jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: В. Г. Поваров | Ignaty Efimov
Source: Journal of Mining Institute (article, 2023-04-13)
Field: Chemistry — Organic Chemistry

Use of the UNIFAC model in the calculation of physicochemical properties of ecotoxicants for technological and ecoanalytical purposes Modern development vector of environmental monitoring leads to elaboration of analytical methods for qualitative and quantitative analysis of different ecotoxicants. Many studies face the lack of information on isomers and homologues of already studied compounds. This problem cannot always be solved experimentally due to the difficulty of separating or synthesizing certain compounds; the use of group theories of solutions will help partly; using them, solubility in water or partition coefficient between two immiscible solvents is calculated for ecotoxicants. These parameters are important for solving the analytical and ecological problems. The partition coefficient in the octanol – water system is associated with a possibility of accumulation of different compounds in living organisms; the partition coefficient in the hexane – acetonitrile system can be used in gas chromatographic analysis. Solubility in water is closely associated with accumulation of ecotoxicants in water bodies, as well as with their ability to be transferred. This paper presents the capabilities of the UNIFAC model for solving physicochemical problems using the example of calculating the properties of real ecotoxicants on the basis of the available thermodynamic data. All the obtained calculated values were compared with those determined experimentally. In the case of pyrene derivatives, solubility data were obtained for the first time using a correlation group model to calculate the heat of fusion and melting temperature. UNIFAC ; Partition coefficient ; Solubility ; Activity coefficient ; Group contribution method ; Chemistry ; Pyrene ; Thermodynamics ; Hildebrand solubility parameter ; Partition (number theory) ; Aqueous solution ; Chromatography ; Organic chemistry ; Phase equilibrium ; Mathematics Chemical Thermodynamics and Molecular Structure ; Analytical Chemistry and Chromatography ; Adsorption, diffusion, and thermodynamic properties of materials
Authors: Reza Mostafazadeh | Hassan Karimi‐Maleh | Ali Ghaffarinejad | Fariba Tajabadi | Yasamin Hamidian
Source: Applied Nanoscience (article, 2023-01-24)
Field: Chemistry — Electrochemistry

Highly sensitive electrochemical sensor based on carbon paste electrode modified with graphene nanoribbon–CoFe2O4@NiO and ionic liquid for azithromycin antibiotic monitoring in biological and pharmaceutical samples Ionic liquid ; Hexafluorophosphate ; Materials science ; Graphene ; Electrochemistry ; Electrochemical gas sensor ; Non-blocking I/O ; Chronoamperometry ; Nanocomposite ; Electrode ; Cyclic voltammetry ; Chemical engineering ; Analytical Chemistry (journal) ; Nanotechnology ; Chromatography ; Chemistry ; Organic chemistry ; Physical chemistry ; Catalysis Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Conducting polymers and applications
Authors: Linbo Tian | Jinbao Xia | A.A. Kolomenskii | H. A. Schuessler | Feng Zhu | Yanfeng Li | Jingliang He | Qian Dong | Sasa Zhang
Source: Communications Engineering (article, 2023-08-01)
Field: Chemistry — Spectroscopy

