Layered graphene oxide membranes (GOM) with densely packed sub-nanometer-wide lamellar channels show exceptional ionic and molecular transport properties. Mass and charge transport in existing materials follows their concentration gradient, whereas attaining anti-gradient transport, also called active transport, remains a great challenge. Here, we demonstrate a coupled photon-electron-ion transport phenomenon through the GOM. Upon asymmetric light illumination, cations are able to move thermodynamically uphill over a broad range of concentrations, at rates much faster than that via simple diffusion. We propose, as a plausible mechanism, that light irradiation reduces the local electric potential on the GOM following a carrier diffusion mechanism. When the illumination is applied to an off-center position, an electric potential difference is built that can drive the transport of ionic species. We further develop photonic ion switches, photonic ion diodes, and photonic ion transistors as the fundamental elements for active ion sieving and artificial photosynthesis on synthetic nanofluidic circuits.

Gas separation is crucial for industrial production and environmental protection, with metal-organic frameworks (MOFs) offering a promising solution due to their tunable structural properties and chemical compositions. Traditional simulation approaches, such as molecular dynamics, are complex and computationally demanding. Although feature engineering-based machine learning methods perform better, they are susceptible to overfitting because of limited labeled data. Furthermore, these methods are typically designed for single tasks, such as predicting gas adsorption capacity under specific conditions, which restricts the utilization of comprehensive datasets including all adsorption capacities. To address these challenges, we propose Uni-MOF, an innovative framework for large-scale, three-dimensional MOF representation learning, designed for multi-purpose gas prediction. Specifically, Uni-MOF serves as a versatile gas adsorption estimator for MOF materials, employing pure three-dimensional representations learned from over 631,000 collected MOF and COF structures. Our experimental results show that Uni-MOF can automatically extract structural representations and predict adsorption capacities under various operating conditions using a single model. For simulated data, Uni-MOF exhibits remarkably high predictive accuracy across all datasets. Additionally, the values predicted by Uni-MOF correspond with the outcomes of adsorption experiments. Furthermore, Uni-MOF demonstrates considerable potential for broad applicability in predicting a wide array of other properties.

Aging has a serious impact on the properties of functional polymers. Therefore, it is necessary to study the aging mechanism to prolong the service and storage life of polymer-based devices and materials. Due to the limitations of traditional experimental methods, more and more studies have adopted molecular simulations to analyze the intrinsic mechanisms of aging. In this paper, recent advances in molecular simulations of the aging of polymers and their composites are reviewed. The characteristics and applications of commonly used simulation methods in the study of the aging mechanisms (traditional molecular dynamics simulation, quantum mechanics, and reactive molecular dynamics simulation) are outlined. The current simulation research progress of physical aging, aging under mechanical stress, thermal aging, hydrothermal aging, thermo-oxidative aging, electric aging, aging under high-energy particle impact, and radiation aging is introduced in detail. Finally, the current research status of the aging simulations of polymers and their composites is summarized, and the future development trend has been prospected.

Recently developed enzymes for the depolymerization of polyethylene terephthalate (PET) such as FAST-PETase and LCC-ICCG are inhibited by the intermediate PET product mono(2-hydroxyethyl) terephthalate (MHET). Consequently, the conversion of PET enzymatically into its constituent monomers terephthalic acid (TPA) and ethylene glycol (EG) is inefficient. In this study, a protein scaffold (1TQH) corresponding to a thermophilic carboxylesterase (Est30) was selected from the structural database and redesigned in silico. Among designs, a double variant KL-MHETase (I171K/G130L) with a similar protein melting temperature (67.58 °C) to that of the PET hydrolase FAST-PETase (67.80 °C) exhibited a 67-fold higher activity for MHET hydrolysis than FAST-PETase. A fused dual enzyme system comprising KL-MHETase and FAST-PETase exhibited a 2.6-fold faster PET depolymerization rate than FAST-PETase alone. Synergy increased the yield of TPA by 1.64 fold, and its purity in the released aromatic products reached 99.5%. In large reaction systems with 100 g/L substrate concentrations, the dual enzyme system KL36F achieved over 90% PET depolymerization into monomers, demonstrating its potential applicability in the industrial recycling of PET plastics. Therefore, a dual enzyme system can greatly reduce the reaction and separation cost for sustainable enzymatic PET recycling.

The development of a sensitive, quick-responding, and robust glucose sensor is consistently pursued for use in numerous applications. Here, we propose a new method for preparing a Cu₂O electrode for the electrochemical detection of glucose concentration. The Cu₂O glucose electrode was prepared by in situ electrical oxidation in an alkaline solution, in which Cu₂O nanoparticles were deposited on the electrode surface to form a thin film, followed by the growth of Cu(OH)₂ nanorods or nanotubes. The morphology and electrocatalytic activity of a Cu₂O glucose electrode can be tuned by the current density, reaction time, and NaOH concentration. The results from XRD, SEM, and a Raman spectrum show that the electrode surface was coated with cubic Cu₂O nanoparticles with diameters ranging from 50 to 150 nm. The electrode exhibited a detection limit of 0.0275 mM, a peak sensitivity of 2524.9 μA·cm⁻²·mM⁻¹, and a linear response range from 0.1 to 1 mM. The presence of high concentrations of ascorbic acid, uric acid, dopamine and lactose appeared to have no effects on the detection of glucose, indicating a high specificity and robustness of this electrode.