromagnets that appeared with the perspective of using them for spintronics, their experimental study, including the imaging of antiferromagnetic textures, remains a challenge. To address this issue, quantum sensors, and, in particular, the nitrogen-vacancy (NV) defects in diamond have become a widespread technical solution. We review here the recent applications of single NV centers to study a large variety of antiferromagnetic materials, from quantitative imaging of antiferromagnetic domains and non-collinear states, to the detection of spin waves confined in antiferromagnetic textures and the non-perturbative measurement of spin transport properties. We conclude with recent developments improving further the magnetic sensitivity of scanning NV microscopy, opening the way to detailed investigations of the internal texture of antiferromagnetic objects. Antiferromagnetism ; Spintronics ; Condensed matter physics ; Magnetometer ; Materials science ; Diamond ; Spin (aerodynamics) ; Texture (cosmology) ; Relaxometry ; Ferromagnetism ; Physics ; Magnetic field ; Spin echo ; Magnetic resonance imaging ; Computer science Diamond and Carbon-based Materials Research ; Electronic and Structural Properties of Oxides ; Metal and Thin Film Mechanics
Authors: Mona A. Abdel Rahman | Mohamed R. Elghobashy | Hala E. Zaazaa | Sally S. El‐Mosallamy
Source: BMC Chemistry (article, 2023-12-07)
Field: Chemistry — Analytical Chemistry

Novel analytical method based on chemometric models applied to UV–Vis spectrophotometric data for simultaneous determination of Etoricoxib and Paracetamol in presence of Paracetamol impurities The multivariate models that are used for spectral data analysis have many beneficial applications, and one of the important applications is the analysis of drugs and their impurities. Three Chemometrically-assisted spectrophotometric models have been proposed and validated. The proposed models are Partial Least Squares (PLS), Artificial Neural Networks (ANN), and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). The advanced chemometric models were applied to resolve the significantly overlapping spectra of Etoricoxib (ETO) and Paracetamol (PCM), along with impurities of PCM namely; P-aminophenol (PAP) and P-hydroxy acetophenone (PHA). The proposed models succeeded in simultaneously analyzing the mixture of ETO and PCM along with the impurities of PCM. So, the proposed techniques can be used without requiring a separation step in the analysis of pharmaceutical formulation. Moreover, no significant differences were found when the results of the suggested and published chemometric models were compared statistically with the reported HPLC method. Etoricoxib ; Partial least squares regression ; Chemometrics ; Impurity ; Multivariate statistics ; Chromatography ; Acetophenone ; Chemistry ; Biological system ; Analytical Chemistry (journal) ; Computer science ; Machine learning ; Organic chemistry Spectroscopy and Chemometric Analyses ; Analytical Methods in Pharmaceuticals ; Water Quality Monitoring and Analysis
Authors: Vanessa Rück | Vlad Andrei Neacșu | Mikkel Baldtzer Liisberg | Christian Brinch Mollerup | Park Hee Ju | Tom Vosch | Jiro Kondo | Cecilia Cerretani
Source: Advanced Optical Materials (article, 2023-11-08)
Field: Materials Science — Materials Chemistry

Atomic Structure of a DNA‐Stabilized Ag<sub>11</sub> Nanocluster with Four Valence Electrons Abstract The combination of mass spectrometry and single crystal X‐ray diffraction of HPLC‐purified DNA‐stabilized silver nanoclusters (DNA‐AgNCs) is a powerful tool to determine the charge and structure of the encapsulated AgNC. Such information is not only relevant to design new DNA‐AgNCs with tailored properties, but it is also important for bio‐conjugation experiments and is essential for electronic structure calculations. Here, the efforts to determine the structure of a HPLC‐purified green emissive DNA‐AgNC are presented. Unfortunately, the original DNA‐AgNC, known to have four valence electrons, could not be crystallized. By modifying the stabilizing DNA sequence, while maintaining the original spectroscopic properties, several mutants could be successfully crystallized, and for one of them, single crystal X‐ray diffraction data provided insight into the silver positions. While the DNA conformation is not resolved, the described approach provides valuable insight into the class of green and dual emissive DNA‐AgNCs with four valence electrons. These results constitute a roadmap on how to improve crystallization and crystal quality for X‐ray diffraction measurements. Nanoclusters ; Materials science ; Valence (chemistry) ; Valence electron ; DNA ; Diffraction ; Crystallography ; Crystallization ; Crystal structure ; X-ray crystallography ; Single crystal ; Electron ; Chemical physics ; Nanotechnology ; Chemistry ; Physics ; Optics Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Gold and Silver Nanoparticles Synthesis and Applications