Ubon Ratchathani University

Post-recycling plastic waste contamination in freshwater ecosystems represents an escalating environmental threat, while algal blooms continue to generate vast quantities of underutilized biomass. Addressing both challenges, this study investigated the co-hydrothermal liquefaction of Chlorella pyrenoidosa with representative post-recycling plastic wastes polypropylene, polyethylene terephthalate, and Nylon-6 as a dual-resource valorization strategy. Experiments were conducted in a 1000 mL high-pressure batch reactor at 350 degrees C for 30 min, with varying biomass-to-plastic feed ratios. Systematic product characterization, including functional group, elemental analysis, Van Krevelen diagrams, and heating value assessment, was employed to elucidate synergistic effects and evaluate product quality. Results revealed that co-processing with polyethylene terephthalate achieved the highest biocrude yield of 71.5%, with an enhanced higher heating value of 35.7 MJ kg-1, surpassing the 62.4% yield from microalgae alone. Nylon-6 blends also improved oil yield to 69.6% while producing aqueous fractions enriched with epsilon-caprolactam, indicating the recovery of valuable nitrogenous monomers. In contrast, PP exhibited limited reactivity toward oil generation but produced carbon-rich biochar with a higher heating value up to 41.4 MJ kg-1, comparable to high-grade solid fuels. Mechanistic analyses confirmed that plastics acted as hydrogen donors, promoting deoxygenation, radical stabilization, and selective depolymerization, thereby improving both liquid and solid fuel fractions. By employing ecologically relevant freshwater feedstocks from Thailand, this work advances beyond prior studies dominated by marine biomass or synthetic surrogates, providing realistic insights into resource integration within polluted inland waters. The co-hydrothermal liquefaction process simultaneously mitigates eutrophication-driven algal blooms and persistent plastic pollution while generating fuels and functional carbon materials, directly contributing to a circular bioeconomy. The demonstrated synergy between biological and synthetic wastes highlights a scalable, catalyst-free route to energy-dense biofuels and multifunctional biochar. These outcomes align strongly with SDG which offer a pragmatic framework for waste-to-energy transition in freshwater-dependent regions.

Electrochemical nitrate reduction reaction (ENR) provides an eco-friendly route to ammonia (NH3) synthesis, positioning it as a viable substitute to the traditional Haber-Bosch method. However, accomplishing high efficiency and selectivity is challenging because of competing hydrogen production and reaction instability. Herein, we present phase-stabilized NiFe2O4@CoFe2O4 (NFCO) core@shell nanocages synthesized via a controlled annealing process using Prussian blue analogs. These analogs are self-assembled from divalent Ni and Co species within a trivalent Fe-CN framework, which serves as a sacrificial template. These nanocages impressively enhanced the ENR to NH3 with a Faradaic efficiency of 95% at-0.4 V vs. RHE via a direct eight-electron N-end reduction pathway. The NiFe2O4 core facilitates rapid charge transfer, while the CoFe2O4 shell boosts NO3-adsorption and stabilizes reaction intermediates, effectively suppressing hydrogen evolution. Theoretical calculations and in situ Raman spectroelectrochemistry unveil ENR mechanisms and possible limiting steps on NFCO sites. Beyond the ENR, integrating NFCO into a Zn-NO3-battery enables simultaneous energy generation and stable NH3 production, demonstrating an open-circuit voltage of 1.4 V and a power density of 1.54 mW cm-2. This approach advances the design of competent, stable catalysts for large-scale, sustainable NH3 generation and NO3-removal, with promising environmental applications.

