Chem & Bio Engineering最新文献

Nonviral gene delivery offers promise for treating age-related macular degeneration (AMD), a major cause of blindness. Genetic modification of retinal pigment epithelium (RPE) cells is a potential therapeutic strategy for AMD. This study presents a multiparametric approach to enhance nonviral transfection of human ARPE-19 cells using linear poly-(ethylenimine) (l-PEI, 25 kDa) as a delivery agent for plasmid DNA (pDNA). The transfection protocol was optimized by adjusting the N/P ratio through nucleic acid concentration, varying polymer density, reducing transfection volume, and minimizing contact time between cells and polyplexes. Under optimized conditions, transfection efficiency (TE) reached 88% with ∼85% viability. A semi-automated method for polyplex formation was developed using a 3D-printed microfluidic system, thereby enabling standardized production. This optimized protocol was successfully adapted to the microfluidic system without compromising TE or viability. This semi-automated approach represents a step toward the scalable and reproducible application of l-PEI-based transfection technologies for future therapeutic use.

Potassium-ion layered transition-metal oxides (K _x TmO₂) have gained widespread attention due to their high theoretical capacity, suitable operating voltage, and simple synthesis method. Nevertheless, the complex phase evolutions during the K⁺ intercalation/extraction process lead to poor cycling stability and rate performance, severely hindering its application in potassium-ion batteries. In this work, we found that, in addition to the temperature and heating rate, the annealing process also played a crucial role in modulating the microstructure of layered materials. Optimal annealing rate effectively helps to improve the K⁺ diffusion dynamics, thereby enhancing the electrochemical performance of the cathode. With an annealing time of 500 min (KMNM-500), the obtained sample exhibited a low-defect crystal structure and simpler phase transition process. Thus, it showed good K⁺ diffusion dynamics and cycling stability with an average capacity loss of only 0.048% per cycle at a current density of 0.5 A g^-1. Our work reveals the mechanism by which the annealing process modulates the microstructure of K _x TmO₂, providing guidance for the development of high-performance layered cathodes.

Catalytic dehydrogenation of a liquid organic hydrogen carrier like methylcyclohexane is considered as a potential solution for hydrogen transportation storage. Hydrogen production via the catalytic dehydrogenation of methylcyclohexane (MCH) was performed over a Pt-based catalyst in a microwave reactor to determine the effective catalyst systems. It was found that Pt/Al₂O₃ displayed good MCH conversion and hydrogen production rates but faced urgent issues including C-C bond cleavage side reactions and undesirable coke deposition. After Sn was introduced as a promoter, we explored the optimal ratio of Sn and its promoter effects. Pt_0.6Sn_0.6/Al₂O₃ showed comparable dehydrogenation activity and less coke compared with single Pt/Al₂O₃. According to the characterization analysis of XPS, H₂-TPR and NH₃-TPD, the Sn promoter induced the formation of the Pt-Sn alloy and masked the medium and strong acidic site of Al₂O₃. Excessive Sn loading enhanced the formation of Sn rich alloy, which explains the reduced catalytic activity and stability.

A semirational engineering of novel bright variants of archaerhodopsin-based genetically encoded voltage indicators (GEVIs) is performed. The corresponding focused protein sequence library was generated using data derived from prior directed evolution experiments, computational modeling, evolutionary information, and a physics-based theoretical framework. The proposed variants were synthesized in Escherichia coli, extracted, and purified. Their absorption spectra, fluorescence quantum yields, extinction coefficients, and pK _a of the retinal protonated Schiff base were evaluated. The brightest variants were also expressed in HEK293T cells, and the voltage-dependence of the fluorescence signal was confirmed. As a result, a series of novel GEVIs with enhanced brightness, fluorescence quantum yield, and red-shifted absorption bands have been developed. The approach proposed in this study is general and can be applied to a wide range of protein engineering problems.

