朝陽科技大學

Addressing the growing global energy demand requires the exploration of alternative and sustainable energy resources. Developing materials through cost-effective and eco-friendly approaches offers a viable pathway toward achieving this goal. Activated carbon-based materials derived from low-cost biomass, owing to their high surface area and porous architecture, have shown significant potential for energy-related applications. In this work, biomass derived from nut grass was activated using three different ratios of KMnO4 and subsequently pyrolyzed at two temperatures (700 and 800 °C). The resulting samples were structurally and morphologically characterized using XRD, BET, Raman, XPS, and FESEM analyses. The results indicate that KMnO4 functions both as an activator and as a catalyst, promoting high surface area, partial graphitization, and enhanced electrical conductivity. The sample activated with double the proportion of KMnO4 and pyrolyzed at 800 °C exhibited the highest surface area (∼1010.28 m2 g−1) and a high defect-to-graphitic ratio in Raman analysis. Furthermore, this sample demonstrated excellent hydrogen evolution reaction (HER) activity, achieving an overpotential of 73.1 mV at 10 mA cm−2, and served effectively as a counter electrode in dye-sensitized solar cells, yielding a power conversion efficiency of 7.3%. Overall, this study presents a cost-effective and sustainable approach for synthesizing activated carbon materials with promising potential as electrocatalysts and counter electrodes for renewable energy applications.

Background: Dynamic binding capacity at 5% breakthrough (DBC5%) is a key performance parameter in packed-bed chromatography because it determines the effective working capacity of the adsorbent and directly influences process productivity under dynamic-flow conditions. Methods: This study presents a systematic methodology for the predictive optimization and scale-up of DBC5% for C-phycocyanin (CPC) purification from Spirulina platensis using a sequential design of experiments (DoE) approach. A 2 degrees full factorial design with two center points (2 degrees + 2) was first employed to identify the significant operating variables, followed by a central composite design (CCD) to evaluate potential nonlinear responses. Significant findings: The 2 degrees + 2 factorial model exhibited superior predictive performance, achieving an R2 of 98.97% and a predicted R2 of 94.82%, compared with corresponding values of 91.04% and 48.50%, respectively, for the CCD model. The optimized operating conditions (pH 6.0, 10% (w/v) feed concentration, 1.6 cm bed height, and a flow rate of 10.0 mL/min) yielded a predicted DBC5% of 10.51 mg/mL, which was experimentally validated by an observed value of 10.45 mg/mL, corresponding to a relative error of 2.9%. Furthermore, scale-up from 1.6 cm to 5.0 cm internal-diameter columns while maintaining hydrodynamic similarity successfully preserved DBC5%, demonstrating consistent adsorption performance across the investigated scales. Although the developed regression model is specific to the chromatographic system investigated, the proposed DoE-based optimization framework and hydrodynamic scale-up strategy provide a practical methodology that can be applied to other packed-bed chromatography systems following appropriate experimental calibration and validation.

Bioethanol production from lignocellulosic biomass offers a sustainable alternative to fossil fuels, mitigating environmental challenges such as climate change and air pollution. Traditional pretreatment methods involving acid and alkali treatments pose risks due to wastewater discharge. This study investigates the use of Deep Eutectic Solvent (DES), specifically choline-chloride-based DES, for the pretreatment of pistachio shell biomass. The optimal pretreatment conditions were determined, and the pretreated biomass was characterized and compared to its untreated counterpart. XRD analysis revealed an increase in crystallinity index (CrI%) to around 27–28% post-pretreatment, compared to 21% in untreated biomass. For production of bioethanol, the samples were subjected to Simultaneous Saccharification and Fermentation (SSF) and Separate Hydrolysis and Fermentation (SHF) using Aspergillus niger cellulase and Saccharomyces cerevisiae. The enzymatic hydrolysis of 1:2 Cch/CA 2h-pretreated biomass produced the highest reducing sugar concentration, about 24 g/L.The ethanol concentrations obtained from the sample pretreated with 1:2 Cch/CA for 2 h were 28.60 g/L (SHF) and 29.82 g/L (SSF) after 72 h of fermentation. This was more than the ethanol obtained from the untreated sample (22 g/L). This study demonstrates the promising potential of DES pretreatment for efficient bioethanol production from lignocellulosic biomass.

Background Eggshell (ES), an abundant CaCO₃-rich biowaste, was valorized as a support for bovine serum albumin–polyethyleneimine (BSA–PEI) functionalization to develop a sustainable adsorbent for direct air capture (DAC) of CO₂. This study investigated the effects of surface functionalization and humidity on CO₂ adsorption behavior. Methods BSA and PEI immobilization, physicochemical characterization, gravimetric water uptake, fixed-bed CO₂ adsorption, nonlinear kinetic modeling, and adsorption–desorption cycling were evaluated. The optimized ES–BSA2–PEI10% adsorbent exhibited BSA and PEI immobilization densities of 8.15 ± 0.31 and 80.0 ± 6.0 mg/g ES, respectively, with a nitrogen content of 2.47 wt%. At 420 ppm CO₂ and 25 °C, the adsorption capacity reached 0.2697 mg/g, a 9.82-fold increase over unmodified ES. The Avrami model provided the best statistical description of the adsorption kinetics. Significant Findings Water uptake increased from 0.50% at 15% RH to 8.36% at 82% RH, accompanied by an increase in CO₂ adsorption capacity from 0.0403 to 0.2697 mg/g. These results support a strong association between humidity-dependent hydration and CO₂ adsorption behavior in the BSA–PEI layer. Under alternating wet/dry cycling, the adsorbent retained 94.6% of its initial CO₂ adsorption capacity after ten cycles, indicating good short-term cyclic stability and regenerability under the investigated conditions. Despite its modest absolute CO₂ adsorption capacity, these findings support the feasibility of a waste-derived, humidity-responsive adsorbent platform and highlight humidity history as an important operational consideration for repeated DAC operation.

The growing demand for clean and sustainable energy has accelerated the development of efficient electrocatalysts for water splitting and hydrogen production. The bimetallic metal-organic framework (BM-MOF) has emerged as promising electrocatalysts due to its tunable composition, synergistic bimetallic interactions, high surface area, abundant active sites, and structural versatility. Furthermore, materials derived from BM-MOFs such as metal phosphides, sulfurides, oxides, and metallic nanoparticles had excellent electrical conductivity, catalytic activity, and durability properties, which excellent properties make them attractive for the oxygen evolution reaction (OER), hydrogen evolution reaction (HER), and overall water splitting (OWS). This review comprehensively summarises recent progress (2020-2026) in the design, synthesis, and electrocatalytic applications of BM-MOF and its derived composites for OER, HER, and OWS. Various fabrication methods, including solvothermal/hydrothermal, co-precipitation, and post-synthetic modifications, are discussed. The performance of BM-MOF supported on conductive substrates such as nickel foam, graphene oxide, and carbon nanotubes is critically examined. Importantly, this review provides an updated and focused perspective on recent advances in BM-MOFs and BM-MOF-derived composite electrocatalysts for OWS, with particular emphasis on developments reported during 2020–2026. This review systematically discusses catalyst design, synthesis strategies, structure–performance relationships, and electrocatalytic performance, while highlighting emerging trends and future research opportunities. Finally, future opportunities in green synthesis, scalable production, and AI-assisted catalyst design for sustainable hydrogen production are highlighted.