Chemical chaperones at the interface of proteostasis and metabolostasis.

Ultra-high field solid-state NMR of microporous materials at 1GHz and beyond: A mini review.

The persistent contamination of water sources by perfluorooctanoic acid (PFOA) poses a major environmental and public health challenge. PFOA is a representative member of per- and polyfluoroalkyl substances (PFAS), a class of compounds characterized by high chemical stability, bioaccumulation potential, and toxicity. Conventional water treatment processes are not fully effective in removing PFOA, underscoring the urgent need for advanced remediation strategies. Here, we report the development of Fe-MOF-808, a novel porous material obtained by incorporating binuclear iron species into the Zr6O8 nodes of the MOF-808 framework. Comprehensive structural characterization was performed, including ex/in situ synchrotron-based techniques combined with computational modeling. The results confirm successful iron integration without compromising the structural integrity and accessibility of the porous network. Moreover, the presence of multiple, spatially accessible binding sites enables Fe-MOF-808 to capture PFAS through a combination of electrostatic, hydrophobic and coordinative interactions. This resulted in high removal efficiencies across various water matrices and for a wide range of PFAS pollutants and concentrations. Fe-MOF-808 notably achieves complete PFOA removal within minutes and demonstrates excellent recyclability over multiple adsorption cycles. The material also reaches experimental uptake and a maximum Langmuir adsorption capacity of 2081 and 3120 mg PFOA g-1, respectively, vastly outperforming the pristine MOF-808 and other state-of-the-art MOF materials. Overall, mechanistic insights gained from this study highlight the critical role of designing specific chemical environments within MOFs to maximize pollutant-sorbent interactions.

Luminescence of the transuranic actinides continues to gain interest given the series' unequivocal importance in the development of nuclear energy, technology, and radiotherapies. Luminescence acts as a key identifier for transuranic radioisotopes, with spectra displaying characteristic peaks for each actinide. This review aims to be a comprehensive source for years of studies into the luminescence of Np, Pu, Am, Cm, Bk, Cf, and Es, including insights into their electronic structures and coordination chemistry. This review highlights the photophysical properties of transuranic actinides in a range of chemical environments, including aquo ions, single crystals, metal-ligand complexes, and cases of coordination with proteins. Unique luminescence mechanisms are discussed, including antenna effect, ligand-to-metal charge transfer, two-photon excitation, circularly polarized luminescence, and self-induced luminescence from radioactive decay. The emission spectra for a multitude of species across the series have been extracted and overlaid on singular plots to highlight similarities and oddities within each element. Observations of actinide luminescence have further solidified chemists' understanding of the electronic structure of 5f elements. This review emphasizes how luminescence has helped assign 5f transition states. Such fundamentals contribute to growing interest in f-block chemistry and enable safer use of the actinides in human endeavors.

ConspectusThe synergistic control of lattice properties and porosity with mass transport is a defining feature of molecular lattices known as metal-organic frameworks (MOFs), a capability largely absent in conventional rigid inorganic materials. These open frameworks provide adaptive chemical environments in which guest inclusion can directly reorganize electronic structure and magnetic order. In redox-active MOFs, guest insertion, ejection, and transport, collectively referred to as guest dynamics, can reversibly modulate charge distribution and spin states, enabling electronic and magnetic phase switching. Such dynamic coupling between framework, space, and closely spaced electronic states establishes porous magnets as a versatile platform for stimulus-responsive molecular materials with potential applications in information storage and chemical sensing. This Account summarizes our efforts to develop redox-active MOF magnets based on donor-acceptor (D/A) architectures. These systems integrate redox-active paddlewheel-type diruthenium (II,II) complexes ([Ru2II,II]; donors, D) with π-acidic TCNQ derivatives (acceptors, A), forming layered D2A frameworks that support multiple, closely spaced electronic states. Because these states lie in delicate energetic balance, subtle structural perturbations such as guest adsorption can trigger charge transfer and reorganize magnetic ground states. To enable guest-induced magnetic switching, we have developed two key mechanisms: (1) on-host charge transfer (CT), where neutral guests modulate the electronic state of the host framework, and (2) host-guest CT, where redox-active guests directly exchange electrons with the framework. Whereas host-guest CT is limited to strongly redox-active guests, on-host CT exploits the intrinsic energetic competition between donor and acceptor units, amplified by lattice electrostatics. The central question addressed in this Account is how to rationally design D/A-MOFs poised at electronic instability, such that minor external stimuli can tip the balance between competing charge states. We outline two guiding strategies: positioning donor-acceptor pairs at the boundary of multiple electronic states and targeting systems that display emergent electronic configurations beyond initial predictions. Guided by these principles, five representative systems showing guest-induced on-host CT have been discovered. We hope this Account will encourage continued investigation into these multifunctional materials at the interface of magnetism, electronic regulation, and host-guest chemistry.

