jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M.C. Díaz-Liñán | Verónica Sánchez de Medina | Carlos Ferreiro‐Vera | María Teresa García-Valverde
Source: AppliedChem (article, 2023-11-29)
Field: Chemistry — Analytical Chemistry

Light and Shadow in Near-Infrared Spectroscopy: A Powerful Tool for Cannabis sativa L. Analysis Cannabis sativa L. is an ancient cultivar that has found applications in various fields, e.g., medicine, due to its beneficial effects. However, due to its psychotropic effects, the regulation of this cultivar has increased throughout the decades. In this context, the need for rapid and reliable analytical methods to ensure the quality control of Cannabis cultivars has become of extreme importance. NIRS has arisen as a powerful tool in this field due to its multiple advantages, e.g., non-destructive, rapid, and cost-effective. In this article, the chemometric techniques commonly employed in NIRS method development are described, along with their application for the analysis of Cannabis samples. Regarding qualitative methods, different mathematical treatments and classification models are explained. As for quantitative methods, the representative linear and non-linear modelling techniques applied for the development of prediction equations are described, alongside their application in the Cannabis field. To the best of our knowledge, this is the first time this type of review is written, since there are several articles which address cannabinoid determination, but the main purpose of this review is to enhance the potential of NIRS over the traditional techniques employed for the analysis of Cannabis samples. Cannabis sativa ; Context (archaeology) ; Cannabis ; Biochemical engineering ; Computer science ; Cultivar ; Biology ; Engineering ; Botany ; Geography ; Psychology ; Psychiatry ; Archaeology Spectroscopy and Chemometric Analyses ; Coffee research and impacts ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Hicham Ait Laasri | Eliane Bsaibess | Fabian Delorme | Guillaume F. Nataf | Fabien Giovannelli
Source: Journal of Alloys and Compounds (article, 2023-04-17)
Field: Materials Science — Materials Chemistry

Ultra-low lattice thermal conductivity in tungsten-based scheelite ceramics Scheelite ; Materials science ; Microstructure ; Tungsten ; Crystallite ; Ceramic ; Thermal conductivity ; Atmospheric temperature range ; Mineralogy ; Tungstate ; Crystallography ; Analytical Chemistry (journal) ; Metallurgy ; Chemistry ; Composite material ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Thermal Expansion and Ionic Conductivity ; Thermal properties of materials
Authors: Christopher Fifty | Jure Leskovec | Sebastian Thrun
Source: arXiv (Cornell University) (preprint, 2023-10-13)
Field: Materials Science — Materials Chemistry

In-Context Learning for Few-Shot Molecular Property Prediction In-context learning has become an important approach for few-shot learning in Large Language Models because of its ability to rapidly adapt to new tasks without fine-tuning model parameters. However, it is restricted to applications in natural language and inapplicable to other domains. In this paper, we adapt the concepts underpinning in-context learning to develop a new algorithm for few-shot molecular property prediction. Our approach learns to predict molecular properties from a context of (molecule, property measurement) pairs and rapidly adapts to new properties without fine-tuning. On the FS-Mol and BACE molecular property prediction benchmarks, we find this method surpasses the performance of recent meta-learning algorithms at small support sizes and is competitive with the best methods at large support sizes. Property (philosophy) ; Context (archaeology) ; Computer science ; Shot (pellet) ; Artificial intelligence ; Underpinning ; Fine-tuning ; One shot ; Machine learning ; Chemistry ; Engineering ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Source: NanoWorld Journal (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Synthesized MgO/Chitosan Nanocomposite: It’s Application for the Removal of Dicofol and Optimization by Box Benhken Design Variganji Gowthami, Bandela Sowjanya, Malluvalasa Nagendra Kumar and Meena Vangalapati Pesticides are used to control a wide range of crops and pests around the world. Among the widely used pesticides dicofol is mildly poisonous to humans and very toxic to aquatic creatures. Chitosan ; Nanocomposite ; Dicofol ; Materials science ; Chemical engineering ; Chemistry ; Nanotechnology ; Engineering ; Organic chemistry Magnesium Oxide Properties and Applications
Authors: Suha Elderderi | Franck Bonnier | Xavier Perse | Hugh J. Byrne | Florent Yvergnaux | Igor Chourpa | Abdalla A. Elbashir | Emilie Munnier
Source: Pharmaceutics (article, 2023-05-23)
Field: Chemistry — Analytical Chemistry

