Selection of the optimal makeup solvent composition is critical for achieving sensitive and reproducible ionization in supercritical fluid chromatography-mass spectrometry (SFC-MS). This study investigated the ionization processes in a spray-based ionization source called UniSpray (US), by an artificial neural network driven approach, emphasizing the effect of makeup solvent composition. A set of compounds with different physicochemical properties was analyzed using a generic SFC method and 24 makeup solvents. Artificial neural networks were used to correlate molecular descriptors with MS responses and to identify key analyte properties affecting ionization. Statistical analysis of this extensive dataset revealed significant differences in ionization efficiency compared to electrospray ionization (ESI), depending on makeup solvent composition and analyte properties. While US outperformed ESI for 82 % of compounds, certain analytes, including basic beta-blockers, fluorine-substituted compounds, and small lipophilic molecules, benefited from ESI. Optimized makeup solvent compositions differed notably between ESI and US. For example, ethanol and isopropanol were recommended for US+ but not for ESI+. The use of water and ammonia also affected MS responses differently between sources and ionization modes, with optimal concentrations varying depending on the analyte and organic modifier of the SFC mobile phase. This study highlights key differences between SFC-ESI-MS and SFC-US-MS ionization efficiency and demonstrates the utility of data-driven methodologies for faster and more efficient method development.
- Keywords
- Artificial neural networks, Chemometrics, Electrospray, Mass spectrometry, Supercritical fluid chromatography, UniSpray,
- MeSH
- Spectrometry, Mass, Electrospray Ionization * methods MeSH
- Mass Spectrometry * methods MeSH
- Neural Networks, Computer MeSH
- Solvents chemistry MeSH
- Chromatography, Supercritical Fluid * methods MeSH
- Artificial Intelligence * MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Solvents MeSH
This paper presents an intelligent predictive system designed to support real-time decision making in the control of rubber blend mixing processes. The core of the system is a General Regression Neural Network (GRNN), which accurately predicts key process parameters, such as viscosity (expressed as torque), temperature, and energy consumption across varying masses of the processed material. The model can evaluate the mixing progress based on the initial 10% of input data, allowing early intervention and process optimisation. Experimental validation was conducted using a Brabender Plastograph EC Plus with a natural rubber-based blend in the mass range of 60-75 g. The GRNN kernel width parameter (σ) was optimised through a 10-fold cross-validation. High predictive accuracy was confirmed by values of the coefficient of determination (R2) approaching 1, and consistently low values of the root mean square error (RMSE). This system offers a robust and scalable solution for intelligent process control, productivity enhancement, and quality assurance across diverse industrial applications, beyond rubber blending.
- Keywords
- elastomers, general regression neural network, intelligent modelling, mixing process, optimisation, rubber blends,
- Publication type
- Journal Article MeSH
Developing novel memristive systems aims to implement key principles of biological neuron assemblies - plasticity, adaptivity, and self-organization - into artificial devices for parallel, energy-efficient computing. Solid-state memristive devices, such as crossbar arrays and percolated nanoparticle (NP) networks, already demonstrate these properties. However, closer similarity to neural networks is expected from liquid-state systems, including polymer melts, which remain largely unexplored. Here, the resistive switching in silver/poly(ethylene glycol) (Ag/PEG) nanofluids, prepared by depositing gas-aggregated Ag NPs into PEGs of varying molecular mass, is investigated. These systems form long-range conductive NP bridges with reconfigurable resistance states in response to an electric field. The zeta-potential of Ag NPs and molecular mobility of PEG determine the prevalence of low resistance (ohmic) state, high resistance states (poor conductance) or intermediate transition states governed by space-charge-limited conduction or electron tunneling. The occurrence of these states is given by the interparticle gaps, which are determined by the conformation of PEG molecules adsorbed on the NPs. It is presented, for the first time, an equivalent circuit model for the Ag/PEG system. These findings pave the way to adopt polymer melts as matrices for neuromorphic engineering and bio-inspired electronics.
Characterizing biological and environmental samples at a molecular level primarily uses tandem mass spectroscopy (MS/MS), yet the interpretation of tandem mass spectra from untargeted metabolomics experiments remains a challenge. Existing computational methods for predictions from mass spectra rely on limited spectral libraries and on hard-coded human expertise. Here we introduce a transformer-based neural network pre-trained in a self-supervised way on millions of unannotated tandem mass spectra from our GNPS Experimental Mass Spectra (GeMS) dataset mined from the MassIVE GNPS repository. We show that pre-training our model to predict masked spectral peaks and chromatographic retention orders leads to the emergence of rich representations of molecular structures, which we named Deep Representations Empowering the Annotation of Mass Spectra (DreaMS). Further fine-tuning the neural network yields state-of-the-art performance across a variety of tasks. We make our new dataset and model available to the community and release the DreaMS Atlas-a molecular network of 201 million MS/MS spectra constructed using DreaMS annotations.