Gas phase multicomponent detection and analysis combining broadband dual-frequency comb absorption spectroscopy and deep learning Abstract In absorption spectroscopy, analysis of multicomponent gas mixtures becomes challenging when absorption features overlap (blended spectra). Here we propose a gas sensor which can accurately identify the species and retrieve the concentrations of components in a gaseous mixture in a broad spectrum. The sensor integrates a mid-infrared dual-frequency comb laser source for spectrum acquisition and a deep learning algorithm for spectral analysis. The sensor was tested on gas phase mixtures of methane, acetone and water vapor. A prototype sensor was assessed in realistic scenarios in real time. We also systematically analyzed and presented explicit visualizations to explain the underlying working mechanism of the algorithms. Broadband ; Absorption (acoustics) ; Spectroscopy ; Absorption spectroscopy ; Methane ; Water vapor ; Gas phase ; Infrared spectroscopy ; Phase (matter) ; Materials science ; Dual (grammatical number) ; Spectral line ; Infrared ; Analytical Chemistry (journal) ; Computer science ; Optics ; Chemistry ; Physics ; Chromatography Spectroscopy and Laser Applications ; Analytical Chemistry and Sensors ; Advanced Fiber Laser Technologies
Authors: Muhammad Haroon | Tashfeen Akhtar | Qurat-ul-ain Shaikh | Hasnain Mehmood | Muhammad Khalid | Muhammad Adnan Asghar | Saad M. Alshehri | Suvash Chandra Ojha
Source: ACS Omega (article, 2023-07-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Facile Synthesis and DFT Analysis of Novel Thiazole-Based Hydrazones: An Experimental and Theoretical Perspective Hydrazone compounds with remarkable nonlinear optical (NLO) properties were found with vast applications due to their cost-effective synthesis and greater stability. Therefore, we synthesized hydrazone scaffolds (<b>TCAH1-TCAH8</b>) by condensation reaction, and their structural confirmation was accomplished with spectroscopic methods (<sup>1</sup>H-, <sup>13</sup>C-NMR, and HRMS). Quantum chemical calculations were also performed at B3PW91/6-311G(d,p) functional of DFT to explore electronic, structural, and chemical properties. To understand the NLO responses of afore-said chromophores, various kinds of analyses such as natural bonding orbitals (NBOs), frontier molecular orbitals (FMOs), UV-vis analysis, and density of states (DOS) were performed. Findings showed that the HOMO-LUMO energy gap in <b>TCAH8</b> (3.595 eV) was found to be lower than the <b>TCAH1-TCAH7</b> (4.123-3.932 eV) with a large red shift which leads to a substantial NLO response. Furthermore, strong intramolecular interactions showed the highest stabilization energy (24.1 kcal mol<sup>-1</sup>) for <b>TCAH8</b> in the NBO transitions, combined with the least binding energy. The significant NLO response of <b>TCAH4</b> was explored with ⟨α⟩, β<sub>tot</sub>, and ⟨γ⟩ values as 5.157 × 10<sup>-23</sup>, and 2.185 × 10<sup>-29</sup>, and 2.753 × 10<sup>-34</sup> esu, respectively, among the entitled compounds. The recent findings may inspire scientists to develop extremely effective NLO materials for forthcoming hi-tech applications. Thiazole ; Perspective (graphical) ; Combinatorial chemistry ; Computer science ; Chemistry ; Stereochemistry ; Artificial intelligence Nonlinear Optical Materials Research ; Organic Chemistry Cycloaddition Reactions ; Chemical synthesis and pharmacological studies
Authors: Changhwan Lee | P. James Schuck
Source: Annual Review of Physical Chemistry (review, 2023-04-24)
Field: Materials Science — Materials Chemistry

Photodarkening, Photobrightening, and the Role of Color Centers in Emerging Applications of Lanthanide-Based Upconverting Nanomaterials Upconverting nanoparticles (UCNPs) compose a class of luminescent materials that utilize the unique wavelength-converting properties of lanthanide (Ln) ions for light-harvesting applications, photonics technologies, and biological imaging and sensing experiments. Recent advances in UCNP design have shed light on the properties of local color centers, both intrinsic and controllably induced, within these materials and their potential influence on UCNP photophysics. In this review, we describe fundamental studies of color centers in Ln-based materials, including research into their origins and their roles in observed photodarkening and photobrightening mechanisms. We place particular focus on the new functionalities that are enabled by harnessing the properties of color centers within Ln-doped nanocrystals, illustrated through applications in afterglow-based bioimaging, X-ray detection, all-inorganic nanocrystal photoswitching, and fully rewritable optical patterning and memory. Photodarkening ; Materials science ; Nanotechnology ; Nanomaterials ; Lanthanide ; Afterglow ; Photonics ; Luminescence ; Nanocrystal ; Nanoparticle ; Optoelectronics ; Wavelength ; Ion ; Chemistry ; Physics Luminescence Properties of Advanced Materials ; Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics
Authors: Zihao Wan | Hong Yang | Jipan Xu | Hongbo Mu | Dawei Qi
Source: Journal of Forestry Research (article, 2023-12-06)
Field: Chemistry — Organic Chemistry