Label-Free Quantification of Nanoencapsulated Piperonyl Esters in Cosmetic Hydrogels Using Raman Spectroscopy Raman spectroscopy is a well-established technique for the molecular characterisation of samples and does not require extensive pre-analytical processing for complex cosmetic products. As an illustration of its potential, this study investigates the quantitative performance of Raman spectroscopy coupled with partial least squares regression (PLSR) for the analysis of Alginate nanoencapsulated Piperonyl Esters (ANC-PE) incorporated into a hydrogel. A total of 96 ANC-PE samples covering a 0.4% <i>w</i>/<i>w</i>-8.3% <i>w</i>/<i>w</i> PE concentration range have been prepared and analysed. Despite the complex formulation of the sample, the spectral features of the PE can be detected and used to quantify the concentrations. Using a leave-K-out cross-validation approach, samples were divided into a training set (<i>n</i> = 64) and a test set, samples that were previously unknown to the PLSR model (<i>n</i> = 32). The root mean square error of cross-validation (RMSECV) and prediction (RMSEP) was evaluated to be 0.142% (<i>w</i>/<i>w</i> PE) and 0.148% (<i>w</i>/<i>w</i> PE), respectively. The accuracy of the prediction model was further evaluated by the percent relative error calculated from the predicted concentration compared to the true value, yielding values of 3.58% for the training set and 3.67% for the test set. The outcome of the analysis demonstrated the analytical power of Raman to obtain label-free, non-destructive quantification of the active cosmetic ingredient, presently PE, in complex formulations, holding promise for future analytical quality control (AQC) applications in the cosmetics industry with rapid and consumable-free analysis. Partial least squares regression ; Raman spectroscopy ; Analytical Chemistry (journal) ; Chemistry ; Training set ; Mean squared error ; Chromatography ; Mathematics ; Materials science ; Biological system ; Statistics ; Computer science ; Artificial intelligence ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Xue Zong | Dionisios G. Vlachos
Source: Chemical Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Reconciling experimental catalytic data stemming from structure sensitivity Experimental data have long served as a valuable resource for model validation and identification of the active site. Yet, literature kinetics data often exhibit significant differences among laboratories for the same catalyst and reaction, but the reasons have remained elusive. Here, we exploit if we can rationalize (most of) this variation through catalyst structure sensitivity. We introduce a methodology to build a structure-descriptor-based microkinetic model and investigate the relations between nanoparticle structure and reaction kinetics using the complete methane oxidation on Pt as a model reaction and literature data mining. A volcano-like rate is observed with an optimum coordination number. Unlike common expectations, smaller particles have very low reactivity because of carbon poisoning. Interestingly, most of the data variation can be successfully traced to structure sensitivity. This methodology also enables rapid prediction of kinetic performance and active site determination for designing optimal catalyst structures. It can also serve as a data quality tool to assess experimental outliers. Additional reasons for data variability are discussed. Outlier ; Sensitivity (control systems) ; Catalysis ; Computer science ; Biological system ; Identification (biology) ; Kinetics ; Chemistry ; Data mining ; Biochemical engineering ; Physics ; Artificial intelligence ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Kristina D. Popovich | S. A. Vagner | Denis T. Murashko | Galina N. Ten | Dmitry I. Ryabkin | Mikhail S. Savelyev | E. P. Kitsyuk | E.A. Gerasimenko | Polina A. Edelbekova | An N Konovalov | D. V. Telyshev | С. В. Селищев | A. Yu. Gerasimenko
Source: Membranes (article, 2023-04-01)
Field: Materials Science — Biomaterials