Torsional parametrization remains one of the most persistent challenges and weaknesses of modern force fields, particularly for dihedrals whose asymmetry and multimodality evade traditional Fourier or Ryckaert-Bellemans treatments [Shirts, M. R.; Mobley, D. L. In Biomolecular Simulations, Springer, 2013; Vol. 9, pp 71-120 andBowman, J. M. Chem. Rev. 2017, 117, 10034-10072]. Here, we introduce a general and fully automated methodology for dihedral parametrization in both all-atom (AA) and coarse-grained (CG) models based on the Fast Fourier Transform (FFT). This Fourier-analysis framework provides a systematic and unbiased route to reconstruct torsional energy profiles of arbitrary complexity, including nonsymmetric and multimodal shapes that have remained inaccessible to existing parametrization tools. When combined with iterative refinement via QM-MM energy matching in AA models and Iterative Boltzmann Inversion in CG models, our FFT approach yields torsional potentials that quantitatively reproduce reference energy landscapes across a wide variety of chemical environments. We apply this methodology to different molecules within an AA framework, obtaining consistently improved agreement with QM reference profiles. In the CG regime, our method is demonstrated on two systems, the MS-Z molecular switch and the Aβ42 peptide, yielding transferable torsional potentials that enable accurate modeling of their conformational behavior. Overall, this work demonstrates that a FFT-based torsional parametrization is a robust and general strategy for developing next generation force fields.

The artificial neural network-optimized model for exploring local thermal non-equilibrium (LTNE) influences on gyrotactic microorganisms in a chemically reactive SWCNTs-MWCNTs/water-based hybrid nanofluid has a wide range of applications in advanced engineering and biotechnology. It has the potential to upsurge heat transfer performance in microfluidic and cooling systems that incorporate hybrid nanofluid and microbial activity. In biotechnology, the model aids in the optimization of microorganism-driven mixing, sensing, and biodegradation processes in complicated heat and chemical environments. It can also help with the design of more efficient reactors and bio-suspension systems, as gyrotactic microorganisms improve fluid stability and energy transmission. Additionally, the application of neural networks enables real-time optimization and predictive control, which is advantageous for smart bio-nanofluid systems, environmental remediation, and industrial thermal management. The goal of this investigation is to use artificial neural networks to evaluate the outcome of local thermal non-equilibrium on microbes in hybrid nanofluid flow over a sheet with porous media. The liquid phase thermal profile increases as the values of the interphase heat transfer parameter grow, whereas the solid phase thermal profile decreases.

