Physchem

Troponin C (TnC) is the Ca²⁺-sensing subunit of troponin that is responsible for activating thin filaments in striated muscle, and, in turn, for regulating the systolic and diastolic contractile function of cardiac muscle. The secondary structure of vertebrate TnC is mainly composed of α-helices, with nine helices named sequentially, starting from the amino terminus, from N to A–H. The N-helix is a 12-residue-long α-helix located at the extreme amino terminus of the protein and is the only helical structure that does not participate in forming Ca²⁺-binding EF-hands. Evolutionarily, the N-helix is found only in TnC from mammalian species and most other vertebrates and is not present in other Ca²⁺-binding protein members of the calmodulin (CaM) family. Furthermore, the primary sequence of the N-helix differs between the genetic isoforms of the fast skeletal TnC (sTnC) and cardiac/slow skeletal TnC (cTnC). The 3D location of the N-helix within the troponin complex is also distinct between skeletal and cardiac troponin. Physical chemistry and biophysical studies centered on the sTnC N-helix demonstrate that it is crucial to the thermal stability and Ca²⁺ sensitivity of thin filament-regulated MgATPase activity in solution and to isometric force generation in the sarcomere. Comparable studies on the cTnC N-helix have not yet been performed despite the identification of cardiomyopathy-associated genetic variants that affect the residues of cTnC’s N-helix. Here, we review the current status of the research on TnC’s N-helix and establish future directions to elucidate its functional significance. Full article

Bio-hybrid fuels, chemically derived from sustainable raw materials and green energies, offer significant potential to reduce carbon dioxide emissions in the transport sector. However, when these fuels are used as drop-in replacements in internal combustion engines, material compatibility with common sealing materials is not always given. Within the cluster of excellence, “The Fuel Science Center (FSC)” at RWTH Aachen, experimental immersion tests were conducted on a limited set of fuel and sealing material combinations. Given the extensive range of possible fuel and sealing combinations, a data-based machine learning prediction framework was developed and validated to pre-select promising fuel candidates. Due to the limited number of samples, preliminary results indicate a need to expand the database. Since experimental investigations are time-consuming and costly, this work explores faster physics-motivated data generation approaches by modeling molecular interactions between fuel and sealing materials. Two modeling scales are employed. One calculates the intermolecular distance using density functional theory. The other uses Hansen solubility parameters, representing an abstract modeling of intermolecular forces. Both approaches are compared, and their limitations are assessed. Including the generated data in the prediction framework improves its accuracy. Full article

This review focuses on the rheological behavior of polystyrene (PS) composites reinforced with carbon nanotubes (CNTs), providing an in-depth analysis of how CNT incorporation affects the viscosity, elasticity, and flow properties of these materials. The review covers fundamental aspects of PS and CNT structures, emphasizing their influence on the composite’s rheological properties. Key interaction mechanisms, including van der Waals forces and covalent bonding, are discussed for their role in determining material behavior. Various preparation methods, such as melt mixing, solution mixing, and in situ polymerization, are evaluated based on their impact on CNT dispersion and rheological performance. The study examines critical rheological parameters such as relative and complex viscosity, shear thinning, and elasticity, supported by theoretical models and experimental findings. The review also identifies major challenges, such as achieving uniform CNT dispersion and addressing processing limitations, while offering insights into future research directions aimed at improving the rheological performance and scalability of PS/CNT composites for advanced applications. Full article

The removal of cadmium ions (Cd²⁺) using raw argan shells (ArS) was optimized through experimental and theoretical studies. Adsorption experiments revealed optimal conditions at an adsorbent dose of 3.5 g, an initial Cd²⁺ concentration of 20 mg·L⁻¹, and a pH of 8, achieving a maximum sorption capacity of 3.92 mg·g⁻¹. The kinetic analysis showed that the adsorption followed a pseudo-second-order model (R² = 0.98), and the Langmuir isotherm model predicted a maximum adsorption capacity of 4 mg·g⁻¹. Thermodynamic analysis indicated an endothermic adsorption process, with ΔG° shifting from positive to negative as temperature increased, confirming that adsorption is favored at higher temperatures. Desorption studies demonstrated that HCl was the most effective eluting agent, achieving a desorption efficiency of 90.02%, followed by HNO₃ (76.65%) and CH₃COOH (71.59%). The varying desorption efficiencies were attributed to differences in acid strength and ionic interactions with Cd²⁺. This study demonstrates the potential of raw argan shells as an efficient, reusable, and sustainable biosorbent for cadmium removal, offering a promising solution for water treatment and environmental remediation. Full article

