Semiconductor synthetic biology (SemiSynBio) is an emerging field with the aim of exploring distinctive advantages of biological system to tackle computational hard problems. SemiSynBio-Biocomputing solves computational problems using cells or tissue level bio-inspired system or devices. This new pathway explores the potential of biological computing systems and overcomes limits of traditional semiconductor technology, driven by the exponentially growing demand for high-performance, energy-efficient computing resource. This review provides a comprehensive overview of biocomputing at DNA/RNA-level and cell/tissue-level. SemiSynBio-Biocomputing builds an integrated system consisting with physiological processes, internal network interfaces and cross-talks among cells or tissues. The primary objective is to develop design methodologies to utilize cellular-scale networks and their natural communication capabilities and bypass the constrains of traditional algorithmic computation. The biological performance at cell/tissue-level offers a robust pathway to achieve self-organizing, self-repairing, resilient, distributed and adaptive biological computing systems, making it possible to construct a bio-computer with a variety of applications. Future directions focus on exploring the architecture for information processing and storage within biocomputing applications, particularly those with non-Von Neumann configurations. SemiSynBio opens up a new pathway towards biocomputing and is expected to address increasingly complex problems and applications, serving as a foundational platform for next generation bio-computers.

Accurate prediction of the research octane number (RON) is essential for clean fuel design, engine optimization, and gasoline blending, yet experimental measurements remain expensive, and mixture blending behavior is difficult to describe using conventional linear rules. To address this challenge, we developed an interpretable multimodal molecular representation framework based on latent space mixing. Because the reliability of mixture prediction depends on the quality of the pure-component representations, a multimodal encoder was built for pure components by integrating graph neural network embeddings, molecular access system (MACCS) fingerprints, and molecular descriptors selected through a residual-guided strategy. The trained pure-component encoder was then transferred to mixture prediction. For the pure-component test set, the model achieved an R² value of 0.9373 and a mean absolute error (MAE) of 4.04. For the mixtures, the latent space model achieved an R² of 0.9736 and an MAE of 1.46. The ablation results showed that the graph topology provided the strongest contribution, whereas the MACCS fingerprints and descriptors supplied complementary information. Atom-level attention analysis identified a graph-branch emphasis on the local structural environments associated with the RON. Finally, the mixture model was applied to fuel formulation design, where inverse searching under the target RON constraints generated a feasible candidate formulation. These results demonstrated that composition-weighted molecular embedding is a promising strategy for mixture RON prediction and fuel formulation.

The selective hydrogenation of dimethyl oxalate (DMO) to methyl glycolate (MG) is critical for biodegradable polyglycolic acid production, but MG is prone to overhydrogenation to ethylene glycol. Ni₃P/SiO₂ catalysts show promise in selective hydrogenation due to their unique electronic structure and thermal stability. The conventional strategies to enhance the performance of these catalysts rely on maximizing the carrier specific surface area to expose more active sites. In this study, we synthesized three spherical SiO₂ carriers with similar particle diameters but different internal structures to load Ni₃P. Unexpectedly, among the three catalysts, the highest-surface-area Ni₃P/SiO₂-II catalyst exhibited the best Ni₃P dispersion yet the poorest DMO conversion (only 55.4%), as its micro-mesoporous structure compromised the formation of phase-pure Ni₃P and limited the effective utilization of active sites. The lowest-surface-area Ni₃P/SiO₂-III, which lacks sufficient Si-OH anchoring, suffered from severe Ni₃P agglomeration but still achieved 75.4% DMO conversion owing to its mesoporous structure. In contrast, Ni₃P/SiO₂-I, despite its low surface area, achieved excellent performance with 92.2% DMO conversion and 94.8% MG selectivity at 220 °C, benefiting from abundant Si-OH groups and large mesopores. Systematic characterization revealed that the density of surface Si-OH groups and the pore size were the two carrier properties responsible for the observed differences in catalytic activity. Further analysis showed that abundant Si-OH groups suppressed Ni₃P agglomeration via chemical anchoring, while large mesopores enabled sufficient Ni-P precursor contact to ensure pure Ni₃P formation and also enhance DMO accessibility and timely MG diffusion. This combination promoted activity and maintained high MG selectivity by minimizing overhydrogenation. Thus, besides specific surface area, abundant Si-OH groups combined with large mesopores are key factors for Ni₃P catalytic performance in DMO hydrogenation.

Although HZSM-5 zeolite is widely used in catalytic cracking reactions, most relevant studies have not thoroughly examined the initial reaction behavior of n-hexane catalytic cracking, leading to an insufficient understanding of molecular-scale reaction mechanisms. Herein, we conducted precise catalyst tests to establish a molecular-scale reaction kinetic model for n-hexane catalytic cracking. The results showed that isohexane is an important primary product of the reaction. Two reaction networks, with and without n-hexane isomerization to isohexane, were compared, revealing that isohexane directly influences ethylene and propylene formation pathways and indirectly affects substances like isobutane. Results from the kinetic model showed that consideration of the pathway of n-hexane isomerization to isohexane helps reduce model errors. The detailed investigation indicated that the pathway of n-hexane isomerization exerts opposite effects on the formation of ethylene and propylene, and the model conclusions were confirmed by co-feeding experiments, which demonstrated that adding isohexane boosts ethylene selectivity and suppresses propylene formation. The present study deepens the understanding of the n-hexane cracking mechanism and provides a basis for the directional regulation of reaction pathways and the optimization of industrial process conditions.

