Chemical Information Research Articles

Feasible estimation of weathering stability for free film acrylic coatings via dynamic mechanical analysis as the sole characterization method

AbstractWeathering stability is a highly relevant materials property, especially for exterior coatings. Assessment of artificially or naturally weathered coatings is however still mostly subjective without distinct correlation between the formulation of a coating and its weathering stability. An aggravating issue in the industrial context is the often very sparse information on the exact chemical composition of materials by the manufacturers. In this study, a quantitative method to examine weathering-induced changes in coatings with limited chemical information is described, which is based solely on dynamic mechanical analysis (DMA). This approach is applied to free films to eliminate possible influences of the substrate during the measurement. The effects of artificial weathering on three exemplary acrylic waterborne wood coatings are then examined and compared to the results of state of the art artificial weathering. Precisely, the impact of additional light stabilizer on mechanical properties, molecular weight, and Tg is monitored. For the exemplary systems, it could be shown that the molecular weight of the polymers decreases during artificial weathering, where the most prominent decrease was observed in the first 300 h. In the same time frame, E′ and E″ also decrease, while the glass transition temperature increased dependent of the formulation by 6–15°C. Remarkably, for coatings without light stabilizer an apparent two-staged degradation mechanism is observed, which leads to an increase of E′ and E″ after 600 h of artificial weathering, accompanied with an increased brittleness of the free film. The primary goal of this approach is to offer a tool for quick and systematic investigation of new coating formulations in the industrial context.

Chemical Information Network and Molecular Docking Inspire the Novel Indole Discovery Against Glioma from Tabernaemontana corymbosa.

The alkaloidingredients were considered to be responsible forthe diverse pharmacological activitiesof the medicinalplant Tabernaemontana corymbosa(Roxb. ex Wall.). In the current finding, the systematic phytochemical investigation onT.corymbosahave been achieved. One new indole alkaloidtabercorympyline A(1) along with sevenknown indoles (2-8) wereisolated from T.corymobsa. Their structures were elucidated by means of spectroscopic techniquesand quantum chemical calculations. Tabercorympyline A(1) possessedthe indoleskeleton with rare N-containing nine membered ring.Chemical informationnetwork was used to comprehensively discover the cluesfor the glioma therapeutic leadsfromT.corymbosaalkaloids (TA). Inspired by chemical information networkanalysis, all the isolated compoundshave been furthervalidated theiranti glioma activities in glioma cell line U251.Interestingly, the compounds2, 3,5, and6exhibited significantinhibitory effects on gliomacellsin vitro.Molecular docking was ultimately used to indicatepossible binding performance and mechanism between active compounds(2-3) andthe core targets.This study sequentially assembled chemical informationand networkanalysis, phytochemistry, molecular docking, and in vitroactivity validation to comprehensively explore the effective compounds, related targets, and potential mechanisms of TA therapy for glioma.

Understanding the Role of Surface Chemistry in Nanocellulose Kink Formation: A Case Study of TEMPO-Mediated Oxidation.

This study found that the sources of cellulose have a significant effect on the parameters related to the kinks present in nanocellulose. During nanocellulose preparation, 2,2,6,6-tetramethylpiperidinyl-1-oxyl (TEMPO)-mediated oxidation induced partial depolymerization on whole cellulose and made the amorphous regions more susceptible to consequent mechanical treatment irrespective of cellulose sources. However, plant cellulose microfibrils were prone to break into shorter nanocellulose with fewer kinks, while bacterial and tunicate cellulose were more likely to bend rather than break, thus leading to the generation of more kinks. The kinks did not show significant effects on the size, crystallinity index, and thermal properties of nanocellulose for each cellulose source, though the kink numbers were positively related to the mechanical performance of nanocellulose. Collectively, this study elucidated the kink formation mechanisms and clarified the effects of kinks on nanocellulose performance, thus providing new insights into understanding the source and behaviors of microdefects present in nanocellulose.

