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)

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains a leading cause of infectious diseaserelated mortality worldwide, with latent tuberculosis infection (LTBI) presenting a major diagnostic challenge. Heat shock protein 16.3 (Hsp16.3), a latency-associated antigen strongly expressed during dormancy, has emerged as a promising biomarker for LTBI detection. However, conventional diagnostic methods are costly, complex, and infrastructure-dependent, underscoring the need for portable and reagent-free biosensing solutions. Here, we report a contactless biosensing platform based on the giant magnetoimpedance (GMI) effect for the detection of Hsp16.3. The system integrates a commercial pico-Tesla resolution amorphous wire sensor with an Arduino-based microcontroller and MCP3223 analog-to-digital converter. Detection relies on binding-induced magnetic field perturbations generated by antibody- functionalized iron-oxide nanoparticles, and antibodyantigen complexes, which modulate the local magnetic fields and induce measurable impedance changes. The biosensor achieved reproducible detection of Hsp16.3 in model assays, with limits of detection of similar to 99 mu g/mL for antibody titration and similar to 44 mu g/mL for antigen response. More importantly, the platform was successfully validated with plasma samples from LTBI patients, demonstrating specific responses to antibody-antigen complexes in complex biological matrices. This work represents the first demonstration of a GMI-biosensor validated with LTBI plasma samples, highlighting its potential as a portable, scalable, and reagent-free diagnostic tool for future development toward early TB screening in resource-limited settings.

Novel fluorescence-based molecularly imprinted polymer nanoparticles (MIP NPs) were prepared by incorporating fluorescent thionine within the polymer matrix. Thionine functioned both as a functional monomer and as an internal signaling probe, enabling a direct fluorescence readout without the need for external fluorescent labels. MIP NPs served as the selective recognition element for vancomycin (VA) assessment. VA bound to the selective cavities of the MIP NPs and quenched the fluorescence via static quenching mechanism. The fluorescence intensity decreased with increasing concentrations of VA. For MIP NPs-based fluorescence spectrometry development, 1 mg/mL MIP NPs dispersed in DI water completely interacted with the VA-containing sample. The assay exhibited a linear response ranging from 1.55 to 100 mg/L (LOD = 0.46 mg/L), enabling highly sensitive measurement of low VA levels in serum. The recovery was 97–115