Accurate and timely flood monitoring is critical for disaster risk reduction, yet operational deep-learning flood mapping in resource-limited regions is constrained by high computational cost and the cloud sensitivity of optical imagery. We present a scalable, weather-independent framework that fuses Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data for training, then deploys a lightweight KD-compressed model for basin-scale monitoring. The workflow comprises three stages: (i) sparse label generation via unsupervised clustering on fused Sentinel-1/Sentinel-2 features with threshold-based confidence filtering, retaining only high-confidence pixels as supervised targets; (ii) Knowledge Distillation (KD) from a PSPNet teacher to compact students, evaluated under redundancy-stratified five-fold cross-validation with spatial autocorrelation control; and (iii) SAR-only deployment using Sentinel-1 VV and VH for all-weather inference. A band-agnostic, single-channel architecture trained on seven spectral bands requires only SAR at inference, decoupling operational monitoring from optical availability. We evaluate the framework over a 14-month wet–dry cycle in the Mun–Chi Basin, northeastern Thailand. KD from PSPNet to PSPMixer ( ∼6.5× compression, 349k parameters) raises IoU_pos from 0.100 to 0.404 while producing spatially coherent flood masks; a 571× -compressed U-NetLight (11.6k parameters) is also evaluated but PSPMixer KD is selected for deployment owing to superior map quality. Focal Tversky Loss consistently outperforms cross-entropy, which causes complete failure in two of five architectures. At basin scale, predicted inundation area tracks gauge water level with Pearson r = 0.858 and a ∼7 -day mean lag explained by floodplain storage and geomorphological routing across trap, pathway, and far-field domains. These results demonstrate that KD-compressed models can faithfully reproduce basin-scale flood dynamics at a fraction of the computational cost of their teachers, offering a practical pathway toward near-real-time, climate-resilient flood monitoring in data-scarce tropical basins. This graphical abstract presents a streamlined workflow for scalable, weather-independent flood monitoring in resource-limited regions, integrating satellite remote sensing, machine learning, and model compression. The left section illustrates the study background, highlighting the challenge of monitoring flood-prone floodplains with limited computational resources. Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery are identified as key data sources, with SAR enabling consistent monitoring under all weather conditions. The central panel depicts the methodology, beginning with the acquisition of SAR and optical datasets, followed by sparse labeling via unsupervised clustering with threshold-based confidence filtering to reduce annotation requirements. A Knowledge Distillation (KD) framework is then employed, transferring predictive capabilities from a high-capacity PSPNet teacher model to a compact PSPMixer student model. This ∼6.5× compression pipeline (349k parameters) raises IoU_pos from 0.100 to 0.404 while producing spatially coherent flood masks, trained with Focal Tversky Loss under a redundancy-stratified five-fold cross-validation protocol. The right panel summarizes the main findings. SAR-only inference enables operational deployment in cloud-covered conditions, producing flood extent maps with strong agreement to hydrological gauge measurements (Pearson r = 0.858 ). The framework captures a ∼7 -day mean lag between peak flood extent and downstream gauge peaks, explained by floodplain storage and geomorphological routing across trap, pathway, and far-field domains. Overall, the visual encapsulates how data fusion, sparse labeling, and knowledge distillation synergize to deliver a cost-effective, reproducible, and operationally viable approach for flood risk monitoring in data- and resource-constrained settings, with potential for broader application in Earth systems and environmental monitoring. PSPNet-to-PSPMixer KD achieves ∼6.5× compression with spatially coherent flood masks Sparse labeling via unsupervised clustering with threshold-based confidence filtering SAR-only inference enables weather-independent flood monitoring Focal Tversky Loss outperforms cross-entropy; CE fails in two of five architectures Basin-scale flood extent tracks gauge water level ( r = 0.858 , ∼7 -day lag)

This study evaluated the antibacterial efficacy of sesame protein hydrolysate with a molecular weight cut-off below 10 kDa (<10SPH) against Escherichia coli and Bacillus cereus, focusing on the impact of food matrix effects (pH, sucrose, and sodium chloride (NaCl)) on their antimicrobial activities. This peptide exhibited much stronger antimicrobial activity against B. cereus (gram-positive) than E. coli (gram-negative), which was mainly attributed to differences in bacterial membrane structure and composition. The thermal resistance of both pathogens was assessed in soymilk with and without <10SPH by determining the D-values at 50, 55, and 60 degrees C. The antimicrobial activity of the peptides was found to be dependent on the surrounding food matrix, including pH, sucrose, and NaCl conditions. In particular, acidic conditions (pH 4) reduced the activity of the antimicrobial peptides against E. coli, whereas high salt concentrations (1-5 %) increased their activity. Thermal inactivation results showed that both E. coli and B. cereus in soymilk were slightly less resistant to heat after <10SPH was added. The calculated z-values were 7.01, 7.09, 7.04, and 7.56 degrees C for E. coli in soymilk, E. coli in <10SPH soy milk, B. cereus in soymilk, and B. cereus in <10SPH soymilk, respectively. The inclusion of <10SPH in soymilk therefore enhanced microbial inactivation, demonstrating its potential as a natural plant-based preservative. These findings provide valuable insights into the impact of food matrix effects on the antimicrobial activity of plant-derived peptides, which may be useful for optimizing their performance in plant-based food products.

Rice bran, a nutrient-rich by-product of rice milling, is an underutilized resource in sustainable crop utilization. This study aimed to investigate the characteristics, total phenolic content, and antioxidant activities of rice bran protein hydrolysates (RBPHs) produced using proteases from Bacillus licheniformis (RBPH-B) and α-chymotrypsin (RBPH-C), along with their protein fractions (F1; >100 kDa, F2; 10–100 kDa, F3; 1–10 kDa, F4; <1 kDa). Molecular weight, color, surface hydrophobicity, secondary structure, total phenolic content, and antioxidant activities of the hydrolysates were assessed. Both enzymatic hydrolysis and ultrafiltration reduced molecular weight and surface hydrophobicity, enhanced lightness, and increased α-helix content. Among all samples, the <1 kDa peptide fraction derived from α-chymotrypsin hydrolysis (RBPH-C-F4) exhibited the strongest antioxidant activity, with the lowest EC50 values for ABTS (0.94 mg/mL) and DPPH (210 µg/mL), as well as the highest inhibition of metal chelating activity (1.35 mmol EDTA/g sample) and linoleic peroxidation (90.62%). Enzymatic hydrolysis enhanced total phenolic content compared with native rice bran protein. These findings highlight the potential of rice bran-derived peptides as antioxidant candidates and indicate that further validation in food systems is required.