In this research, the use of machine learning techniques for predicting the state of health (SoH) of 5 Ah—21,700 lithium-ion cells were explored; data from an experimental aging test were used to build the prediction model. The main objective of this work is to develop a robust model for battery health estimation, which is crucial for enhancing the lifespan and performance of lithium-ion batteries in different applications, such as electric vehicles and energy storage systems. Two machine learning models: support vector regression (SVR) and random forest (RF) were designed and evaluated. The random forest model, which is a novel strategy for SoH prediction application, was trained using experimental features, including current (A), potential (V), and temperature (°C), and tuned through a grid search for performance optimization. The developed models were evaluated using two performance metrics, including R² and root mean squared error (RMSE). The obtained results show that the random forest model outperformed the SVR model, achieving an R² of 0.92 and an RMSE of 0.06, compared to an R² of 0.85 and an RMSE of 0.08 for SVR. These findings demonstrate that random forest is an effective and robust strategy for SoH prediction, offering a promising alternative to existing SoH monitoring strategies. Full article

Nanomaterials have attracted significant attention in recent decades for their diverse applications, including energy storage devices like supercapacitors. Among these, cobalt oxide (Co₃O₄) nanostructures stand out due to their high theoretical capacitance, unique electrical properties, and tunable morphology. This study explores the hydrothermal synthesis of Co₃O₄, revealing that the molar ratio of cobalt nitrate to potassium hydroxide significantly influences the morphology, crystal structure, and electrochemical performance. An optimized 1:1 molar ratio (COK 11) yielded well-defined cubic nanostructures with uniform elemental distribution, as confirmed by SEM, TEM, and EDS analyses. Structural characterization through XRD, XPS, and FTIR validated the formation of the Co₃O₄ spinel phase with distinctive lattice and surface oxygen features. Electrochemical property analysis demonstrated the superior performance of the COK 11 electrode, achieving a high specific capacity of 825 ± 3 F/g at a current density of 1 A/g, a rate capability of 56.88%, and excellent cycle stability of 88% at 3 A/g after 10,000 cycles. These properties are attributed to the nano-cubic morphology and interconnected porosity, which enhanced ion transport and active surface area. This study highlights the importance of synthesis parameters in tailoring nanomaterials for energy storage, establishing COK 11 as a promising candidate for next-generation high-performance supercapacitor applications. Full article

Silicon carbide (SiC) MOSFETs, as a member of the emerging technology of wide-bandgap (WBG) semiconductors, are transforming high-power and high-temperature applications due to their superior electrical and thermal properties. Their potential to outperform traditional silicon-based devices, particularly in terms of efficiency and operational stability, has made them a popular choice for power electronics. However, reliability issues about numerous failure types, including gate-oxide degradation, threshold voltage instability, and body diode degeneration, remain serious challenges. This article critically evaluates the key failure mechanisms that affect SiC MOSFET reliability and their impact on device performance. Furthermore, this paper discusses current advances in SiC technology, including both improvements and continued dependability difficulties. Key areas of future study are suggested, with an emphasis on improved material characterization, thermal management, and creative device architecture to improve SiC MOSFET performance and long-term reliability. The insights presented will help to improve the design and testing processes required for SiC MOSFETs’ widespread use in critical high-power applications. Full article

Previously synthesized and tested water-dispersible photoactive polymeric microparticles have been employed as heterogenous photosensitizers to evaluate their performance in generating singlet oxygen through direct solar irradiation. This study utilizes these photocatalysts for the degradation of Acetamiprid in IWWTP wastewater effluents from the Agri-food industry, exploring, in addition to direct or simulated solar irradiation, the influence of pH on the photooxidation process. Over a thousand emerging pollutants, including pesticides like Acetamiprid, have been detected in aquatic environments in recent years, posing challenges due to the limitations of current wastewater treatment technologies. The developed method is particularly effective under basic or slightly basic conditions, aligning with the natural pH of wastewater and addressing a limitation of conventional Acetamiprid degradation methods, which typically require medium acidification to be effective. Polymers P3 and P4 exhibited high photocatalytic activity, achieving over 99% degradation of Acetamiprid through oxidation via singlet oxygen generated by Rose Bengal supported on the polymer matrix, while maintaining catalytic efficiency across multiple cycles. The results confirm that Acetamiprid removal from industrial wastewater via direct solar irradiation is feasible, though constrained by the availability of sufficient effective sunlight hours. Full article