Stability and Thrombogenicity Analysis of Collagen/Carbon Nanotube Nanocomposite Coatings Using a Reversible Microfluidic Device Currently, the development of stable and antithrombogenic coatings for cardiovascular implants is socially important. This is especially important for coatings exposed to high shear stress from flowing blood, such as those on ventricular assist devices. A method of layer-by-layer formation of nanocomposite coatings based on multi-walled carbon nanotubes (MWCNT) in a collagen matrix is proposed. A reversible microfluidic device with a wide range of flow shear stresses has been developed for hemodynamic experiments. The dependence of the resistance on the presence of a cross-linking agent for collagen chains in the composition of the coating was demonstrated. Optical profilometry determined that collagen/c-MWCNT and collagen/c-MWCNT/glutaraldehyde coatings obtained sufficiently high resistance to high shear stress flow. However, the collagen/c-MWCNT/glutaraldehyde coating was almost twice as resistant to a phosphate-buffered solution flow. A reversible microfluidic device made it possible to assess the level of thrombogenicity of the coatings by the level of blood albumin protein adhesion to the coatings. Raman spectroscopy demonstrated that the adhesion of albumin to collagen/c-MWCNT and collagen/c-MWCNT/glutaraldehyde coatings is 1.7 and 1.4 times lower than the adhesion of protein to a titanium surface, widely used for ventricular assist devices. Scanning electron microscopy and energy dispersive spectroscopy determined that blood protein was least detected on the collagen/c-MWCNT coating, which contained no cross-linking agent, including in comparison with the titanium surface. Thus, a reversible microfluidic device is suitable for preliminary testing of the resistance and thrombogenicity of various coatings and membranes, and nanocomposite coatings based on collagen and c-MWCNT are suitable candidates for the development of cardiovascular devices. Materials science ; Glutaraldehyde ; Thrombogenicity ; Coating ; Carbon nanotube ; Nanocomposite ; Adhesion ; Microfluidics ; Composite material ; Chemical engineering ; Shear stress ; Nanotechnology ; Chemistry ; Chromatography Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Orthopaedic implants and arthroplasty
Authors: Austin Browning | Keilian MacCulloch | David Guarin | Carlos Dedesma | Boyd M. Goodson | Matthew S. Rosen | Eduard Y. Chekmenev | Yi‐Fen Yen | Patrick TomHon | Thomas Theis
Source: ChemRxiv (preprint, 2023-03-20)
Field: Chemistry — Spectroscopy

Facile hyperpolarization chemistry for molecular imaging and metabolic tracking of [1-13C]pyruvate in vivo Hyperpolarization chemistry based on reversible exchange of parahydrogen, also known as Signal Amplification By Reversible Exchange (SABRE), is a particularly simple approach to attain high levels of nuclear spin hyperpolarization, which can enhance NMR and MRI signals by many orders of magnitude. SABRE has received significant attention in the scientific community since its inception because of its relative experimental simplicity and its broad applicability to a wide range of molecules, however in vivo detection of molecular probes hyperpolarized by SABRE has remained elusive. Here we describe the first demonstration of SABRE-hyperpolarized contrast detected in vivo, specifically using hyperpolarized [1-13C]pyruvate. A biocompatible formulation of hyperpolarized [1-13C]pyruvate was injected into healthy Sprague Dawley and Wistar rats, and metabolic conversion of pyruvate to lactate, alanine, pyruvate-hydrate, and bicarbonate was detected. Measurements were performed on the liver and kidney at 4.7 T via time-resolved spectroscopy and chemical-shift-resolved MRI. In addition, whole-body metabolic measurements were obtained using a cryogen-free 1.5 T MRI system, illustrating the utility of combining lower-cost MRI systems with simple, low-cost hyperpolarization chemistry to develop scalable, next-generation molecular imaging. Hyperpolarization (physics) ; Chemistry ; Spin isomers of hydrogen ; In vivo ; Nuclear magnetic resonance spectroscopy ; Nuclear magnetic resonance ; Hydrogen ; Physics ; Stereochemistry Advanced NMR Techniques and Applications ; Electron Spin Resonance Studies ; Magnetism in coordination complexes
Authors: Yue Sun | Sándor Brockhauser | Péter Hegedűs | Christian Plückthun | Luca Gelisio | D. E. Ferreira de Lima
Source: Scientific Reports (article, 2023-06-09)
Field: Materials Science — Materials Chemistry