Clear thermoplastic orthodontic aligners are widely used due to their aesthetic appeal and patient comfort; however, exposure to chemically diverse intraoral environments may compromise their physical, aesthetic, and chemical stability. This study aimed to evaluate the effect of acidic, alkaline, and neutral chemical environments on weight variation, pH interaction, colour stability, and leaching behaviour of thermoplastic orthodontic aligner materials. Thirty maxillary thermoplastic orthodontic aligners representing three commercial materials (Erkodur, Duran, and Zendura) were immersed in acidic (lime juice), alkaline (ENO® solution), or neutral (artificial saliva) media for 14 days at 37°C. Assessments were performed at Day 0, Day 7, and Day 14. Weight changes, pH variation, colour stability (ΔE, CIE Lab* using ImageJ and VITA Easyshade), and leaching behaviour (UV-Visible spectrophotometry) were analysed using repeated-measures and one-way ANOVA. Aligners in acidic media exhibited the greatest weight reduction (3.410 ± 0.015g to 3.358 ± 0.025g; p < 0.001), followed by alkaline media. Colour change was highest in acidic conditions (ΔE = 3.08 ± 0.15), moderate in alkaline (1.92 ± 0.12), and minimal in neutral media (0.77 ± 0.08). Leaching absorbance was significantly higher in acidic media at Day 14 (0.083 ± 0.004 AU; p < 0.001). Oral chemical exposures significantly compromise the physicochemical and aesthetic properties of thermoplastic aligners, highlighting the need for careful material selection, patient guidance, and further in vivo studies to confirm clinical impact.

Monitoring internal electrolyte decomposition byproducts is pivotal for the early warning of thermal runaway in lithium-ion batteries yet remains a formidable challenge due to the harsh chemical environment. Herein, we engineer a robust electrochemical sensor based on a CsPbBr3/Al2O3@EVA heterojunction architecture to achieve real-time, in situ tracking of lithium methoxide (CH3OLi) evolution. Through interface engineering, the synthesized quantum dot-Al2O3 composite (8.7at%Al) achieves exceptional stability in reducing electrolytes, attributed to the synergistic dual-passivation of Br-Al interfacial bonding and EVA encapsulation. The sensor exhibits superior sensitivity (0.3 at 10% CH3OLi) and rapid kinetics (response/recovery: 8.98 s/56.11 s), driven by the promoted molecular diffusion in mesoporous Al2O3 and polarized adsorption within the Pb+2-Br- framework. Density functional theory (DFT) calculations further corroborate this mechanism, revealing a strong Li+ binding energy of -1.54 eV at Al-O-Br active sites. Furthermore, by integrating the sensor signals with an XGBoost machine learning algorithm (accuracy >99%), we demonstrate a smart monitoring system capable of accurately predicting battery voltage variations and identifying potential safety hazards. This work establishes a new paradigm merging interface-stabilized materials with intelligent algorithms, transforming battery safety management from passive protection to active early warning.

The sensory biology of mosquito gustation.

Aromatic hydrocarbons such as benzene, toluene, and ethylbenzene are extensively used as solvents in coatings, resin, and artificial leather industries. Azeotropic mixtures involving these compounds are commonly encountered in chemical manufacturing, where accurate azeotropic temperature and composition are essential for designing and optimizing separation processes such as extractive and pressure-swing distillation. In this study, two quantitative structure-property relationship (QSPR) models were developed to predict the azeotropic temperature and composition of binary mixtures containing aromatic hydrocarbons using only molecular structural information. The models show excellent agreement with experimental data (R2 = 0.9454 and 0.9448, = 0.9400 and 0.9413). Internal validation via leave-one-out cross-validation yields = 0.9308 and 0.9364, while external validation using an independent test set yields = 0.8939 and 0.9364, indicating strong robustness and superior predictive performance compared to previously reported models. Molecular geometries were optimized using HyperChem 8.0, employing MM + and PM3 methods. Molecular descriptors were calculated using the Online Chemical Modeling Environment (OCHEM). Binary mixture descriptors were derived from pure-component descriptors via Kay's mixing rule. The genetic function approximation (GFA) algorithm was used to select the most relevant descriptors, and predictive models were constructed using multiple linear regression (MLR). Model robustness and predictive capacity were evaluated using leave-one-out cross-validation and an external test set, with applicability domains assessed via Williams plots. All computational procedures and modeling analyses were performed using OCHEM, SPSS, and HyperChem 8.0.