Marine Cyanobacteria: A Rich Source of Structurally Unique Anti-Infectives for Drug Development

Marine cyanobacteria represent a promising yet underexplored source of novel natural products with potent biological activities. Historically, the focus has been on isolating cytotoxic compounds from marine cyanobacteria, but a substantial number of these photosynthetic microorganisms also produce diverse specialized molecules with significant anti-infective properties. Given the global pressing need for new anti-infective lead compounds, this review provides a concise yet comprehensive overview of the current knowledge on anti-infective secondary metabolites derived from marine cyanobacteria. A majority of these molecules were isolated from free-living filamentous cyanobacteria, while several examples were derived from marine cyanobacterial symbionts. In addition, SAR studies and potent synthetic analogs based on selected molecules will be featured. With more than 200 molecules, this review presents their antibacterial, antifungal, antiviral, antiprotozoal, and molluscicidal activities, with the chemical and biological information covered in the literature up to September 2024.

Cell Painting and Chemical Structure Read-Across Can Complement Each Other for Rat Acute Oral Toxicity Prediction in Chemical Early Derisking.

Early derisking decisions in the development of new chemical compounds enable the identification of novel chemical candidates with improved safety profiles. In vivo studies are traditionally conducted in the early assessment of acute oral toxicity of crop protection products to avoid compounds, which are considered "very acutely toxic", with an in vivo lethal dose of 50% (LD50) ≤ 60 mg/kg body weight. Those studies are lengthy and costly and raise ethical concerns, catalyzing the use of nonanimal alternatives. The objective of our analysis was to assess the predictive efficacy of read-across approaches for acute oral toxicity in rats, comparing the use of chemical structure information, in vitro biological data derived from the Cell Painting profiling assay on U2OS cells, or the combination of both. Our findings indicate that the classification of compounds as very acute oral toxic (LD50 ≤ 60 mg/kg) or not is possible using a read-across approach, with chemical structure information, morphological profiles, or a combination of both. When classifying compounds structurally similar to those in the training set, the chemical structure was more predictive (balanced accuracy of 0.82). Conversely, when the compounds to be classified were structurally different from those in the training set, the morphological profiles were more predictive (balanced accuracy of 0.72). Combining the two models allowed for the classification of compounds structurally similar to those in the training set to slightly improve the predictions (balanced accuracy of 0.85).

Decreasing the Uncertainty in the Comparison of Molecular Fingerprints of Organic Aerosols with H/D Exchange Mass Spectrometry.

High-resolution mass spectrometry (HRMS) has become an indispensable tool in the characterization of organic aerosols (OA) providing information on air quality, health assessment, climate trends, reactions, and source apportionment. Spectra-derived lists of formulas and their relative abundances are used to compare ambient OA from different sources or to monitor secondary OA formation under controlled laboratory conditions in smog chamber experiments. Various techniques are implemented to visualize common and unique features, series of precursors, and products. The disadvantage of this conventional approach is in associating elemental compositions to specific compounds, while due to several analytical limitations, the structural information remains hidden. We argue that some of the conclusions derived from this data analysis can be misleading. In this study, we applied in-ESI source H/D exchange (HDX), which facilitated enumeration of labile protons in molecules behind elemental compositions of OA. We applied this technique to compare OA from three different locations representing urban, forest, and marine environments and to examine tentative chemical information derived from Kendrick mass defect (KMD) series analysis. Significant discrepancies were found between numbers of labile protons in protogenic functional groups and the stoichiometry of chemical reactions, which are associated with KMD series. Only a portion of chemical pairs matched target stoichiometries, which highlights the existing limitations in environmental applications of conventional formula-based HRMS data interpretation strategies.

Raman Spectroscopy Detects Bone Mineral Changes with Aging in Archaeological Human Lumbar Vertebrae from Thornton Abbey.