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, continually undergoes mutation, leading to variants with altered pathogenicity and transmissibility. The Omicron variant (B.1.1.529), first identified in South Africa in 2021, has become the dominant strain worldwide. It harbors approximately 50 mutations compared to the original strain, with 15 located in the receptor-binding domain (RBD) of the spike protein that facilitates viral entry via binding to the human angiotensin-converting enzyme 2 (ACE2) receptor. How do these mutated residues modulate the intermolecular interactions and binding affinity between the RBD and ACE2? This is a question of great theoretical importance and practical implication. In this study, we employed quantum chemical calculations at the B2PLYP-D3/def2-TZVP level of theory to investigate the molecular determinants governing Omicron’s ACE2 interaction. Comparative analysis of the Omicron and wild-type RBD–ACE2 interfaces revealed that mutations including S477N, Q493R, Q498R, and N501Y enhance binding through the formation of bifurcated hydrogen bonds, π–π stacking, and cation–π interactions. These favorable interactions counterbalance such destabilizing mutations as K417N, G446S, G496S, and Y505H, which disrupt salt bridges and hydrogen bonds. Additionally, allosteric effects improve the contributions of non-mutated residues (notably A475, Y453, and F486) via structural realignment and novel hydrogen bonding with ACE2 residues such as S19, leading to an overall increase in the electrostatic and π-system interaction energy. In conclusion, our findings provide a mechanistic basis for Omicron’s increased infectivity and offer valuable insights for the development of targeted antiviral therapies. Moreover, from a methodological perspective, we directly calculated mutation-induced binding energy changes at the residue level using advanced quantum chemical methods rather than relying on the indirect decomposition schemes typical of molecular dynamics-based free energy analyses. The strong correlation between calculated energy differences and experimental deep mutational scanning (DMS) data underscores the robustness of the theoretical framework in predicting the effects of RBD mutations on ACE2 binding affinity. This demonstrates the potential of quantum chemical methods as predictive tools for studying mutation-induced changes in protein–protein interactions and guiding rational therapeutic design. Full article

Galactomannan/organomodified montmoriollonite (G₁M/OM-MMT) nanocomposites and G₂M/OM-MMT nanocomposites were biosynthesized using galactomannan (GM) and organomodified montmorillonite (OM-MMT) with cetyltrimethylammonium bromide (CTAB, 10⁻² M) designed for antioxidant activities. Furthermore, galactomannan (GM) was isolated from fruit rind of Punica granatum grown in the Djelfa region, in Algeria, and the nanoclay used in this work was an Algerian montmorillonite. Two different types of nanocomposites were synthetized using different amounts of GM and OM-MMT (w/w) [GM₁/OM-MMT (0.5:1) and GM₂/OM-MMT (0.5:2)] via a solution interaction method. FTIR analysis confirmed the intercalation of GM in the interlayer of OM-MMT. Moreover, X-ray diffraction (XRD) showed that the interlayer space of OM-MMT was increased from 124.6 nm to 209.9 nm, and regarding the intercalation of GM in the OM-MMT interlayers, scanning electron microscopy (SEM) and energy-dispersive X-ray (DEX) confirmed the intercalated structure of the nanocomposites, while thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) improved the thermal stability of the synthesized bionanocomposites. The antioxidant activities of the GM₁/OM-MMT nanocomposites and GM₂/OM-MMT nanocomposites were evaluated with a spectrophotometer and DPPH (1,1-diphenyl-2-picrylhydrazine) radical scavenging assay. GM₁/OM-MMT nanocomposites and GM₂/OM-MMT nanocomposites gave good antioxidant activity. Indeed, GM₁/OM-MMT had an IC₅₀ of 0.19 mg/mL and GM₂/OM-MMT an IC₅₀ of 0.28 mg/mL. Full article