- Publication type
- Journal Article MeSH
Understanding and predicting mass spectrometry responses in supercritical fluid chromatography-mass spectrometry (SFC-MS) is critical for optimizing detection across diverse analytes and solvent compositions. We present a novel approach using artificial neural networks (ANN) to explore the complex relationships between molecular descriptors of analytes and MS responses in different makeup solvent compositions enabling SFC-MS coupling. 226 molecular descriptors were evaluated for compounds under standardized SFC conditions, with 24 makeup solvent compositions. These makeup solvents included pure alcohols and methanol with varying concentrations of volatile additives. Our results highlight distinct ionization processes for the two most commonly used soft ionization techniques: (i) electrospray ionization (ESI), primarily involving proton or cation transfer, and (ii) atmospheric pressure chemical ionization (APCI), associated with charged ion transfer. Principal component analysis of weights assigned to molecular descriptors reveals that, in positive detection mode, these descriptors effectively differentiate ionization efficiency between ESI and APCI. In contrast, this differentiation is less pronounced in negative mode, where the variance explained is more homogeneously distributed, with stronger discrimination observed when NH3 is used as an additive to the organic modifier. These findings provide critical insights into the influence of molecular descriptors and solvent composition on ionization efficiency, serving as a foundation for future investigations into SFC-MS optimization. This proof-of-concept underscores the feasibility of using predictive models to advance understanding of ionization efficiency and offers a valuable framework for refining SFC-MS workflows in analytical chemistry.
- Publication type
- Journal Article MeSH
A search is presented for the pair production of new heavy resonances, each decaying into a top quark (t) or antiquark and a gluon (g). The analysis uses data recorded with the CMS detector from proton-proton collisions at a center-of-mass energy of 13 Te V at the LHC, corresponding to an integrated luminosity of 138 fb - 1 . Events with one muon or electron, multiple jets, and missing transverse momentum are selected. After using a deep neural network to enrich the data sample with signal-like events, distributions in the scalar sum of the transverse momenta of all reconstructed objects are analyzed in the search for a signal. No significant deviations from the standard model prediction are found. Upper limits at 95% confidence level are set on the product of cross section and branching fraction squared for the pair production of excited top quarks in the t ∗ → tg decay channel. The upper limits range from 120 to 0.8 fb for a t ∗ with spin-1/2 and from 15 to 1.0 fb for a t ∗ with spin-3/2. These correspond to mass exclusion limits up to 1050 and 1700 Ge V for spin-1/2 and spin-3/2 t ∗ particles, respectively. These are the most stringent limits to date on the existence of t ∗ → tg resonances.
- Publication type
- Journal Article MeSH
Efficient heat dissipation is crucial for various industrial and technological applications, ensuring system reliability and performance. Advanced thermal management systems rely on materials with superior thermal conductivity and stability for effective heat transfer. This study investigates the thermal conductivity, viscosity, and stability of hybrid Al2O3-CuO nanoparticles dispersed in Therminol 55, a medium-temperature heat transfer fluid. The nanofluid formulations were prepared with CuO-Al2O3 mass ratios of 10:90, 20:80, and 30:70 and tested at nanoparticle concentrations ranging from 0.1 wt% to 1.0 wt%. Experimental results indicate that the hybrid nanofluids exhibit enhanced thermal conductivity, with a maximum improvement of 32.82% at 1.0 wt% concentration, compared to the base fluid. However, viscosity increases with nanoparticle loading, requiring careful optimization for practical applications. To further analyze and predict thermal conductivity, a Type-2 Fuzzy Neural Network (T2FNN) was employed, demonstrating a correlation coefficient of 96.892%, ensuring high predictive accuracy. The integration of machine learning enables efficient modeling of complex thermal behavior, reducing experimental costs and facilitating optimization. These findings provide insights into the potential application of hybrid nanofluids in solar thermal systems, heat exchangers, and industrial cooling applications.
