Ziyan Wu

All Publications

• Microplastics Undergo Fragmentation during PressurizedMembrane Filtration ENVIRONMENTAL SCIENCE & TECHNOLOGY LETTERS Wang, Z., Wu, Z., Janssen, S. E., Harrington, G. W., Wei, H., Qin, M. 2026

  View details for DOI 10.1021/acs.estlett.6c00550

  View details for Web of Science ID 001848758700001

• Degradation Kinetics for Organic Nitrogen in Bioelectrochemical Systems toward Ammonia Recovery ACS ES&T ENGINEERING Burns, M., Wu, Z., Mirsha, T., Beaudet, A., Mangus, K., Qin, M. 2026; 6 (4): 1369-1381

  Abstract

  Land application of dairy manure returns nitrogen (N) to the soil for crop production. However, direct land application of manure faces challenges such as nitrogen volatilization and imprecise manure nitrogen applications, which significantly contribute to losses to the environment while also reducing the nitrogen value of manure. Manure processing methods that can recover nitrogen, particularly organic nitrogen (orgN) in a mineralized form, in a concentrated product can increase the nutrient use efficiency, reducing the demand for manufactured nitrogen fertilizers. In this study, we investigate two operation configurations of bioelectrochemical systems (BES) for ammonia (NH3) recovery from orgN in synthetic dairy manure. Glutamic acid, an amino acid found in high concentrations in dairy manure, was used as the N source in the synthetic feed, and the BES was operated in both microbial electrolysis cell (MEC, E appl. = 0.8 V) and microbial fuel cell (MFC) operation modes. Samples from four time series experiments, two in each operation mode, were analyzed for chemical oxygen demand (COD), total nitrogen (TN), total ammoniacal nitrogen (TAN), and acetate concentrations. Raman spectroscopy was applied to track the orgN content in the time series samples throughout the experiments. Results indicated superior N removal from the anolyte in MEC mode, with an average TN removal above 95% and first-order degradation kinetics with rate coefficients between 0.05 and 0.06 h-1. Kinetic analysis of the Raman data revealed that glutamic acid degradation to be complex and not singularly ordered in either operation mode, requiring further quantitative study. This work provides vital insight into the kinetics of degradation within BES toward a more complete understanding of anode-chamber processes. Such insight can be useful in guiding further research into BES as resource recovery mechanisms and supporting BES adaptation as manure treatment processes focused on the recovery of nutrient-rich, value-added fertilizer products.

  View details for DOI 10.1021/acsestengg.6c00128

  View details for Web of Science ID 001730229700001

  View details for PubMedID 41987811

  View details for PubMedCentralID PMC13077639

• A Two-Step Artificial Intelligence Framework for Discriminating and Quantifying Mixed Pollutants ACS ES&T WATER Wu, Z., Li, C., Peng, P., Qin, M., Wei, H. 2026; 6 (2): 1317-1323

  View details for DOI 10.1021/acsestwater.5c01344

  View details for Web of Science ID 001651837100001

• Regenerable Membrane Sensors for Ultrasensitive Nanoplastic Quantification Enabled by A Data-driven Raman Spectral Processing Algorithm ENVIRONMENTAL SCIENCE & TECHNOLOGY Wu, Z., Janssen, S. E., Tate, M. T., Qin, M., Wei, H. 2025; 59 (31): 16652-16661

  Abstract

  The detection of nanoplastics (NPs) in complex natural water systems is hindered by matrix interferences and limitations in current analytical techniques. This study presents Pre_seg, a Raman spectral processing algorithm integrated with regenerable anodic aluminum oxide (AAO) membrane sensors, for ultrasensitive, rapid, and quantitative NP detection at the single-particle level. The AAO membranes function as both filtration substrates and Raman sensors, reducing sample loss and contamination. Pre_seg incorporates statistically determined thresholds for signal-to-noise ratios (SNRs) and full width at half maximums (fwhms) across segmented spectral ranges, effectively minimizing noise and enhancing accuracy and sensitivity of NP detection. Pre_seg achieved 93.5% prediction accuracy of NPs and ≥90.4% rejection accuracy for non-NP entries. Mixed NPs were quantified at the lowest concentration of 0.5 μg L-1. The robustness of Pre_seg was validated in eutrophic and oligotrophic lake matrices following oxidation digestion pretreatment to mitigate organic interferences. Furthermore, the AAO membrane sensors demonstrated stability through multiple regeneration and reuse cycles. This innovative approach advances NP detection by enabling scalable, customizable, and environmentally relevant monitoring.

  View details for DOI 10.1021/acs.est.5c05396

  View details for Web of Science ID 001539134400001

  View details for PubMedID 40728145

  View details for PubMedCentralID PMC12503354

• Adaptable Plasmonic Membrane Sensors for Fast and Reliable Detection of Trace Low-Micrometer Microplastics in Lake Water ENVIRONMENTAL SCIENCE & TECHNOLOGY Wu, Z., Janssen, S. E., Tate, M. T., Wei, H., Qin, M. 2024; 58 (45): 20172-20180

  Abstract

  In freshwater environments, low-micrometer microplastics (LMMPs) have captured significant attention due to their prevalence and toxicity. Yet, rapid detection of LMMPs (1-10 μm) at the single-particle level within complex freshwater matrices remains a hurdle. We developed an adaptable plasmonic membrane sensor for fast detection of individual LMMPs in eutrophic lake waters. The plasmonic membrane sensor functions both as a membrane filter and as a sensor for LMMP collection and analysis. Among the four types of membrane sensors, polycarbonate track-etch (PCTE) membrane sensors exhibit superior imaging quality for LMMPs due to their flat and homogeneous surfaces. Besides the significantly improved imaging contrast and reduced background interferences, the Raman intensity of LMMPs is enhanced by 48% ± 25% on PCTE membrane sensors compared to unmodified membranes. The increased Raman intensities of a chemical probe with an increasing gold layer thickness and a decreasing membrane pore size suggest a surface-enhanced Raman scattering effect from the membrane sensors. The membrane sensors achieve a detection limit of 1 μg/L and an ultrafast scanning time of 0.01 s for individual LMMPs across natural eutrophic lake water. The developed membrane sensors offer an adaptable tool for the swift and reliable detection of individual LMMPs in complex environmental matrices.

  View details for DOI 10.1021/acs.est.4c06503

  View details for Web of Science ID 001346642100001

  View details for PubMedID 39471153

  View details for PubMedCentralID PMC11562713

• Improved Reliability of Raman Spectroscopic Imaging of Low-Micrometer Microplastic Mixtures in Lake Water by Fractionated Membrane Filtration ACS ES&T WATER Wu, Z., Qin, M., Wei, H. 2023; 3 (8): 2616-2626

  View details for DOI 10.1021/acsestwater.3c00141

  View details for Web of Science ID 001006184400001

• Laboratory Filter Membranes May Release Organic Particles That Affect Water Analysis ACS ES&T ENGINEERING Wu, Z., Cai, S., Cho, S., Wei, H., Qin, M. 2022; 2 (12): 2311-2316

  View details for DOI 10.1021/acsestengg.2c00235

  View details for Web of Science ID 000861306000001