Material (Belgium)

The vast repository of published chemical literature constitutes a rich yet deeply flawed resource, where systemic publication bias and data heterogeneity have fostered widespread skepticism regarding its utility for predictive modeling. Here, we demonstrate that this imperfect literature data can be transformed into a robust and generalizable predictive model for asymmetric catalysis. We introduce OrgAlcat, a machine learning framework anchored by iSynth, a rigorously curated dataset of over 22,000 aminocatalytic reactions that bridge the gap between structural repositories and raw patent data. Powered by R-SPOC, a chemically interpretable descriptor that obviates computationally prohibitive quantum mechanical calculations, OrgAlcat predicts enantioselectivity with high fidelity (R 2 > 0.7, MAE < 0.30 kcal/mol) and, crucially, distinguishes intrinsic structure-selectivity relationships from mere literature trends. Validating its practical utility in a "human-in-the-loop" workflow, OrgAlcat guided the Bayesian optimization of a challenging Aldol reaction, boosting enantiomeric excess (ee) from 41% to 96% in just 12 experiments. This work establishes a validated methodology for converting historical literature into actionable intelligence, offering a powerful tool to accelerate catalyst discovery and reaction optimization.

Selective oxidation of 1,3-diisopropylbenzene with molecular oxygen was performed under a heterogeneous catalyst system of manganese molecular sieves (OMS-2) and N-hydroxyphthalimide (NHPI). The catalyst system showed high conversion (>99%) and selectivity (86%) toward the target diol product under mild conditions (40 °C, 560 psi). The recyclability test demonstrated that the catalyst system retained 80% selectivity after three consecutive reactions, highlighting its potential as a practical heterogeneous approach for the reaction. Discussion on the reaction mechanism suggested that a lower reaction temperature and abundant active sites are beneficial to suppress the β-scission reaction, thus improving the selectivity and yield of the target product.

Water-absorbing expansion elastomers, valuable in environmental engineering, are limited by their low mechanical strength, poor self-healing capacity, and recycling challenges. While polyurethane (PU) offers superior mechanical robustness and weather resistance, it inherently lacks hydrophilicity. To address these gaps, this study developed self-healing, hydrophilic swelling PU elastomers via a multiscale synergistic strategy; rigid crystalline hard segments (imidazolidinyl urea) were integrated with soft segments poly(tetrahydrofuran) glycol and hydrophilic poly(ethylene glycol), and 2-hydroxyethyl disulfide (HEDS) was introduced to modulate interfacial interactions, forming a bicontinuous phase network. Fourier transform infrared spectroscopy confirmed the complete reaction of isocyanate groups, validating successful synthesis. Optical transmittance increased with poly(tetrahydrofuran) glycol (PTMEG) content, while X-ray diffraction revealed an enhanced amorphous phase intensity. Mechanically, polyurethane elastomer (PU2) achieved a toughness of 36.50 MJ/m3, and polyurethane elastomer (PU2–17%) demonstrated a tensile strength of 7.4 MPa with 731% elongation. Self-healing efficiency reached 96% (at 100 °C/6 h) with optimized disulfide bond density, though excessive HEDS slowed thermal response via dynamic bond exchange. Notably, polyurethane elastomer (PU1), containing higher PEG content, exhibited the highest swelling ratio (195% at 24 h). The hygroscopic conductive sensor sample with a sandwich structure, denoted as PU2–17%-PPy, possesses a high humidity responsiveness. Response and recovery tests indicated that it featured a humidity response time of 7 s and a full recovery time of 60 s, which was significantly faster than that of traditional humidity-sensitive materials. By synergizing dynamic bonds, soft segment design, and crystalline hard domains, this work provides a sustainable strategy for advanced functional materials, balancing mechanical durability, environmental responsiveness, and recyclability.

Solar interfacial evaporation (SIE) emerges as a pivotal technology for addressing global freshwater scarcity and advancing sustainable development. Gel materials, distinguished by their inherent hydrophilicity and porous architecture, have garnered significant interest for SIE applications. First, this review elaborates on the compelling advantages of gels for SIE, stemming from their low thermal conductivity, reduced evaporation enthalpy, good hydrophilicity, tunable porous structure, adjustable mechanical properties, facile processability, and multifunctional integration capabilities. Subsequently, it focuses on strategies to enhance the performance of gel-based evaporators, specifically addressing the achievement of photothermal stability, mechanical robustness, salt resistance, interfacial stability and antibiofouling stability through rational structural design and exploitation of intrinsic gel properties to realize optimal evaporation performance. Scalable applications of gel-based SIE, including water treatment, auxiliary power generation, atmospheric water harvesting, and metal extraction, are highlighted. Finally, conclusions and perspectives outline current challenges and future research directions for gel-based SIE at both theoretical and practical levels.

Hydrogels are three-dimensional networks of hydrophilic polymers often used as a simplified model of hydrated biological materials, from cartilaginous joints to the ocular tear film. However, the lubrication mechanisms of hydrogels remain poorly understood, partly due to their complex polymeric structure, which creates blurred interfaces during sliding that are challenging to study experimentally. In this study, we employ dissipative particle dynamics (DPD) to investigate the frictional behavior of a polymeric hydrogel network sliding against a solid wall in an explicit viscous solvent. This computational approach enables us to model hydrodynamic interactions and mesoscale polymer dynamics, capturing key aspects of hydrogel friction. Our simulations reveal that hydrogel friction is governed by the interplay between polymer relaxation and viscous shear, characterized by the Weissenberg number (Wi). At low Wi, friction coefficient remain nearly constant, dominated by polymer relaxation. However, at higher Wi, friction is dominated by viscous drag within a near-wall solvent layer, leading to a linear increase in friction coefficient with Wi. Furthermore, our results demonstrate an inverse relationship between the friction coefficient and the applied normal load, consistent with experimental observations. This work provides new insights into the fundamental tribological properties of hydrogels, shedding light on the micromechanics of hydrogel friction. Improving our understanding of hydrogel structure and dynamics under friction advances our knowledge of the mechanisms regulating biological lubrication in health and disease.