Application of self-supervised approaches to the classification of X-ray diffraction spectra during phase transitions Spectroscopy and X-ray diffraction techniques encode ample information on investigated samples. The ability of rapidly and accurately extracting these enhances the means to steer the experiment, as well as the understanding of the underlying processes governing the experiment. It improves the efficiency of the experiment, and maximizes the scientific outcome. To address this, we introduce and validate three frameworks based on self-supervised learning which are capable of classifying 1D spectral curves using data transformations preserving the scientific content and only a small amount of data labeled by domain experts. In particular, in this work we focus on the identification of phase transitions in samples investigated by x-ray powder diffraction. We demonstrate that the three frameworks, based either on relational reasoning, contrastive learning, or a combination of the two, are capable of accurately identifying phase transitions. Furthermore, we discuss in detail the selection of data augmentation techniques, crucial to ensure that scientifically meaningful information is retained. Diffraction ; Phase transition ; Spectral line ; Computer science ; X-ray ; X-ray crystallography ; Crystallography ; Artificial intelligence ; Computational biology ; Physics ; Chemistry ; Biology ; Condensed matter physics ; Optics ; Quantum mechanics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Rama N. Behera | Vinod S. Bisht | Kuldeep Giri | Kiran Ambatipudi
Source: PROTEOMICS - CLINICAL APPLICATIONS (review, 2023-05-31)
Field: Chemistry — Spectroscopy

Realm of proteomics in breast cancer management and drug repurposing to alleviate intricacies of treatment Breast cancer, a multi-networking heterogeneous disease, has emerged as a serious impediment to progress in clinical oncology. Although technological advancements and emerging cancer research studies have mitigated breast cancer lethality, a precision cancer-oriented solution has not been achieved. Thus, this review will persuade the acquiescence of proteomics-based diagnostic and therapeutic options in breast cancer management. Recently, the evidence of breast cancer health surveillance through imaging proteomics, single-cell proteomics, interactomics, and post-translational modification (PTM) tracking, to construct proteome maps and proteotyping for stage-specific and sample-specific cancer subtyping have outperformed conventional ways of dealing with breast cancer by increasing diagnostic efficiency, prognostic value, and predictive response. Additionally, the paradigm shift in applied proteomics for designing a chemotherapy regimen to identify novel drug targets with minor adverse effects has been elaborated. Finally, the potential of proteomics in alleviating the occurrence of chemoresistance and enhancing reprofiled drugs' effectiveness to combat therapeutic obstacles has been discussed. Owing to the enormous potential of proteomics techniques, the clinical recognition of proteomics in breast cancer management can be achievable and therapeutic intricacies can be surmountable. Proteomics ; Breast cancer ; Medicine ; Cancer ; Internal medicine ; Biology Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Cancer Genomics and Diagnostics
Authors: Erica Scarel | Marco Corti | Maurizio Polentarutti | Giovanni Pierri | Consiglia Tedesco | Silvia Marchesan
Source: Journal of Peptide Science (article, 2023-12-18)
Field: Materials Science — Biomaterials

Self‐assembly of heterochiral, aliphatic dipeptides with Leu This work describes the self-assembly behavior of heterochiral, aliphatic dipeptides, l-Leu-d-Xaa (Xaa = Ala, Val, Ile, Leu), in green solvents such as acetonitrile (MeCN) and buffered water at neutral pH. Interestingly, water plays a structuring role because at 1% v/v, it enables dipeptide self-assembly in MeCN to yield organogels, which then undergo transition towards crystals. Other organic solvents and oils were tested for gelation, and metastable gels were formed in tetrahydrofuran, although at high peptide concentration (80 mM). Single-crystal X-ray diffraction revealed the dipeptides' supramolecular packing modes in amphipathic layers, as opposed to water channels reported for the homochiral Leu-Leu, or hydrophobic columns reported for homochiral Leu-Val and Leu-Ile. Chemistry ; Dipeptide ; Acetonitrile ; Solvophobic ; Supramolecular chemistry ; Self-assembly ; Yield (engineering) ; Tetrahydrofuran ; Amphiphile ; Crystallography ; Peptide ; Organic chemistry ; Crystal structure ; Stereochemistry ; Solvent ; Materials science ; Polymer Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Photochromic and Fluorescence Chemistry
Authors: Maria H. Rasmussen | Julius Seumer | Jan H. Jensen
Source: Angewandte Chemie International Edition (article, 2023-10-13)
Field: Chemistry — Inorganic Chemistry