Dissolved oxygen (DO) is a crucial parameter in water quality monitoring because it directly affects the health of aquatic ecosystems. This study explored electrodeposited Cu₂O thin-film semiconductors for DO sensing. Cu₂O was chosen for its low cost, eco-friendliness, and non-toxic nature. Cu₂O films were electrodeposited on titanium (Ti) substrates using an acetate bath (0.1 M sodium acetate and 0.01 M cupric acetate) at −200 mV versus Ag/AgCl for 30 min, with a bath temperature of 55 °C, stirred at 50 rpm. The bath pH was systematically adjusted from 5.8 to 6.8 in 0.2 steps using NaOH and Acetic acid. A range of analyses including synchrotron X-ray diffraction (SXRD), scanning electron microscopy (SEM), surface wettability, capacitance–voltage (C-V), Raman spectroscopy, Fourier-transform infrared (FTIR) spectrum, and Electrochemical Impedance Spectroscopy (EIS) was performed to assess their properties and sensing performance. The results showed that Cu₂O films deposited at pH 6.4 exhibited optimal performance for DO sensing, with a strong linear response, marking this pH, deposition time, and temperature as ideal for creating effective DO sensors. This study introduces a novel, cost-effective approach to dissolved oxygen sensing using electrodeposited n-Cu₂O thin-film semiconductors, marking the first application of this material in such sensors and showcasing its potential for scalable and environmentally sustainable sensing technologies. Full article

Adsorption is a complex process since it is affected by multiple variables related to the physicochemical properties of the adsorbate, the adsorbent and the interface; therefore, to understand the adsorption process in batch systems, kinetics, isotherms empiric models are commonly used. On the other hand, artificial neural networks (ANNs) have proven to be useful in solving a wide variety of complex problems in science and engineering due to their combination of computational efficiency and precision in the results; for this reason, in recent years, ANNs have begun to be used for describing adsorption processes. In this work, we present an ANN model of the adsorption of fluoride ions in water with layered double hydroxides (LDHs) and its comparison with empirical kinetic adsorption models. LHD was synthesized and characterized using X-Ray diffraction, FT-Infrared spectroscopy, BET analyses and zero point of charge. Fluoride ion adsorption was evaluated under different experimental conditions, including contact time, initial pH and initial fluoride ion concentration. A total of 262 experiments were conducted, and the resulting data were used for training and testing the ANN model. The results indicate that the ANN can accurately forecast the adsorption conditions with a determination coefficient $R^2$ of 0.9918. Full article

Feed-forward neural networks (NNs) are widely used for the machine learning of properties of materials and molecules from descriptors of their composition and structure (materials informatics) as well as in other physics and chemistry applications. Often, multilayer (so-called “deep”) NNs are used. Considering that universal approximator properties hold for single-hidden-layer NNs, we compare here the performance of single-hidden-layer NNs (SLNN) with that of multilayer NNs (MLNN), including those previously reported in different applications. We consider three representative cases: the prediction of the band gaps of two-dimensional materials, prediction of the reorganization energies of oligomers, and prediction of the formation energies of polyaromatic hydrocarbons. In all cases, results as good as or better than those obtained with an MLNN could be obtained with an SLNN, and with a much smaller number of neurons. As SLNNs offer a number of advantages (including ease of construction and use, more favorable scaling of the number of nonlinear parameters, and ease of the modulation of properties of the NN model by the choice of the neuron activation function), we hope that this work will entice researchers to have a closer look at when an MLNN is genuinely needed and when an SLNN could be sufficient. Full article

In recent years, perovskites have quickly gained popularity in applications related to photonic devices and in photovoltaic applications. Over the last several years, the efficiency of photovoltaic (PV) cells based on perovskites has matched the efficiency of PV cells based on silicon. CsPbBr₃ perovskite is gaining more and more popularity, but due to the too large band gap value, its use in photovoltaics is difficult. Another perovskite, very intensively researched and giving hope for further development of photovoltaics, is CsPbI₃. The CsPbI₃ band gap is smaller than the CsPbBr₃ band gap and more suitable for photovoltaic applications. However, CsPbI₃ is unstable under the conditions of solar cell operation. To reduce the band gap value and increase the perovskite stability, very intensive research, both theoretical and experimental, is devoted to structures with mixed halides, i.e., a mixture of bromine and iodine with the general formula CsPbBr_xI_3−x. Computational methods based on DFT have been successfully used for many years to determine the parameters and properties of materials. The use of computational methods significantly reduces the costs of the research performed compared to experimental techniques. The aim of this work is to understand the band gap changes based on DFT calculations as well as XRD and UV-Vis experiments for CsPbBr₃, CsPbI₃, and CsPbBr_xI_3x perovskites. Full article