Archaeological human remains provide key insight into lifestyles, health, and diseases affecting past societies. However, only limited analyses can be conducted without causing damage due to the destructive nature of current technologies. The same problem exists with current clinical analyses of the skeleton, and the preferred advanced imaging techniques only provide macroscopic information. Raman spectroscopy could provide chemical information without detriment to archaeological bone samples and perhaps the need for invasive diagnostic procedures in the future. This study measured archaeological human vertebrae to investigate if chemical differences with aging were detectable with Raman spectroscopy and if differences in mineral chemistry could contribute to information on bone mineral diseases. The three lowest bones of the spine (lumbar vertebrae L3-L5), which are subject to the heaviest loading in life, of nine adults from three age groups (18-25, 25-45, and 45+ years) were provided by the Thornton Abbey Project. Three biomechanically important anatomical locations were selected for analysis; likely sites chosen to measure any chemical changes associated with aging, the vertebral body center and the zygapophyseal joints. Results detected chemical changes associated with aging. These changes relate to the minerals phosphate (∼960 cm-1) and carbonate (∼1070 cm-1), which are fundamental to bone function. Overall mineralization was found to increase with aging, but while carbonate increased with age, phosphate increased up to ∼45 years and then declined. These fluctuations were found in all three vertebrae, but were more distinct in L5, particularly in the vertebral body, indicating this is an optimal area for detecting bone mineral chemistry changes with aging. This is the first Raman analysis of bone samples from the historically significant site of Thornton Abbey. Results detected age-related changes, illustrating that ancient remains can be used to enhance understanding of modern diseases and provide information on the health and lifestyle of historic individuals.

Machine Learning Approaches for the Fusion of Near-Infrared, Mid-Infrared, and Raman Data to Identify Cartilage Degradation in Human Osteochondral Plugs.

Vibrational spectroscopy methods such as mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopies have been shown to have great potential for in vivo biomedical applications, such as arthroscopic evaluation of joint injuries and degeneration. Considering that these techniques provide complementary chemical information, in this study, we hypothesized that combining the MIR, NIR, and Raman data from human osteochondral samples can improve the detection of cartilage degradation. This study evaluated 272 osteochondral samples from 18 human knee joins, comprising both healthy and damaged tissue according to the reference Osteoarthritis Research Society International grading system. We established the one-block and multi-block classification models using partial least squares discriminant analysis (PLSDA), random forest, and support vector machine (SVM) algorithms. Feature modeling by principal component analysis was tested for the SVM (PCA-SVM) models. The best one-block models were built using MIR and Raman data, discriminating healthy cartilage from damaged with an accuracy of 77.5% for MIR and 77.8% for Raman using the PCA-SVM algorithm, whereas the NIR data did not perform as well achieving only 68.5% accuracy for the best model using PCA-SVM. The multi-block approach allowed an improvement with an accuracy of 81.4% for the best model by PCA-SVM. Fusing three blocks using MIR, NIR, and Raman by multi-block PLSDA significantly improved the performance of the single-block models to 79.1% correct classification. The significance was proven by statistical testing using analysis of variance. Thus, the study suggests the potential and the complementary value of the fusion of different spectroscopic techniques and provides valuable data analysis tools for the diagnostics of cartilage health.

Challenges and Prospects of DNA-Encoded Library Data Interpretation.

DNA-encoded library (DEL) technology is a powerful platform for the efficient identification of novel chemical matter in the early drug discovery process enabled by parallel screening of vast libraries of encoded small molecules through affinity selection and deep sequencing. While DEL selections provide rich data sets for computational drug discovery, the underlying technical factors influencing DEL data remain incompletely understood. This review systematically examines the key parameters affecting the chemical information in DEL data and their impact on hit triaging and machine learning integration. The need for rigorous data handling and interpretation is emphasized, with standardized methods being critical for the success of DEL-based approaches. Major challenges include the relationship between sequence counts and binding affinities, frequent hitters, and the influence of factors such as inhomogeneous library composition, DNA damage, and linkers on binding modes. Experimental artifacts, such as those caused by protein immobilization and screening matrix effects, further complicate data interpretation. Recent advancements in using machine learning to denoise DEL data and predict drug candidates are highlighted. This review offers practical guidance on adopting best practices for integrating robust methodologies, comprehensive data analysis, and computational tools to improve the accuracy and efficacy of DEL-driven hit discovery.