Toward De Novo Catalyst Discovery: Fast Identification of New Catalyst Candidates for Alcohol‐Mediated Morita–Baylis–Hillman Reactions** Recently we have demonstrated how a genetic algorithm (GA) starting from random tertiary amines can be used to discover a new and efficient catalyst for the alcohol-mediated Morita-Baylis-Hillman (MBH) reaction. In particular, the discovered catalyst was shown experimentally to be eight times more active than DABCO, commonly used to catalyze the MBH reaction. This represents a breakthrough in using generative models for catalyst optimization. However, the GA procedure, and hence discovery, relied on two important pieces of information; 1) the knowledge that tertiary amines catalyze the reaction and 2) the mechanism and reaction profile for the catalyzed reaction, in particular the transition state structure of the rate-determining step. Thus, truly de novo catalyst discovery must include these steps. Here we present such a method for discovering catalyst candidates for a specific reaction while simultaneously proposing a mechanism for the catalyzed reaction. We show that tertiary amines and phosphines are potential catalysts for the MBH reaction by screening 11 molecular templates representing common functional groups. The method relies on an automated reaction discovery workflow using meta-dynamics calculations. Combining this method for catalyst candidate discovery with our GA-based catalyst optimization method results in an algorithm for truly de novo catalyst discovery. Catalysis ; Alcohol ; Identification (biology) ; Chemistry ; Combinatorial chemistry ; Organic chemistry ; Biology Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science ; Chemical Synthesis and Analysis
Authors: Elizabeth M. Diessner | Gemma R. Takahashi | Rachel W. Martin | Carter T. Butts
Source: Biomolecules (article, 2023-02-09)
Field: Materials Science — Materials Chemistry

Comparative Modeling and Analysis of Extremophilic D-Ala-D-Ala Carboxypeptidases Understanding the molecular adaptations of organisms to extreme environments requires a comparative analysis of protein structure, function, and dynamics across species found in different environmental conditions. Computational studies can be particularly useful in this pursuit, allowing exploratory studies of large numbers of proteins under different thermal and chemical conditions that would be infeasible to carry out experimentally. Here, we perform such a study of the MEROPS family S11, S12, and S13 proteases from psychophilic, mesophilic, and thermophilic bacteria. Using a combination of protein structure prediction, atomistic molecular dynamics, and trajectory analysis, we examine both conserved features and trends across thermal groups. Our findings suggest a number of hypotheses for experimental investigation. Proteases ; Computational biology ; Molecular dynamics ; Function (biology) ; Carboxypeptidase ; Biology ; Thermophile ; Biological system ; Evolutionary biology ; Chemistry ; Genetics ; Bacteria ; Biochemistry ; Computational chemistry ; Enzyme Enzyme Structure and Function ; Protein Structure and Dynamics ; Enzyme Production and Characterization
Authors: Ján Titiš | Cyril Rajnák | Roman Boča
Source: Inorganics (article, 2023-11-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetostructural D-Correlations and Their Impact on Single-Molecule Magnetism Functional dependence of the axial zero-field splitting parameter D with respect to a properly chosen geometrical parameter (Dstr) in metal complexes is termed the magnetostructural D-correlation. In mononuclear hexacoordinate Ni(II) complexes with the ground electronic term 3B1g (3A2g in the regular octahedron), it proceeds along two intercepting straight lines, allowing for predicting the sign and magnitude of the D-parameter by knowing the X-ray structure alone; Dstr is constructed from the metal–ligand bond lengths. In hexacoordinate Co(II) complexes, it is applicable only in the segment of the compressed bipyramid where the ground electronic term 4B1g is orbitally non-degenerate so that the spin Hamiltonian formalism holds true. The D vs. Dstr correlation is strongly non-linear, and it is represented by a set of decreasing exponentials. In tetracoordinate Co(II) complexes, on the contrary, the angular distortion from the regular tetrahedron is crucial so that the appropriate structural parameter Dstr is constructed of bond angles. The most complex case is represented by pentacoordinated Co(II) systems, for which it is not yet possible to define a statistically significant correlation. All of these empirical correlations originate in the electronic structure of metal complexes that can be modelled using generalized crystal-field theory. As the barrier to spin reversal in single-molecule magnets is proportional to the D-value, for rational tuning and/or prediction of the single-molecule magnetic behaviour, knowledge/prediction of the D-parameter is beneficial. In this review, we present the statistical processing of an extensive set of structural and magnetic data on Co(II) and Ni(II) complexes, which were published over the past 15 years. Magnetostructural D-correlations defined for this data set are reviewed in detail. Hexacoordinate ; Degenerate energy levels ; Tetrahedron ; Mathematics ; Physics ; Chemistry ; Crystallography ; Quantum mechanics Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Electron Spin Resonance Studies