BACNN: Multi-scale feature fusion-based bilinear attention convolutional neural network for wood NIR classification Abstract Effective development and utilization of wood resources is critical. Wood modification research has become an integral dimension of wood science research, however, the similarities between modified wood and original wood render it challenging for accurate identification and classification using conventional image classification techniques. So, the development of efficient and accurate wood classification techniques is inevitable. This paper presents a one-dimensional, convolutional neural network (i.e., BACNN) that combines near-infrared spectroscopy and deep learning techniques to classify poplar, tung, and balsa woods, and PVA, nano-silica-sol and PVA-nano silica sol modified woods of poplar. The results show that BACNN achieves an accuracy of 99.3% on the test set, higher than the 52.9% of the BP neural network and 98.7% of Support Vector Machine compared with traditional machine learning methods and deep learning based methods; it is also higher than the 97.6% of LeNet, 98.7% of AlexNet and 99.1% of VGGNet-11. Therefore, the classification method proposed offers potential applications in wood classification, especially with homogeneous modified wood, and it also provides a basis for subsequent wood properties studies. Convolutional neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Support vector machine ; Artificial neural network ; Deep learning ; Scale (ratio) ; Homogeneous ; Dimension (graph theory) ; Feature (linguistics) ; Machine learning ; Mathematics Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Spectroscopy and Chemometric Analyses
Authors: Shuai Liu | Honggao Liu | Jieqing Li | Yuanzhong Wang
Source: Food Science & Nutrition (article, 2023-07-11)
Field: Chemistry — Analytical Chemistry

Building deep learning and traditional chemometric models based on Fourier transform mid‐infrared spectroscopy: Identification of wild and cultivated <i>Gastrodia elata</i> To identify wild and cultivated <i>Gastrodia elata</i> quickly and accurately, this study is the first to apply three-dimensional correlation spectroscopy (3DCOS) images combined with deep learning models to the identification of <i>G. elata</i>. The spectral data used for model building do not require any preprocessing, and the spectral data are converted into three-dimensional spectral images for model building. For large sample studies, the time cost is minimized. In addition, a partial least squares discriminant analysis (PLS-DA) model and a support vector machine (SVM) model are built for comparison with the deep learning model. The overall effect of the deep learning model is significantly better than that of the traditional chemometric models. The results show that the model achieves 100% accuracy in the training set, test set, and external validation set of the model built after 46 iterations without preprocessing the original spectral data. The sensitivity, specificity, and the effectiveness of the model are all 1. The results concluded that the deep learning model is more effective than the traditional chemometric model and has greater potential for application in the identification of wild and cultivated <i>G. elata</i>. Gastrodia elata ; Fourier transform infrared spectroscopy ; Identification (biology) ; Traditional medicine ; Botany ; Biology ; Engineering ; Medicine ; Chemical engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Sabyasachi Roy Chowdhury | Ngan Nguyen | Bess Vlaisavljevich
Source: The Journal of Physical Chemistry A (article, 2023-04-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Importance of Dispersion in the Molecular Geometries of Mn(III) Spin-Crossover Complexes The computational investigation of the molecular geometries of a pair of manganese(III) spin-crossover complexes is reported. For the geometry of the quintet high-spin state, density functionals significantly overestimate Mn-N<sub>amine</sub> bond distances, although the geometry for the triplet intermediate-spin state is well described. Comparisons with several wave function-based methods demonstrate that this error is due to the limited ability of commonly used density functionals to recover dispersion beyond a certain extent. Among the methods employed for geometry optimization, restricted open-shell Møller-Plesset perturbation theory (MP2) appropriately describes the high-spin geometry but results in a slightly shorter Mn-O distance in both spin states. On the other hand, extended multistate complete active space second-order perturbation theory (XMS-CASPT2) provides a good description of the geometry for the intermediate-spin state but also sufficiently recovers dispersion, performing well for the high-spin state. Despite the fact that the electronic structure of both spin states is dominated by one-electron configuration, XMS-CASPT2 offers a balanced approach, leading to molecular geometries with much better agreement with experiment than MP2 and DFT. A scan along the Mn-N<sub>amine</sub> bond demonstrates that for these complexes coupled cluster methods (i.e., DLPNO-CCSD(T)) also yield bond distances in agreement with experiment while multiconfiguration pair density functional theory (MC-PDFT) is unable to recover dispersion well enough, analogous to single-reference DFT. Density functional theory ; Perturbation theory (quantum mechanics) ; Bond length ; Coupled cluster ; Spin states ; Wave function ; Molecular geometry ; Spin (aerodynamics) ; Dispersion (optics) ; Molecular physics ; Geometry ; Chemistry ; Physics ; Molecule ; Atomic physics ; Computational chemistry ; Quantum mechanics ; Mathematics ; Thermodynamics Magnetism in coordination complexes ; Metal-Catalyzed Oxygenation Mechanisms ; Metal complexes synthesis and properties
Authors: Sungsoo Kim | HyeonSeok Shin | Kyung-Geun Ahn | Youngmin Park | Min‐Chul Kwon | Jae‐Min Lim | Eun-Kyung Oh | Yumi Kim | Seung-Man Han | Dong‐Young Noh
Source: Scientific Reports (article, 2023-06-02)
Field: Chemistry — Spectroscopy