Studying the Photoactivity of Ag-Decorated TiO2 Nanotubes with Combined AFM and Raman Spectroscopy

The drive for the development of systems that can simultaneously investigate chemical and morphological information comes from the requisite to fully understand the structure and chemical reactivity relationships of materials. This is particularly relevant in photocatalysis, a field ruled by surface interactions. An in-depth understanding of these complex interactions could lead to significant improvements in materials design, and consequently, in photocatalytic performances. Here, we present a first approach to a combined atomic force microscopy (AFM) and Raman spectroscopy characterization of anodic TiO2 nanotubes arrays decorated with Ag nanoparticle electrodeposition from either the same anodizing organic electrolyte or from an aqueous one. Photocatalytic substrates were used in up to 15 consecutive photocatalysis tests to prove their possible deterioration with reuse. Sample aging can, in principle, produce changes in both the morphology and the chemical compounds that characterize the photocatalyst surface. Adopting multiple characterization techniques, such as a combination of AFM and Raman spectroscopy in an original setup, can profitably enable the observation of surface contamination. A significant drop in photocatalytic activity was observed after 10 cycles on samples where silver was deposited from the organic electrolyte, while the others remained stable. Such a drop was ascribed to photocatalyst deactivation. While in other cases, a simple recovery treatment allowed the initial photoactivity to be restored, this deactivation was not restored even after chemical and thermal cleaning treatments.

Raman spectroscopy and imaging in bio and life sciences

AbstractRaman spectroscopy is a well‐known technique to study different materials and determine their chemical composition. It is based on the inelastic scattering of light and provides a unique spectrum for the sample under study. The advances in lasers and detectors, their combination with imaging systems and the discovery of new phenomena have expanded the use of this technique in several research fields. In the particular case of biology and life sciences, it has been used to provide chemical and compositional information on plant and animal tissues, cells, biofluids, etc., with the advantage that the presence of water molecules normally does not cause interference in the measured signal.

Impacts of distorted local chemical coordination on electrochemical performance in hydrated vanadium pentoxide

Modulating and elevating the operating voltage of a given cathode is a significant challenge to enhance the energy density of secondary batteries without sacrificing power output. The chemical coordination strongly influences the energy levels of d-orbitals of redox cations in cathode materials, which tie to their operating voltage. In contrast to concentrated studies on enhancing the specific capacity, in this study, we choose bi-layered hydrated vanadium pentoxide as the model to modulate the d-orbital energy levels through local chemical coordination manipulation, achieving a higher operating voltage in rechargeable aqueous zinc ion batteries. Here we show that, by employing X-ray absorption spectroscopy (XAS) and pair distribution function (PDF) techniques, we can analyze the distortion of [VO6] octahedra and extract chemical bond information, deciphering the correlation between the chemical coordination and operating voltage in cathode materials. The fundamentals could guide the designing and developing RAZIBs with higher energy and power density.

Improvements in Fast Mass Microscopy for Large-Area Samples.

Mass spectrometry imaging (MSI) is a technique that analyzes the chemical information and spatial distribution of surface analytes. Most MSI studies are conducted in microprobe mode, in which a mass spectrum is collected for each pixel to create a mass image. Thus, the spatial resolution, sample imaging area, and imaging speed are linked. In this mode, halving the pixel size quadruples the analytical time, which presents a practical limit on the high spatial resolution MSI throughput. Fast mass microscopy (FMM) is, in contrast, a microscope-mode MSI technique that decouples spatial resolution and imaging speed. FMM circumvents the linear-quadratic relationship of pixel size and analytical time, which enables increased imaging size area and the analytical speed achievable. In this study, we implement instrument modifications to the FMM system, including the addition of linear encoders that enable roughly 8.5× faster imaging than was previously achieved, allowing a 42.5 × 26 mm2 sample area to be imaged at a 1 μm pixel size in <4.5 min. Linear encoders also enable the alignment of multipass images that increase image homogeneity and signal intensity. The applicability of FMM to large area samples has made it important to define the tolerance to height variations of the technique, which was determined to be at least 218 ± 0.03 (n = 3) μm.

