Browse Articles

Alternative solid hydrocarbons as propellants for electrothermal plasma radio-frequency thrusters

Correction: Adverse competition-related cognitions and it’s relation to satisfaction and subjective performance: a validation study in a sample of English-speaking athletes

Single antivenom protects against 17 different snakebites

Manipulating Interfacial Charge Distribution for Water Reduction

Correction: Programmed cell death and redox metabolism protect Chlamydomonas reinhardtii populations from the galactic cosmic environment on the Artemis-1 mission

Hijacking Extracellular Targeted Protein Degrader–Drug Conjugates for Enhanced Drug Delivery

Study on fault vibration characteristics of shield cutters based on FEM-MBD

Spin Glass Behavior and Giant Magnetoresistance via Aliovalent Fe/Ni Alloying in Amorphous Tetrathiafulvalene-Tetrathiolate Coordination Polymers

Mechanistic insights into efficient catechol removal using saponin-based emulsion liquid membrane

Facile binder-free hydrothermal synthesis of NiCo2O4 using different reagents: a study as efficient supercapacitor electrode

Pain catastrophizing and its domains significantly impact rheumatoid arthritis disease activity

“De Novo” Construction of Planar-Chiral Ferrocenes via Palladium/Chiral Norbornene Cooperative Catalysis

Analysis of TGA data for polyvinyl alcohol at slow heating rate using deep neural networks, activation energy, and activation enthalpy

Abstract This research aims to enhance the deep neural network (DNN) model previously developed by this group, as referenced in 10 , by integrating degradation time into its feature set. It specifically addresses the thermal decomposition of polyvinyl alcohol (PVA) at low heating rates of 2, 5, and 10 °C.min -1 . In addition, the study presents a thermo-kinetic analysis of the data, facilitating the estimation of activation energy and activation enthalpy. The inputs to the DNN frameworks include degradation time, degradation temperature, and heating rate. Modifications were made to the DNN model to tackle overfitting and reduce the discrepancy between output signals and experimental scatter. This was accomplished through iterative adjustments to the learning rate, implementation of data augmentation techniques, prolongation of the training duration, and early termination to minimize error. An optimized DNN architecture, comprising two hidden layers and eight neurons, effectively facilitated the learning algorithms and successfully trained arbitrary constants. This resulted in output signals that closely aligned with the experimental data ( $$\:{R}^{2}\sim0.999$$ ), thereby providing a ranking of parameter sensitivity characterized by heating rate, time, and degradation temperature. The Flynn-Wall-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) model-free equations were used to estimate the activation energy ( $$\:{E}_{A}$$ ) of the thermogravimetric (TGA) data curves. The estimated average $$\:{E}_{A}$$ values derived from the FWO and KAS model-free equations were 64.6±3.2 kJ·mol⁻¹ and 58.8±2.9 kJ·mol⁻¹, respectively, based on conversion rates between 5 and 50 wt% (i.e., where $$\:{R}^{2}>0.9$$ ). The estimated theoretical value of the activation enthalpy (ΔH) required for the formation of the activation complex, at these higher correlations, was determined to be positive (25.4– 102.0 kJ·mol⁻¹), indicating that the reaction is invariably endothermic and is consistent with established information in the literature. This approach could prove pivotal for manufacturers in the design and fabrication of polyvinyl alcohol (PVA) and composites with enhanced and novel properties.

Cardio-renal protection and mechanisms of the antihypertensive activity of Alcalase-derived moth bean hydrolysate in DOCA-salt induced hypertensive Wistar rats

Coexistence of Liquid- and Solid-like Ion Transport in Metal–Organic Frameworks

Long-term retinal oximetry and OCT angiography in patients recovered from COVID-19

Abstract The aim of this study was to analyse retinal microvascular abnormalities in patients with various degrees of severity of the course of Coronavirus Disease 2019 (COVID-19), with a focus on patients requiring extracorporeal membrane oxygenation (ECMO) or mechanical ventilation (MV). We subclassified the patients after COVID-19 into 3 groups based on the severity of the disease and then performed standard ophthalmic examination, optical coherence tomography (OCT), macular OCT angiography (OCT-A) and retinal oximetry (RO). A total of 32 patients (21 men and 11 women; mean age of 51 years) were included in the study. Group 1 (mild COVID-19 course) included 11 patients, group 2 (moderate course) included 8 patients, and group 3 (severe course) included 13 patients which is particularly noteworthy. The median time after COVID-19 recovery was 22 months. No statistically significant difference between the groups was detected in any of the parameters of interest, suggesting resilience of retinal vasculature post-COVID-19. Patients after full recovery from severe COVID-19 do not show any significant decrease in oxygen saturation of retinal vessels 1.5 years (median) after the episode. Our data show that even a severe course of COVID-19 with ECMO or MV does not cause chronic microvascular changes in the retina.

This ‘impressive’ AI model predicted Hurricane Melissa’s perilous growth

Mechanistic Elucidation and Stereochemical Consequences of Alternative Binding of Alkenyl Substrates by Engineered Arylmalonate Decarboxylase

Analysing the effect of perceived social support on exercise engagement in adolescents using a chained mediation model

Sulfonated Module Aggregation for Ultrahigh Birefringence in Aqueous-Processable Crystals