Xiao-Yu Cao

The oxygen reduction reaction (ORR) constitutes a central process in electrochemical energy conversion technologies such as fuel cells and metal‐air batteries, yet it is severely limited by sluggish kinetics and large overpotentials. Beyond catalyst design, electrolyte regulation plays a critical role in determining ORR activity. In this study, through a combination of kinetic measurements, spectroscopic analyses, and theoretical calculations, we demonstrate that ORR in the choline chloride (ChCl)‐based deep eutectic solvent (DES) Ethaline is thermodynamically more favorable but kinetically more constrained than in ionic liquids. The introduction of O ₂ is found to substantially perturb the intrinsic hydrogen‐bond network of the DES, while the hydrogen‐bonded environment in turn facilitates the activation of O ₂ toward reduction. On this basis, we propose a novel “hydrogen‐bond network‐activated O ₂ ” mechanism for ORR in DESs, which can be classified as a CEC pathway, wherein O ₂ undergoes a solvent‐induced activation step prior to electron transfer in electrolytes featuring complex hydrogen‐bond networks such as Ethaline. This mechanism provides new insights into electrolyte regulation of ORR and opens new avenues for the design of efficient electrocatalytic systems.

Molecular assembly is a fundamental organizational principle in both living organisms and the fabrication of functional materials. However, artificial self-assembly systems lag far behind biological systems in terms of efficiency, controllability, complexity, and functionality. Here, inspired by catalysis in chemical reactions, we propose a novel strategy, termed as molecular catassembly, that employs catassemblers to dynamically manipulate cooperative multisite noncovalent interactions, thereby directing the pathway and accelerating assembly processes. By translating catalytic and biological principles into the catassembly, we summarize the distinctive features and multifaceted roles of catassemblers in manipulating cooperative multisite noncovalent interactions, facilitating mass transfer in crowded environments, and mediating energy transduction and feedback that endow systems with information-processing capabilities. Furthermore, we emphasize the pivotal role of catassemblers in multistep reaction-assembly cascades for the fabrication of hierarchical functional materials and the regulation of the cellular signaling pathway. We further elucidate how the integration of artificial intelligence technologies offers transformative potential to redefine the research paradigm of molecular (cat-)­assembly. Nevertheless, the research of catassembly remains in its infancy and demands the integration of advanced concepts and methodologies from multiple disciplines. Such interdisciplinary efforts will be crucial for unraveling the complexity and functionality of molecular assembly, ultimately offering new perspectives and methodologies for both life sciences and soft matter research.

Chiral molecular assembly represents a frontier in creating life-inspired functional materials; yet, a predictive framework for its enantioselective catalysis remains elusive. Here, we introduce the Catassembly Triad - a mechanistic paradigm for catalytic enantioselective assembly, demonstrated using chiral supramolecular cages as a model system. Combining in situ characterization, ab initio calculations, and machine learning, we resolve how catalysts must harmonize attachment, chiral control, and detachment to direct assembly pathways. Kinetic and structural analyses reveal that chiral diamine catalysts (e.g., cyclohexanediamine) enforce enantioselectivity by templating transient intermediates with deformation-induced affinity changes driving timely catalyst release. We establish a predictive framework based on quantitative triad metrics (ΔE_att, ΔE_ctl, and ΔE_det) from calculations, validated by Bayesian clustering and experimental testing. The framework achieves a perfect concordance between predicted and observed catalyst efficacy across diverse molecular classes. This framework bridges enzymatic cooperativity and supramolecular assembly, enabling programmable chirality transfer and unlocking catalyst design for adaptive chiral materials.

Synthetic illicit drugs have posed severe threats to health and social security. Differential sensing integrated with multivariate data processing methods provides substantial benefits for the simultaneous detection of multiple drugs. Rational design of sensing elements targets a variety of drugs, and achieving high sensitivity using nontargeted sensing elements remains a challenge. To address these challenges, we design peptide-based sensing elements through rational truncation of key recognition fragments from receptor proteins and propose a dual signal amplification strategy that leverages Förster resonance energy transfer (FRET) enhancement and macromolecule-induced probe enrichment. Two fluorogenic peptides are truncated from the binding domain of GABA_A and opioid receptors. The peptides are then enriched by fluorescently tagged carboxymethyl dextran to form multicomponent complexes and trigger FRET signals. A three-channel four-complex sensor array is constructed, which can simultaneously identify 5 drugs (C = 100 nM) with 100% accuracy and different mixing ratios and achieve quantitative detection of drugs. Moreover, the array excels in distinguishing six simulated drug samples in the artificial urine. The sensor array constructed using this strategy combines high sensitivity with parallel detection capability in complex environments, offering a promising solution to the growing challenge of the detection of increasingly prevalent drugs.