Shrouding Gas Plasma Deposition Technique for Developing Low-Friction, Wear-Resistant WS2-Zn Thin Films on Unfilled PEEK: The Relationship Between Process and Coating Properties

In this study, tungsten disulfide–zinc (WS2-Zn) composite films were generated on polyether ether ketone (PEEK) disks by an atmospheric pressure plasma jet (APPJ) equipped with a shrouding attachment. The friction and wear properties of the WS2-Zn coatings were intensively investigated by using a rotational ball-on-disk setup under dry sliding and ambient room conditions. In order to gain more information about the lubrication mechanism, the coating areas as deposited and the worn areas (i.e., in the wear track) were analyzed with a scanning electron microscope (SEM) with regard to their chemical composition in depth by energy-dispersive X-ray spectroscopy (EDS). X-ray photoelectron spectroscopy (XPS) was conducted to obtain precise chemical information from the surface. The results indicated that WS2-Zn coatings significantly improved the tribological properties, exhibiting a coefficient of friction (COF) of &lt;0.2. However, the tribological performance of the coatings is strongly dependent on the plasma process settings (i.e., plasma current, dwell time of the powder particles in the plasma jet), which were tuned to reduce the oxidation by-products of WS2 to a minimum. The COF values achieved of the dry lubricant films were significantly reduced in contrast to the uncoated PEEK by a factor of four.

Geometallurgical modeling and reconciliation with production data in a fhosphate mine

The characterization of the ore and its performance prediction in the processing unit are essential for the success of mining projects and should be part of the resource model. The greatest difficulties encountered in the elaboration of these models are the characteristics of the metallurgical variables, such as non-additivity and non-linearity, lack of uniformity of the database and the few samples of process tests. The generalization of geometallurgical models from primary attributes is a valid option when the relationships between geological characteristics and metallurgical responses are well established and will be tested in this work. Bench tests that simulate the mineral concentration process were performed and compared with geological and chemical information. Multivariate statistical analyses and computational algorithms allowed the prediction of metallurgical attributes from the properties of the rocks simulated by Gaussian sequential simulation. The results were reconciled with the plant data over a period of one year and proved to be consistent.

The Application of a Random Forest Classifier to ToF-SIMS Imaging Data.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is a potent analytical tool that provides spatially resolved chemical information on surfaces at the microscale. However, the hyperspectral nature of ToF-SIMS datasets can be challenging to analyze and interpret. Both supervised and unsupervised machine learning (ML) approaches are increasingly useful to help analyze ToF-SIMS data. Random Forest (RF) has emerged as a robust and powerful algorithm for processing mass spectrometry data. This machine learning approach offers several advantages, including accommodating nonlinear relationships, robustness to outliers in the data, managing the high-dimensional feature space, and mitigating the risk of overfitting. The application of RF to ToF-SIMS imaging facilitates the classification of complex chemical compositions and the identification of features contributing to these classifications. This tutorial aims to assist nonexperts in either machine learning or ToF-SIMS to apply Random Forest to complex ToF-SIMS datasets.