Quantifiable peptide library bridges the gap for proteomics based biomarker discovery and validation on breast cancer Mass spectrometry (MS) based proteomics is widely used for biomarker discovery. However, often, most biomarker candidates from discovery are discarded during the validation processes. Such discrepancies between biomarker discovery and validation are caused by several factors, mainly due to the differences in analytical methodology and experimental conditions. Here, we generated a peptide library which allows discovery of biomarkers in the equal settings as the validation process, thereby making the transition from discovery to validation more robust and efficient. The peptide library initiated with a list of 3393 proteins detectable in the blood from public databases. For each protein, surrogate peptides favorable for detection in mass spectrometry was selected and synthesized. A total of 4683 synthesized peptides were spiked into neat serum and plasma samples to check their quantifiability in a 10 min liquid chromatography-MS/MS run time. This led to the PepQuant library, which is composed of 852 quantifiable peptides that cover 452 human blood proteins. Using the PepQuant library, we discovered 30 candidate biomarkers for breast cancer. Among the 30 candidates, nine biomarkers, FN1, VWF, PRG4, MMP9, CLU, PRDX6, PPBP, APOC1, and CHL1 were validated. By combining the quantification values of these markers, we generated a machine learning model predicting breast cancer, showing an average area under the curve of 0.9105 for the receiver operating characteristic curve. Biomarker discovery ; Biomarker ; Proteomics ; Receiver operating characteristic ; Breast cancer ; Computational biology ; False discovery rate ; Mass spectrometry ; Cancer biomarkers ; Cancer ; Bioinformatics ; Medicine ; Chromatography ; Chemistry ; Biology ; Internal medicine ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Naim Abdul-Khalek | Reinhard Wimmer | Michael T. Overgaard | Simon Gregersen Echers
Source: Computational and Structural Biotechnology Journal (article, 2023-01-01)
Field: Chemistry — Spectroscopy

Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS: A deep learning approach Accurate and absolute quantification of peptides in complex mixtures using quantitative mass spectrometry (MS)-based methods requires foreground knowledge and isotopically labeled standards, thereby increasing analytical expenses, time consumption, and labor, thus limiting the number of peptides that can be accurately quantified. This originates from differential ionization efficiency between peptides and thus, understanding the physicochemical properties that influence the ionization and response in MS analysis is essential for developing less restrictive label-free quantitative methods. Here, we used equimolar peptide pool repository data to develop a deep learning model capable of identifying amino acids influencing the MS1 response. By using an encoder-decoder with an attention mechanism and correlating attention weights with amino acid physicochemical properties, we obtain insight on properties governing the peptide-level MS1 response within the datasets. While the problem cannot be described by one single set of amino acids and properties, distinct patterns were reproducibly obtained. Properties are grouped in three main categories related to peptide hydrophobicity, charge, and structural propensities. Moreover, our model can predict MS1 intensity output under defined conditions based solely on peptide sequence input. Using a refined training dataset, the model predicted log-transformed peptide MS1 intensities with an average error of 9.7 ± 0.5% based on 5-fold cross validation, and outperformed random forest and ridge regression models on both log-transformed and real scale data. This work demonstrates how deep learning can facilitate identification of physicochemical properties influencing peptide MS1 responses, but also illustrates how sequence-based response prediction and label-free peptide-level quantification may impact future workflows within quantitative proteomics. Peptide ; Chemistry ; Biological system ; Mass spectrometry ; Amino acid ; Limiting ; Computer science ; Chromatography ; Biochemistry ; Biology Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Lei Tao | Elena V. Khramenkova | Insu Lee | Takaaki Ikuno | Rachit Khare | Andreas Jentys | John L. Fulton | Alexander A. Kolganov | Evgeny A. Pidko | Maricruz Sanchez‐Sanchez | Johannes A. Lercher
Source: Journal of the American Chemical Society (article, 2023-08-07)
Field: Materials Science — Materials Chemistry