A serendipitous outcome in the mandelic acid-mediated imine condensation of a tetraphenylethene-derived tetraaldehyde and tris(2-aminoethyl)amine led to [2 + 4] face-rotating molecular boxes, rather than the expected [6 + 8] cubic cages. The resulting D₂-symmetric assemblies feature two homochiral tetraphenylethene facial units and four unreacted amine handles. Mandelic acid engages in cooperative hydrogen bonding, steric effect and C-H···π interactions that steer the assembly pathway and supramolecular chirality. NMR, ECD, and X-ray analyses confirm the formation of a pair of diastereomeric supramolecular complexes exhibiting distinct optical activity in solution.

Determining the absolute configuration of chiral molecules is of fundamental importance in the fields of natural products chemistry, asymmetric catalysis, and pharmaceutical development. A widely adopted approach involves the comparison of experimental and theoretical electronic circular dichroism (ECD) spectra, which has proven to be a reliable method for absolute configuration assignment. However, the generation of theoretical ECD spectra via time-dependent density functional theory (TD-DFT) remains the rate-limiting step in this workflow, making its acceleration both essential and challenging. Although recent advances in deep learning offer promising strategies for establishing structure-spectrum relationships and expediting theoretical spectrum prediction, the lack of standardized and comprehensive ECD spectral datasets continues to hinder progress. This study presents the Chiral Molecular Circular Dichroism Spectral (CMCDS) dataset, a systematically structural benchmark dataset that addresses the fragmentation of existing ECD data. Characterized by high standardization, scalability, and broad molecular diversity, CMCDS facilitates deep learning applications in ECD analysis and fosters data-driven discovery of chiral molecules.

Vespa velutina (V. velutina) is a globally widespread predator of honeybees, posing a serious threat to them. Apis cerana (A. cerana) has acquired the ability to partially thwart the predation of the wasps, but the molecular mechanism by which it responds to V. velutina attacks is unknown. Here, both transcriptomic and metabolomic analyses were integrated to decipher what happened in the brain tissues of A. cerana exposed to V. velutina attacks. Transcriptomic analysis revealed 296 differentially expressed genes (DEGs) that were significantly enriched in neural signaling pathways (particularly serotonin and dopamine transmission), the cAMP signaling pathway, and energy metabolism. Metabolomic results showed that V. velutina attacks affected 38 metabolic pathways involving 86 differentially expressed metabolites (DEMs), primarily including amino acids, nucleotides, and carbohydrates. Several metabolites, such as isocitrate, xanthine, and adenosine, were upregulated in response to the hornet presence. Combined pathway analysis indicated an upregulation of the purine and tyrosine metabolism pathways, highlighting their critical role in A. cerana against invasive hornets. The results of this study will help to understand the molecular mechanisms of A. cerana in response to V. velutina attacks and provide valuable insights for enhancing collective defensive behavior against V. velutina in honeybees.

Subtle structural variations among fatty acids significantly influence their biological roles and health effects. However, molecular recognition of their long, flexible, and chemically inert hydrocarbon chains remains a challenge. Inspired by natural fatty acid-binding proteins (FABPs), we designed and synthesized nanotubular metallo-cavitands, termed metal-organic pillars, through the coordination-driven assembly of pillararene-derived ligands with Ag(I) salts. These biomimetic hosts feature continuous interior channels exceeding 2.6 nm, selectively binding long-chain fatty acids through precise size and shape complementarity. Saturated and trans fatty acids, with linear conformations, are effectively encapsulated and stabilized by C-H···π and van der Waals interactions. In contrast, coiled cis-polyunsaturated fatty acids, such as docosahexaenoic acid (DHA), cannot be accommodated due to structural incompatibility. This work highlights the ability of artificial receptors to emulate the recognition capabilities of natural proteins, enabling the targeting of "bad" fatty acids associated with adverse health effects.

We unveil spontaneous fragmentation-induced autocatalysis in heterogeneously catalyzed supramolecular polymerization, where nucleating agents serve as catalysts, driving efficient chiral induction and acceleration at an optimal catalytic equivalent.