SChemNET: a deep learning framework for predicting small molecules targeting microRNA function

MicroRNAs (miRNAs) have been implicated in human disorders, from cancers to infectious diseases. Targeting miRNAs or their target genes with small molecules offers opportunities to modulate dysregulated cellular processes linked to diseases. Yet, predicting small molecules associated with miRNAs remains challenging due to the small size of small molecule-miRNA datasets. Herein, we develop a generalized deep learning framework, sChemNET, for predicting small molecules affecting miRNA bioactivity based on chemical structure and sequence information. sChemNET overcomes the limitation of sparse chemical information by an objective function that allows the neural network to learn chemical space from a large body of chemical structures yet unknown to affect miRNAs. We experimentally validated small molecules predicted to act on miR-451 or its targets and tested their role in erythrocyte maturation during zebrafish embryogenesis. We also tested small molecules targeting the miR-181 network and other miRNAs using in-vitro and in-vivo experiments. We demonstrate that our machine-learning framework can predict bioactive small molecules targeting miRNAs or their targets in humans and other mammalian organisms.

Droplet Impedance Feedback-Enabled Microsampling Microfluidic Device for Precise Chemical Information Monitoring.

Microelectrodes have transformed our understanding of spatiotemporal responses to electrical stimulation. However, biological signals are often molecular, complicating the capture of intricate chemical signals. The microfluidic chip developed in this paper accurately measures droplet volume by using impedance analysis. The utilization of droplet volume as a feedback signal for precise microsampling pressure control ensures that microsampling remains unaffected by droplet volume influence. Once the microsampling is complete, chemiluminescence detection enables high temporal resolution and continuous and sensitive monitoring of chemical information within the droplets. Experimental verification shows that the chip can avoid volume influence through impedance feedback, achieving consistent and stable microampling at the nanoliter level (0-3 nL). In just 0.3 s, it can perform sensitive chemiluminescence detection of H2O2 and glucose within droplets. The linear detection ranges for these analytes are 10-50,000 and 20-600 μM, respectively, with the limit of detection being 0.648 and 0.334 μM. The significance of this chip lies in its ability to reveal changes in both electrical and chemical signals during transient biological processes. Its potential applications are numerous, encompassing a wide range of emerging areas such as single-cell analysis, cell communication, and cellular immunity.

From Double-Stranded Helicates to Abiotic Double Helical Supramolecular Assemblies.

The folding of oligomeric strands is the method that nature has selected to generate ordered assemblies presenting spectacular functions. In the purpose to mimic these biomacro-molecules and extend their properties and functions, chemists make important efforts to prepare artificial secondary, tertiary, and even quarternary structures based on folded abiotic backbones. A large variety of oligomers and polymers, encoded with chemical informations, were designed, synthesized and characterized, and the establishment of non-covalent interactions lead to complex and functional supramolecular architectures resulting from a spontaneous self-assembly process. The association of comple-mentary molecular strands into double helical structures is a common structural pattern of nucleic acids and proteins, so the synthesis of bio-inspired double helices has emerged as an important subject. In recent years, a number of synthetic oligo-mers have been reported to form stable double helices and it was shown that the equilibrium between single and double helices can be controlled via different stimuli like the modification of the solvent or the temperature. This kind of structure presents highly interesting functions, such as molecular recognition within the cavity of double helices, and some other potential applications will emerge in the future.

QM9star, two Million DFT-computed Equilibrium Structures for Ions and Radicals with Atomic Information

Ions and radicals serve as key intermediates in molecular transformation, with their chemical properties being essential for understanding and predicting reaction reactivity and selectivity. In this data descriptor, we report a quantum chemical dataset named QM9star, comprising cations, anions, and radicals. This dataset is derived from the molecular structures of the QM9 dataset, created by removing terminal hydrogens followed by optimization using B3LYP-D3(BJ)/6-311 + G(d,p) level of density functional theory. The QM9star dataset includes approximately 1.9 million cations, anions, and radicals, along with 120 kilo neutral molecules prior to hydrogen removal. Each entry encompasses both molecular and atomic information: representative global properties include orbital energies, vibrational frequencies, etc., while local properties cover aspects such as charges and spin densities at each atomic site. The QM9star dataset not only serves as a comprehensive source of quantum chemical information for intermediates but also offers insights into the principle of atomic property distribution. We anticipate that these data will aid in machine learning studies related to chemical intermediates and contribute to the molecular representation learning.