Speciation and Reactivity Control of Cu-Oxo Clusters via Extraframework Al in Mordenite for Methane Oxidation The stoichiometric conversion of methane to methanol by Cu-exchanged zeolites can be brought to highest yields by the presence of extraframework Al and high CH<sub>4</sub> chemical potentials. Combining theory and experiments, the differences in chemical reactivity of monometallic Cu-oxo and bimetallic Cu-Al-oxo nanoclusters stabilized in zeolite mordenite (MOR) are investigated. Cu-L<sub>3</sub> edge X-ray absorption near-edge structure (XANES), infrared (IR), and ultraviolet-visible (UV-vis) spectroscopies, in combination with CH<sub>4</sub> oxidation activity tests, support the presence of two types of active clusters in MOR and allow quantification of the relative proportions of each type in dependence of the Cu concentration. <i>Ab initio</i> molecular dynamics (MD) calculations and thermodynamic analyses indicate that the superior performance of materials enriched in Cu-Al-oxo clusters is related to the activity of two μ-oxo bridges in the cluster. Replacing H<sub>2</sub>O with ethanol in the product extraction step led to the formation of ethyl methyl ether, expanding this way the applicability of these materials for the activation and functionalization of CH<sub>4</sub>. We show that competition between different ion-exchanged metal-oxo structures during the synthesis of Cu-exchanged zeolites determines the formation of active species, and this provides guidelines for the synthesis of highly active materials for CH<sub>4</sub> activation and functionalization. Chemistry ; Reactivity (psychology) ; XANES ; Bimetallic strip ; Nanoclusters ; Mordenite ; Inorganic chemistry ; Physical chemistry ; Zeolite ; Metal ; Catalysis ; Organic chemistry ; Spectroscopy Catalytic Processes in Materials Science ; Nanocluster Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Congling Shi | Mei Wan | Xiaodong Qian | Jingyun Jing | Keqing Zhou
Source: Molecules (article, 2023-09-27)
Field: Materials Science — Polymers and Plastics

Zinc Hydroxystannate/Carbon Nanotube Hybrids as Flame Retardant and Smoke Suppressant for Epoxy Resins Novel hybrid flame retardants containing zinc hydroxystannate and carbon nanotubes (ZHS-CNTs) were synthesized using the coprecipitation method, and the structure and morphology of ZHS-CNTs were investigate using an X-ray powder diffractometer (XRD), scanning electron microscope (SEM), transmission electron microscopy (TEM) and thermogravimetric analyzer (TGA). Then, the ZHS, CNTs and ZHS-CNTs were incorporated into EP, respectively, and the flame-retardant and smoke inhibition performance of the composites were compared and studied. Among the three composites, the EP/ZHS-CNT composites have the highest improvements on the fire resistance and smoke inhibition properties. With only 2.0 wt.% ZHS-CNT hybrids, the pHRR of EP/ZHS-CNT composite materials is reduced by 34.2% compared with EP. Moreover, the release of toxic gases including CO, CO<sub>2</sub> and SPR from the composites was also effectively inhibited. The mechanisms of flame retardant and smoke inhibition were investigated and the improved properties were generally ascribed to the synergistic flame-retardant effects between ZHS and CNTs, the catalyzing effect of ZHS and the stable network structure of CNTs. Materials science ; Thermogravimetric analysis ; Fire retardant ; Carbon nanotube ; Scanning electron microscope ; Composite material ; Coprecipitation ; Zinc ; Transmission electron microscopy ; Epoxy ; Composite number ; Chemical engineering ; Nanotechnology ; Metallurgy Flame retardant materials and properties ; Fire dynamics and safety research ; Electromagnetic wave absorption materials