Chemical bonding can be characterized within quantum chemical topology (QCT), which provides a real-space description via the topological analysis of the electron density and the electron localization function (ELF). While QCT has traditionally been applied on a molecule-by-molecule basis, recent advances in machine learning (ML) and the availability of large quantum chemical datasets now enable bonding analysis at scale. Here, we integrate ELF-based topological descriptors with ML to establish a data-driven framework for mapping chemical bonding across the QM9 dataset. Wavefunctions computed at the B3LYP/6-31G(2df,p) level were used to extract ELF basin populations, which were paired with geometric and bonding descriptors to construct a bond-level dataset. Statistical analysis revealed relationships between ELF populations, bond lengths, and local chemical environments. Regression models were trained to predict ELF electron populations directly from molecular geometry. The best performance was obtained when local environmental descriptors were included, reducing the prediction error by a factor of two relative to models using only the bond type and bond length. These results demonstrate that real-space bonding parameters, such as bond electron populations, can be predicted from simple structural features, enabling scalable and interpretable exploration of chemical bonding across large chemical spaces.

Developing iridium (Ir) catalysts with stable active Irδ+ species is critically important for low-temperature methane combustion. Sulfation modification of the support is effective in regulating the surface chemical environment of active metals via synergistic interaction of sulfates. Herein, a series of Ir/TiO2 catalysts with varying sulfate (SO42-) contents were successfully synthesized and evaluated for methane combustion. Among these, the Ir/TiO2-4.1S catalysts exhibit superior methane combustion activity and thermal stability, achieving a turnover frequency of 10.3 × 10-3 s-1 at 225 °C─5.4 and 17.2 times higher than pristine Ir/TiO2 (1.9 × 10-3 s-1) and excessively sulfated Ir/TiO2-6.7S (0.6 × 10-3 s-1) catalysts, respectively. Detailed characterizations and theoretical calculations reveal that moderate sulfation of TiO2 supports promotes the formation of active Irδ+ species and abundant oxygen vacancies. In situ DRIFTS results further demonstrate that the Ir-TiO2-SO42- synergy effectively stabilizes highly reactive Irδ+ species under methane combustion conditions. This synergistic effect significantly enhances CH4 and O2 adsorption and activation while lowering the dissociation energy of the initial C-H bond in methane, leading to markedly improved methane combustion efficiency. This work deepens the fundamental understanding of the Ir-TiO2-SO42- synergistic interaction in efficient methane combustion, providing valuable guidance for the design of advanced catalysts for low-temperature methane combustion.

Silicon (Si) is a promising anode material due to its high specific capacity (∼3580 mAh g-1), far exceeding that of graphite (∼372 mAh g-1). However, its large volumetric expansion (∼300%) during lithiation induces mechanical stress, fracturing particles, and repeatedly exposing fresh surfaces to the electrolyte. This leads to continuous SEI growth, consuming lithium and electrolyte, and causing rapid capacity fading. To address these issues, strategies such as incorporating Si into graphite (Gr) composites and optimizing electrolytes have shown promise in improving the stability and performance of Si-based anodes. NMR spectroscopy offers element-specific sensitivity and can probe local chemical environments, making it a powerful tool for examining both the surface and bulk properties of battery materials. In this work, we use solid-state NMR spectroscopy to investigate Si/Gr anodes in two systematically chosen electrolytes: one EC-based (known to form organic-rich SEI) and one FEC-based (inorganic-rich SEI). We conducted 1D 7Li, 19F, and 1H NMR experiments to elucidate the lithiation mechanism and identify SEI components in Si/Gr composite anodes during the first cycle and after extended cycling in the fully lithiated state for these two electrolyte systems. Additionally, we performed cross-polarization (CP) and two-dimensional exchange spectroscopy (EXSY) NMR experiments to gain deeper insight into Li+ coordination within different SEI components and to probe dynamic exchange processes between the SEI and lithiated Si/Gr phases (Li x Si/Li x C6). 1H/19F → 7Li CP-MAS EXSY NMR was employed to selectively probe Li+ exchange originating from either the organic or inorganic fraction of the SEI. These NMR results were correlated to the electrochemical performance of the Si/Gr anode in both electrolyte systems.