The physiological function of the microtubule-binding protein tau is regulated by charge-neutralizing phosphorylation, while hyperphosphorylation drives its irreversible assembly into amyloid fibrils associated with Alzheimer’s disease and other tauopathies. In vitro, tau is remarkably stable and typically requires the use of polyanions such as heparin or RNA to induce fibrillation. Here, we describe an alternative method that uses electroreduction as a surrogate for charge-neutralization by hyperphosphorylation. To follow the kinetics of tau conformational change in real time, we combined our electrochemical method with in situ circular dichroism and UV-absorption spectroscopies. These spectroelectrochemical techniques demonstrate that electroreduction of positively charged amino acids in freely diffusing tau triggers its assembly, with formation of β-rich fibrillar structures observed at reductive potentials sufficient to neutralize lysine residues. Analyses suggest that the negatively charged electrode might provide a templating function analogous to that seen with heparin and other polyanions (heterogeneous coacervation) but without those polymers’ permanent binding to tau fibrils. Taking advantage of our method to rapidly form electro-assemblies in as quickly as 15 min, we demonstrate its applicability for fast screening of small molecules such as epigallocatechin 3-gallate (EGCG) for tau fibril disassembly. This study demonstrates the unique advantage of our spectroelectrochemical method as a rapid and facile approach to induce cofactor free tau assembly, with the ability to observe and kinetically resolve conformational transitions as a function of time, providing a platform for rapid screening of tau-aggregation modulators.

Photocatalytic lignin valorization has garnered significant interest because of its mild reaction conditions and high selectivity. However, the unclear relationships among photocatalysts, reaction solvents, and catalytic activities have hindered further exploration. Herein, we report the innovative synthesis of sulfur vacancy-rich MoS2@ZnIn2S4 heterostructures through one-pot sulfidation of Mo-based metal–organic frameworks (MOFs) for efficient photocatalytic lignin depolymerization. The Mo-MOF-derived MoS2 provides a large surface area and high porosity, enhancing mass transfer, whereas the sulfur vacancies on ZnIn2S4 promote effective charge carrier separation. Notably, the presence of water in the reaction solvent enables the sulfur vacancies to efficiently adsorb and activate water molecules, facilitating proton generation from water dissociation for selective lignin Cβ–O bond hydrogenolysis. The bond cleavage efficiency exhibits a volcano-type trend with increasing water content, achieving optimal performance with 100% conversion of the lignin model compound 2-phenoxy-1-phenylethanol, yielding 78% phenol and 89% acetophenone. This performance significantly surpasses those of MoS2, ZnIn2S4, and the sulfur vacancy-poor MoS2@ZnIn2S4. This work provides critical insights into the solvent-dependent reactivity of catalysts in lignin depolymerization and underscores the potential of defect engineering in photocatalytic biomass valorization.

The tandem electrocatalytic reduction of carbon dioxide (CO2) and nitrate (NO3–) produces high-value organonitrogen compounds via C–N coupling. This strategy is effective for carbon-neutralization and environmental pollution control, especially for acetamide and urea synthesis applications, which are attracting increasing attention. However, the C–N coupling reaction in this technological process still presents many challenges, the most important of which are addressing the low C–N coupling efficiency and selectivity of the target products and identifying the key intermediates and catalytic mechanism. Fortunately, C–N coupling can occur between ketene (*CCO) and ammonia (NH3) or carbon monoxide (*CO) and hydroxylamine (NH2OH), as has been identified by experiment. The coupling of *CCO and NH3 forms a *CC(OH)NH2 intermediate via nucleophilic attack. Copper–palladium (CuPd) nanoparticles (NPs), a potential class of catalysts derived from the Cu(111) facet via Cu/Pd thermal reduction, have recently attracted extensive attention because of their promising electrochemical properties, which make them potential electrocatalysts for the CO2 reduction reaction (CO2RR) and expand the applications of Cu-based materials. We modeled the electrode double layer (EDL) at the CuPd catalyst interface, which was modulated using 1-butyl-3-methylimidazolium tetrafluoroborate ([Bmim][BF4]) ionic liquid (IL) and a pH-dependent (acidic, neutral, and alkaline) microenvironment. We included a series of anions and cations to study their electrode potentials. The [Bmim]+ cation played a crucial role in enriching local CO2 and stabilizing carbon-based intermediates. Our density functional theory (DFT) calculations showed limiting potentials of −0.97 (neutral) and −0.71 V (acidic) for producing acetamide and urea on CuPd NPs, respectively, indicating remarkable CO2-integrated NO3– coreduction activity to acetamide and urea. Furthermore, the ab initio molecular dynamics (AIMD) method was employed to examine the effect of the solvent layer on reaction intermediates by investigating the dynamic structural evolution of O–H and C═O bond length variations under realistic CO2RR conditions. The results showed that CuPd NPs remained stable after 10 ps of simulated time in a pH-dependent microenvironment with [Bmim][BF4] at 300 K. The pH-dependent microenvironment with [Bmim][BF4] plays an important role in designing high-performance catalysts for both C–N and C–C coupling over CuPd NPs catalyst.