Tianyuan Liu

Developing rapid detection methods with high sensitivity and real-time capabilities is a critical challenge for monitoring antibiotic pollution. This study presents a flexible surface-enhanced Raman scattering (SERS) sensing substrate (GF@PDDA-Ag/rGO) based on the poly dimethyl diallyl ammonium chloride (PDDA)-bridged assembly for real-time, ultrasensitive sulfasalazine detection. PDDA provided a positively charged polyelectrolyte layer, which directs AgNPs to assemble uniformly into hotspots, while reduced graphene oxide (rGO) enhances charge transfer and concentrates target molecules near these hotspots. The synergistic interaction among PDDA, Ag, and rGO endows this composite substrate with excellent performance. This is the first application of SERS for sulfasalazine detection, achieving a lowest detection concentration as low as 10^-11 M, significantly outperforming other detection methods. No preprocessing or incubation is required, and the measurement time is within 1 s, enabling a highly sensitive response. In addition, this substrate demonstrated excellent uniformity (relative standard deviation, RSD = 2.67%) and long-term stability (>60 days). Furthermore, the addition of the Random Forest Regression (RFR) model for concentration prediction enhances both accuracy and convenience, with an R² value of 0.9852. This efficient detection strategy has significant potential for on-site monitoring of antibiotic pollutants in food and environmental samples.

This study focuses on the light scattering spectral properties of spherical nanoparticles under multilaser excitation and proposes the concept of scattering efficiency factor difference ratio (SEFDR) for high-resolution nanoparticle size measurement. The effects of excitation wavelength, particle size, and scattering angle on the scattering spectra are investigated and simulated. Based on the simulation, the SEFDRs for particles of different sizes under different combinations of excitation wavelengths are simulated. The SEFDR calculation can effectively eliminate noise to achieve high-resolution measurements. In experiments, three lasers with center wavelengths of 380, 785, and 1064 nm are used as excitation light sources to measure particle diameter between 33 and 147 nm; the minimum measurable particle diameter is 33 nm. Using three excitation light sources with center wavelengths between 210 and 628 nm, the minimum measurable particle diameter can be broadened to 20 nm. When the spectrometer signal-to-noise ratio is 1000:1, the resolution of particles with a diameter of 33 nm can reach 0.05 nm under ideal conditions.

This paper proposes a data-driven hierarchical framework for distributionally robust joint chance constrained voltage control in distribution networks (DNs). Disentangled representation learning technology is employed to model the distribution of photovoltaic (PV) power at different buses under given predictions. Benefiting from the disentangled model, the correlations implied in the historical dataset are effectively captured. These include correlations between predicted errors and given predicted values, as well as the correlations between PV power at different buses. The KL-metric ambiguity set for PV power distribution is constructed with the reference distribution introduced by a disentangled conditional decoder. Based on the KL-metric ambiguity set, distributionally robust joint chance constraints are adopted. To transform the distributionally robust joint chance constraint into a tractable form, a reconstruction solution method with KL divergence is presented, and a corresponding acceleration technique for selecting support constraints is developed to reduce computation time while ensuring control effectiveness. The proposed method is validated using distribution networks of different scales.

Polycyclic aromatic hydrocarbons (PAHs) have attracted significant attention due to their severe threats to both ecological systems and human health. In this paper, a high-performance surface-enhanced Raman spectroscopy (SERS) substrate based on NCF/GO/Au@Ag nanocomposites was developed, which enabled sensitive and stable detection of PAHs. The NCF/GO/Au@Ag substrate synergistic utilizes the localized surface plasmon resonance (LSPR) effect of Au@Ag core–shell nanorods and the additional interfacial charge transfer provided by graphene oxide (GO) to exhibit extremely high sensitivity. And the three-dimensional fibrous network of nanocellulose (NCF) improved nanoparticle dispersion uniformity. Combined finite element simulations and experimental studies verified that the dual plasmonic resonances (512 nm and 772 nm) of Au@Ag nanorods optimally match 785 nm excitation, yielding an enhancement factor of 5.21 × 10⁵. GO integration enhanced Raman signals by 1.68-fold through interfacial charge transfer, while the introduction of NCF reduced the signal relative standard deviation (RSD) from 36.88% to 4.29%. The NCF/GO/Au@Ag substrate achieved a detection limit of 10 μg/L for PAHs, demonstrating exceptional sensitivity and reproducibility.