Organisms in nature are skilled engineers, equipped with highly evolved sensory systems that enable the precise perception and discrimination of a wide array of environmental stimuli. Among these, the olfactory system exhibits a strong capability to detect and distinguish tens of thousands of odorants with high sensitivity and selectivity. The comprehensive elucidation of the molecular mechanisms underlying human olfaction has laid a solid foundation for the development of bionic olfactory biosensors, which emulate biological olfaction to achieve advanced chemical sensing. These biosensors have introduced novel analytical strategies across diverse fields such as environmental monitoring, medical diagnostics, food safety, and security. Despite considerable progress, challenges persist, particularly in optimizing operational conditions and enhancing the stability and reproducibility of biological recognition elements. This review not only synthesizes recent advancements in the design and application of bionic olfactory biosensors but also provides a comparative analysis of different biological recognition elements, including whole cells, olfactory receptors, odorant-binding proteins, and synthetic peptides. In addition to reviewing sensor architectures and working principles, we also examine nanomaterial-integrated biosensor platforms, highlighting how functional nanomaterials enhance signal transduction and sensitivity. Finally, key application areas are discussed, and current limitations are critically assessed, along with future perspectives for advancing this promising class of biosensors. Through systematic insights into biological sensing mechanisms, material integration, and application-driven requirements, this review offers an integrated perspective on the design principles and future directions of bionic olfactory sensing.

Antifogging coatings with high durability and spatial controllability are essential for advanced optical applications operating under humid or thermally dynamic environments. Herein, we report the design and fabrication of robust, photo-cross-linkable polysaccharide-based multilayer films composed of methacrylated chitosan (CHI-MA) and methacrylated carboxymethyl cellulose (CMC-MA) prepared via layer-by-layer (LbL) assembly. By introducing methacrylate groups to the polysaccharide backbone, the resulting films exhibit photo-cross-linking capability while preserving antifogging performance. We demonstrate that UV-induced cross-linking significantly enhances the chemical and mechanical stability of the resulting films without altering their optical clarity. Furthermore, spatially defined UV exposure through photomasks enables high-resolution micropatterning, allowing realization of stimuli-responsive visual display under fogging conditions as well as hierarchical humidity-resolved contrast for programmable optical display through multilayered photopatterning. These versatile photopatternable polysaccharide films, combining structural robustness, antifogging performance, and spatial patterning capability, offer promising opportunities for next-generation smart coatings in sensors, displays, and environmental interfaces.

Nanoparticles (NPs) such as engineered inorganic NPs (metals, metal oxides, carbon materials, etc.) can induce cytotoxicity in normal biological systems when used for disease treatment or when exposed to the environment, which has raised widespread concerns about their safety in biomedicine, environmental chemistry, and other application fields. Therefore, developing efficient strategies for the hazard and risk assessment of NPs is extremely important to ensure their safety and sustainable development in above applications. Herein, we provide a systematic and comprehensive review that includes the following sections: (i) mechanisms and influencing factors of nanotoxicity, (ii) the classical statistical cytotoxicity prediction models such as nano-quantitative structure-activity relationship (nanoQSAR), physiologically based pharmacokinetic (PBPK), and meta-analysis (MA) models, (iii) the ML-accelerated development of the above three types of models, and (iv) some important nanotoxicity databases. The ML-accelerated nanoQSAR, PBPK, and MA models are mainly focused, in which the ML algorithms, advantages, and schemes for model development are described, and also the prediction performance and key features that influence the cytotoxicity for the developed models are discussed in detail. In addition, future opportunities and challenges in promoting the development of highly efficient, robust, and interpretable ML models for predicting the cytotoxicity of NPs are also highlighted.

The key challenge in the development of a target property polymer stems from the unclear structure-property relationship (SPR). A multimodal graph neural network (GNN) framework, GeoALBEF, is proposed to address this problem. GeoALBEF introduces a training process based on an "Align before Fuse" architecture that optimizes a three-stage loss, which deeply fuses polymer graph information and text information. Benchmark tests constructed based on 5,000 polymer samples showed that GeoALBEF reduced the mean relative RMSE by 8.6% compared to the suboptimal model in the prediction task of 24 key properties in 6 categories. It is especially worth pointing out that the model achieves functional group level interpretability through the attention mechanism and the functional group averaging strategy. The interpretability was mapped using visualization methods. Additionally, we used reinforcement learning to quantify this interpretability and successfully decoupled the SPR of the polymers. This multimodal system, which combines high-precision prediction capability and mechanism analysis, establishes an intelligent mapping paradigm from polymer structure to property and is expected to promote the accelerated optimization of target property polymers.