Methacrylonitrile is an indispensable chemical intermediate in aerospace and wind energy material manufacturing. Currently, the one-step isobutene ammoxidation process is the industrial mainstream for its production, but its drawbacks of high reaction temperature, many by-products and high purification energy consumption have prompted researchers to explore alternatives. Although the two-step ammoxidation process has theoretical advantages, it is still in the experimental stage, with its economic and environmental benefits not systematically evaluated. Under unified conditions (99.5% product purity, 1 t∙h^–1 isobutene feed), this study compared the two processes via techno-economic analysis and life cycle assessment. Results show the two-step process has a higher yield (82.4% vs. 77.3%), while the one-step process is superior in unit cost (1.90 vs. 2.23 USD∙kg_MAN^–1) and life cycle carbon emissions (7.42 vs. 9.10 {\mathrm{t}}_{{\mathrm{C}\mathrm{O}}_2\text{-}\mathrm{e}\mathrm{q}}\cdot {{\mathrm{t}}_{\mathrm{M}\mathrm{A}\mathrm{N}}}^{-1}. Sensitivity analysis indicates yield is key to the two-step process’s competitiveness: each 1% increase reduces cost by 0.07 USD∙kg_MAN^–1 and emissions by 0.37 {\mathrm{t}}_{{\mathrm{C}\mathrm{O}}_2\text{-}\mathrm{e}\mathrm{q}}\cdot {{\mathrm{t}}_{\mathrm{M}\mathrm{A}\mathrm{N}}}^{-1}, and 86.9% yield makes it comparable to the one-step process. Thus, improving yield to over 86.9% via catalyst modification is critical for its industrialization, providing quantitative targets for optimization and a scientific basis for process selection and sustainability assessment.

The majority of energy chemical processes require heat supply to sustain reactions. The conventional heat supply relies mainly on fossil fuel combustion, plagued by high energy consumption and intensive carbon emissions. Advanced heating technologies are urgently demanded for low-carbon transition. Electromagnetic induction heating (EIH), featuring non-contact heating, ultra-fast temperature ramp rate, and high energy efficiency, has emerged as a promising technical pathway for the low-carbon transformation of energy chemical engineering. This paper reviews the research progress of EIH utilization in energy chemical processes, focusing on performance regulation strategies and typical reaction applications of three susceptor categories: carbon-based, macroscopic metal, and magnetic nanoparticle susceptors. Carbon-based susceptors enable reactant-heat source integration for high-temperature carbon-involved endothermic reactions such as calcium carbide synthesis; macroscopic metal susceptors combine excellent machinability with heat-catalysis synergy and support tunable temperature gradients for staged conversion; magnetic nanoparticle susceptors feature high specific heating power and excel in microscale local hot spot intensification for medium-low temperature catalysis. Overall, EIH achieves deep heat-reaction coupling and serves as an effective electrification solution for strongly endothermic processes, with excellent adaptability to intermittent green electricity. Future key research directions include long-life susceptor optimization, multiphysics coupling simulation, and standardized techno-economic evaluation for industrial scale-up.

Metal-organic frameworks-based mixed matrix membranes (MMMs) often face a trade-off between interfacial compatibility and intrinsic pore selectivity. Herein, we report a multi-step crosslinking strategy for constructing UiO-66-AC@PEG by covalently bridging MAH-prefunctionalized UiO-66-NH₂ with polyethylene glycol (PEG), enabling simultaneous interfacial engineering and pore regulation. The resulting crosslinked architecture incorporates abundant amide and ether oxygen moieties, which collectively generate CO₂-philic domains and enhance CO₂ sorption through dipole-quadrupole interactions. Meanwhile, MAH-PEG crosslinking induces pore constriction within UiO-66-NH₂, strengthening size-sieving effects toward CO₂/N₂ separation. Moreover, the flexible long-chain architecture of PEG ensures uniform dispersion of UiO-66-AC@PEG within the Pebax matrix. It also effectively suppresses particle agglomeration, thereby minimizing nonselective interfacial defects. Consequently, the optimized UiO-66-AC@PEG/Pebax membrane achieves a CO₂ permeability of 125.8 Barrer and a CO₂/N₂ selectivity of 81.1, corresponding to increases of 56.6% and 33.5%, respectively, compared with the UiO-66-NH₂/Pebax MMM. This work demonstrates that MAH-mediated PEG tethering provides an effective strategy for simultaneously improving CO₂ affinity, diffusion regulation, and filler-polymer interfacial compatibility in MMMs, offering useful guidance for the design of high-performance CO₂ separation membranes.