The selective conversion of fatty acid methyl esters (FAMEs) into even-numbered paraffins remains a fundamental challenge, since conventional deoxygenation catalysts promote hydrodeoxygenation alongside decarbonylation or decarboxylation pathways, thereby producing both even- and odd-numbered hydrocarbons. Here, we demonstrate that a noble-metal-free Cu–Zn/γ-Al2O3 catalyst, rationally designed to integrate hydrogenation and dehydration functionalities, enables a cascade reaction pathway that selectively produces even-numbered n-paraffins from FAMEs. Under high-pressure hydrogen, methyl laurate is sequentially converted through aldehyde and alcohol intermediates, followed by dehydration to α-olefins and subsequent hydrogenation to paraffins. Detailed product analysis reveals that Cu–Zn sites catalyze efficient hydrogenation, while moderate Lewis acidity of γ-Al2O3 promotes alcohol dehydration without inducing excessive cracking, thereby preserving the carbon backbone. As a result, the Cu[1.5]–Zn[3]/γ-Al2O3 catalyst achieves near-complete conversion with an even-paraffin yield approaching the practical upper limit reported for FAME hydrogenation. Systematic variation of Cu–Zn loading and support textural properties establishes a clear structure–function relationship: increasing surface coverage by Cu–Zn species shifts selectivity toward fatty alcohols, whereas balanced dispersion on alumina favors completion of the dehydration–hydrogenation cascade. The catalyst maintains high performance in shaped form and demonstrates scalability in continuous bench-scale operation using soybean-oil-derived FAMEs. These results identify controlled integration of hydrogenation sites and moderate acidity as a general design principle for producing even-numbered paraffins from renewable lipid feedstocks without noble metals.
Autonomous manufacturing systems require certifiable, auditable safety assurance to transition from simulation to deployment under process drift. Existing approaches treat stabilization, safe learning, deployment gating, and human oversight as separate problems, producing brittle behavior at handoff boundaries. We present a five-stage safety assurance pipeline that couples cold-start stabilization, constrained safe exploration, conformal deployment certification, human oversight certification, and adaptive autonomy-level management through explicit stage contracts. Under the stated assumptions and within this stage-contract formulation, formal analysis suggests that static maturity models incur Ω(K2) misclassification under drift, while dynamic transitions can achieve O(logK) behavior with stage-coupled regret bounded by O(TlogT) and Zeno-free switching. On a paper-mill environment calibrated from industrial logs and the Tennessee Eastman benchmark, the pipeline reduces episode-level safety violations from 4.7% to 0.3%, lowers a simulated-operator NASA-TLX (Raw Task Load Index; RTLX) workload proxy from 52.2 to 34.2, sustains 97.9% conformal coverage under evaluated calibration conditions, and yields a 70.1% end-to-end certified-progression estimate with fallback-governed resilience. The framework extends recent safety assurance methodology for learning-enabled autonomous systems with proxy-based human-oversight evidence grounded in established Human Reliability Analysis (HRA) frameworks and auditable transition records. All evidence is simulation-calibrated and should be interpreted as pre-pilot systems-engineering evidence.
As the global transition toward carbon neutrality and a sustainable chemical economy accelerates, technologies converting biomass- and diverse carbon-source-derived C1–C4 lower alcohols into high-value fuels and chemical feedstocks play a pivotal role. Although conversion technologies for individual alcohols are well established, a unified framework for comparing catalytic valorization pathways across the C1–C4 spectrum has yet to be clearly defined. This review critically examines the impact of the carbon chain length and structural characteristics of feedstock alcohols on reaction mechanisms and catalyst design. For methanol (C1), which lacks C–C bonds, an indirect C–C bond-forming pathway via the Hydrocarbon Pool (HCP) mechanism within zeolite catalysts (e.g., H-ZSM-5, SAPO-34) predominates, where pore architecture governs product selectivity. In contrast, C2+ alcohols inherently possess C–C bonds and undergo direct chain growth via Guerbet condensation or dehydration-oligomerization pathways. Specifically for propanol (C3) and butanol (C4), steric hindrance and isomeric structures (iso- vs. n-) significantly determine pathway selectivity and catalyst deactivation. Furthermore, this review provides an in-depth analysis of universal catalyst deactivation issues, including coke deposition, hydrothermal degradation, and active site poisoning. To mitigate these challenges, advanced catalytic engineering strategies are presented, including hierarchical porous structures, nanocrystallization, precise modulation of active sites, and hybrid catalyst design. Conclusively, this review presents a unified catalytic overview by systematically contrasting the indirect HCP-mediated C1 pathways with the direct chain-growth mechanisms of C2+ alcohols, providing a strategic roadmap for engineering structure-optimized catalysts that overcome universal deactivation barriers in next-generation valorization technologies.
Low-nickel-containing spent catalyst (Low-Ni-SC) is a residual solid obtained after the primary recovery of molybdenum and vanadium from hydroprocessing catalysts used in petrochemical operations. In the secondary recovery process, intrinsic NiO and NiAl2O4 present in Low-Ni-SC were reduced to metallic Ni under high-temperature hydrogen treatment. In this study, acid leaching was employed to recover nickel, achieving a high extraction efficiency of 81.52 %, significantly higher than the 67.16 %p obtained without prior roasting (14.36 %). Subsequently, antisolvent crystallization approach was applied to enhance nickel extraction. During this step, other metal ions such as Al, Si, Ca, V, Fe, and Co were extracted with efficiencies of 90.58 %, 96.74 %, 61.46 %, 99.99 %, 83.04 %, and 90.30 %, respectively. Nickel was also extracted as nickel sulfate hexahydrate (NSH) with an efficiency of up to 11.53 %. To reduce the high sodium ion concentration (34,476 mg/kg) introduced in previous processing steps, repeated precipitation and washing were performed, successfully lowering the Na concentration to 30 mg/kg. This treatment also resulted in a 2.83 times increase in the concentration of nickel. Finally, NSH was recovered with a nickel purity ranging from 99.27 % to 99.54 % via antisolvent crystallization using acetone as the antisolvent.
Practical large-field-of-view (FOV), high-resolution imaging of microfeatures is demonstrated through the geometry-optimized design, precision fabrication, and actual application of reattachable optical lenses and deep learning super-resolution with residual and attention mechanisms. The presented approach to the elastomeric reattachable lens addresses key limitations of metal-based lenses and metalenses, including high manufacturing cost and complexity and material constraints. Ray-tracing simulations are performed to optimize lens curvature and geometry, minimizing aberrations and improving effective focal performance. The polydimethylsiloxane (PDMS) lens, with less than 2