- Publication type
- Journal Article MeSH
BACKGROUND: The cerebellum is one of the major central nervous structures consistently altered in obesity. Its role in higher cognitive function, parts of which are affected by obesity, is mediated through projections to and from the cerebral cortex. We therefore investigated the relationship between body mass index (BMI) and cerebellocerebral connectivity. METHODS: We utilized the Human Connectome Project's Young Adults dataset, including functional magnetic resonance imaging (fMRI) and behavioral data, to perform connectome-based predictive modeling (CPM) restricted to cerebellocerebral connectivity of resting-state fMRI and task-based fMRI. We developed a Python-based open-source framework to perform CPM, a data-driven technique with built-in cross-validation to establish brain-behavior relationships. Significance was assessed with permutation analysis. RESULTS: We found that (i) cerebellocerebral connectivity predicted BMI, (ii) task-general cerebellocerebral connectivity predicted BMI more reliably than resting-state fMRI and individual task-based fMRI separately, (iii) predictive networks derived this way overlapped with established functional brain networks (namely, frontoparietal networks, the somatomotor network, the salience network, and the default mode network), and (iv) we found there was an inverse overlap between networks predictive of BMI and networks predictive of cognitive measures adversely affected by overweight/obesity. CONCLUSIONS: Our results suggest obesity-specific alterations in cerebellocerebral connectivity, specifically with regard to task execution. With brain areas and brain networks relevant to task performance implicated, these alterations seem to reflect a neurobiological substrate for task performance adversely affected by obesity.
- Keywords
- BMI, Human Connectome Project (HCP), Python, cerebellum, connectome-based predictive modeling, functional magnetic resonance imaging (fMRI),
- MeSH
- Adult MeSH
- Body Mass Index * MeSH
- Connectome * methods MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Young Adult MeSH
- Cerebellum * physiology diagnostic imaging MeSH
- Nerve Net MeSH
- Obesity physiopathology MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
Spinal cord injury (SCI) often leads to central neuropathic pain, a condition associated with significant morbidity and is challenging in terms of the clinical management. Despite extensive efforts, identifying effective biomarkers for neuropathic pain remains elusive. Here we propose a novel approach combining matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with artificial neural networks (ANNs) to discriminate between mass spectral profiles associated with chronic neuropathic pain induced by SCI in female mice. Functional evaluations revealed persistent chronic neuropathic pain following mild SCI as well as minor locomotor disruptions, confirming the value of collecting serum samples. Mass spectra analysis revealed distinct profiles between chronic SCI and sham controls. On applying ANNs, 100% success was achieved in distinguishing between the two groups through the intensities of m/z peaks. Additionally, the ANNs also successfully discriminated between chronic and acute SCI phases. When reflexive pain response data was integrated with mass spectra, there was no improvement in the classification. These findings offer insights into neuropathic pain pathophysiology and underscore the potential of MALDI-TOF MS coupled with ANNs as a diagnostic tool for chronic neuropathic pain, potentially guiding attempts to discover biomarkers and develop treatments.
- Keywords
- Artificial intelligence, Artificial neural networks, Central neuropathic pain, MALDI-TOF MS, Spectral profiles, mass spectrometry, spinal cord injury,
- MeSH
- Biomarkers blood MeSH
- Chronic Pain blood diagnosis etiology MeSH
- Mice, Inbred C57BL MeSH
- Mice MeSH
- Neuralgia * blood diagnosis etiology MeSH
- Neural Networks, Computer * MeSH
- Spinal Cord Injuries * complications blood MeSH
- Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization * methods MeSH
- Animals MeSH
- Check Tag
- Mice MeSH
- Female MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Biomarkers MeSH
Stratified flows are commonly observed in numerous industrial processes. For example, a gas-condensate pipeline typically uses a stratified flow regime. However, this flow arrangement is stable only under a specific set of operating conditions that allows the formation of stratification. In this study, the authors analyzed the flow attributes of Prandtl Eyring liquid past an inclined sheet immersed in a stratified medium. The flow also characterizes the features of the magnetic field along with a first-order chemical reaction. Convective boundary constraints associated with the thermosolutal exchange at the extremity of the domain are also prescribed. The fundamental equations of the study are formulated in dimensional PDEs and converted into dimensionless ODEs via similar variables. The numerical solution of the modelled setup is acquired by executing computations using shooting and RK-4 methods. The intelligent computing paradigm working on the mechanism of the back-propagated Levenberg-Marquardt strategy is also capitalized to forecast the behavior of related physical quantities. Graphs and tables are drawn to elaborate the impression of pertinent factors on flow distributions. It is perceived that the momentum profile diminishes with the magnetic field effect, whereas the opposite behavior is observed for the skin friction coefficient. The thermal and concentration distributions were found to dominate in the absence of stratification. Consideration of convective heating and concentration tends to elevate thermal and mass distributions.
- Keywords
- Artificial neural networking, Chemical reaction, Convective boundary constraints, MHD, Prandtl-Eyring fluid, Thermosolutal stratification,
- Publication type
- Journal Article MeSH