Charge density is central to density functional theory (DFT), and deep learning charge density has emerged as a promising approach for accelerating electronic-structure calculations. Existing approaches mainly follow two paradigms: methods that predict coefficients of predefined atom-centered basis functions, which embed strong physical priors but restrict representational flexibility, and methods that directly predict values on real-space grids, which are highly expressive yet largely lack physical structure and efficiency. Here, we introduce the Equivariant Atomic Contribution Network (EAC-Net), which bridges these paradigms by decomposing the total charge density into symmetry-consistent, atom-centered contributions coupled to real space rather than directly predicting the full density on a grid or on a basis. This design enables both high accuracy and efficient training with errors typically below 1% across the periodic table and strong generalization to diverse chemical environments. Moreover, the embedded physical prior yields a natural and consistent atomic decomposition of the charge density, producing atomic charges that align with chemical intuition. Together, EAC-Net provides an accurate, efficient, and physically grounded framework for charge density prediction.

This article investigates the efficiency of the vermicomposting process and the agrochemical properties of biohumus obtained from various organic substrates. The results demonstrated a significant increase in the content of organic matter (43.60–47.4%) and nutrient elements in comparison with the control variant during the vermicomposting process. The highest nitrogen content was identified in biohumus produced from cattle manure (2.31%), the highest potassium content in biohumus obtained from sheep manure (4.91%), and the highest phosphorus content in biohumus derived from poultry manure (2.49%). The research findings revealed a direct relationship between the chemical composition of biohumus and soil fertility. In addition, the balanced ratio of nutrient elements was found to be an important factor in the formation of the soil chemical environment.

In solid-phase microextraction (SPME) research, selecting a coating adsorbent with good compatibility for target molecules can be difficult, and there is no specific migration testing method for vinyl acetate monomer, which is commonly used in the production of food contact materials (FCMs). First, 13 metal-organic frameworks (MOFs) with different structural characteristics and surface chemical environments were prepared and divided into three groups (good, medium, and poor) based on the dynamic vapor sorption (DVS) method. The distinction of superiority and inferiority determined by the DVS method was completely consistent with that determined by the extraction effect of the SPME probe, and the data from the two variables exhibited a statistically significant positive correlation. Then, the electrostatic potential (ESP) distribution on the typical material surface and target molecule and the charge density difference (CDD) of their interaction during adsorption were obtained using a computational simulation method. The results showed that ZIF-68 and ZIF-70 had the highest adsorption energy, which was consistent with the adsorption performance. Finally, ZIF-68 was selected as the optimal adsorption material, and the extraction conditions were optimized. The optimized method was successfully applied to test the specific migration amounts of several ethylene vinyl acetate (EVA) copolymer materials.

Microscopic mechanisms of helical protein unfolding under thermal and confinement effects.

Derivation of evidence-based oral-to-parenteral uncertainty factors for medical device toxicological risk assessment.

Abstract Structural and photoluminescence properties of undoped and Eu3+ doped yttrium hydroxide (Y(OH)3:Eu3+) particles synthesized via a hydrothermal method and a novel hybrid method are reported. A Camellia sinensis extract was used to develop the hybrid method. Two precursors - yttrium nitrate and yttrium acetate- were employed. Yttrium hydroxide obtained through the hybrid method was more crystalline and purer, even at a shorter reaction time (4h), compared to the hydrothermal method. An X-ray photoelectron spectroscopy (XPS) analysis was carried out to study the chemical environment and possible interactions of Eu3+ with Camellia sinensis biomolecules. Band gap energies were obtained using the GapExtractor© software. Finally, Y(OH)3:Eu3+ exhibited strong red photoluminescence, observed using a specialized technique with Raman.