In this paper, the benefits of using monodisperse polymeric particles as matrices to immobilize biosystems are presented and discussed. The nature of the polymer (natural, synthetic, or semisynthetic) and immobilization techniques were directly related to the performance of this process. In addition, this work reviews the major biological and synthetic entities that have been immobilized on monodisperse polymeric particles and their potential applications available in the literature. The research revealed that enzymes, proteins, cells, and drugs are the main entities immobilized on polymeric matrices. Several physicochemical characterization techniques were discussed to determine the presence of entities after the immobilization process. In addition, some applications of immobilized enzymes in different areas are also presented since this biomolecule was the most frequent entity in terms of immobilization on polymeric matrices. Finally, this review describes the main advances in polymeric materials used as supports for immobilizing biosystems due to their interesting physical and chemical properties. Full article

Na₃V₂(PO₄)₃ (NVP), a NASICON-type material, has gained attention as a promising battery cathode owing to its high sodium mobility and excellent structural stability. Using computational simulation techniques based on classical potentials and density functional theory (DFT), we examine the defect characteristics, diffusion mechanisms, and dopant behavior of the NVP. The study found that the Na Frenkel defect is the most favorable intrinsic defect, supporting the desodiation process necessary for capacity and enabling vacancy-assisted Na-ion migration. The Na migration is anticipated through a long-range zig-zag pathway with an overall activation energy of 0.70 eV. K and Sc preferentially occupy Na and V sites without creating charge-compensating defects. Substituting Mg at the V site can simultaneously increase Na content by forming interstitials and reducing the band gap. Additionally, doping Ti at the V site promotes the formation of Na vacancies necessary for vacancy-assisted migration, leading to a notable improvement in electronic conductivity. Full article

Fast electrochemical phenomena occurring in supercapacitors are hard to analyze by ex situ or in situ techniques because many of them are meta-stable (the supercapacitor relaxes once it is not further polarized). In a steady state, one observes the effect of charge storage but not necessarily the mechanism. This is a problem for Raman spectroscopy, too, even though Raman spectra of the electrodes of supercapacitors are commonly recorded ex situ or in a steady state in situ. Raman operando is desired, but it represents a technological challenge since the electrochemical events in a supercapacitor are very fast (occurring within seconds), and in contrast, Raman requires from seconds to minutes to collect enough photons for reliable spectra. This work presents the development of electrodes made of thin layers of iron oxide grown solvothermally on Si wafers, with a porosified surface and resistivity of 0.005 Ωcm, to study their performance as electrodes in supercapacitors and analyze their energy storage mechanisms by cyclic voltammetry and Raman operando. Being flat and containing just iron oxide and silicon, these electrodes allow for studying interfacial phenomena with minor interferents. Full article

In the present study, we report infrared and Raman spectra in both solution and the solid state, together with a state-of-the art inelastic neutron scattering spectrum, of the unusual molecule pagodane. Periodic DFT calculations have enabled a complete assignment of all the modes. The isolated molecule has D_2h symmetry, which is reduced to C_i in the solid state. However, the preservation of the centre of symmetry means that the selection rules for infrared and Raman spectroscopy are almost unchanged. The exceptions are the D_2hA_u modes that are forbidden in the isolated molecule but become allowed in the solid state. These have been located in the solid-state spectra. Full article

In today’s tech world of digitalization, engineers are leveraging tools such as artificial intelligence for analyzing data in order to enhance their capability in evaluating product quality effectively. This research study adds value by applying algorithms and various machine learning techniques—such as support vector regression, Gaussian process regression, and artificial neural networks—on a dataset related to the grinding process of UNS S34700 steel. What sets this study apart is its consideration of factors like three types of grinding wheels, four distinct cooling solutions, and seven varied depths of cut. These parameters are assessed for their impact on surface roughness and grinding forces, resulting in the conversion of information into insights. A relational equation with 25 coefficients is developed, using optimized algorithms to predict surface roughness with an 85 percent accuracy and grinding forces with a 90 percent accuracy rate. Learning from machine models like the Gaussian process regression exhibited stability, with an R² value of 0.98 and a mean accuracy of 93 percent. Artificial neural networks achieved an R² value of 0.96, and an accuracy rate of 90 percent. These findings suggest that machine learning techniques are versatile and precise when dealing with datasets. They align well with digitalization and predictive trends. In conclusion; machine learning provides flexibility and superior accuracy for predicting data trends compared to the formulaic approach, which is contained to existing datasets only. The versatility of machine learning highlights its significance in engineering practices for making data-informed decisions. Full article