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Verified CAS / Academic Author5 Decoded Studies

Prof. XU Kepei

School of Environment and Energy, South China University of Technology

Co-Affiliations:Beijing Technology and Business UniversityTsinghua University

Research Publications & English Decoded Briefs

Showing 5 publications
SCIENCE CHINA Materials2026DOI: 10.1007/s40843-026-4385-1

Homogeneous Dip-Coating of Ion-Modulated Self-Assembled Monolayers for Large-Area Perovskite Photovoltaics

Self-assembled monolayers (SAMs) are effective hole-selective contacts for inverted perovskite solar cells, but scalable deposition on rough substrates is hindered by molecular aggregation, disordered packing, and incomplete adsorption. We propose a hybrid strategy incorporating 4-(Piperidin-4-yl)butanoic acid hydrochloride (PBACl) into the 4PABCz solution during dip-coating. PBACl suppresses aggregation via hydrogen bonding and ionic interactions, yielding homogeneous coverage and improved wettability. The piperidine and carboxyl groups passivate buried interfacial defects through hydrogen bonding and coordination with perovskites. Small-area cells achieve a champion power conversion efficiency (PCE) of 26.09%, while a 5 cm × 5 cm mini-module (aperture area 14.4 cm²) delivers 23.29% PCE. Encapsulated devices retain 80% of initial PCE after 1350 h maximum power point tracking under continuous illumination. This ion modulation strategy bridges molecular-level interface control with scalable processing, offering a pathway to industrially relevant perovskite photovoltaics.

SCIENCE CHINA Materials2026DOI: 10.1007/s40843-025-3623-y

Enhancing efficiency and brightness of deep-blue phosphorescent OLED enabled by a narrowband Pt(II) emitter

Organic light-emitting diodes (OLEDs) are an advanced technology for full-color displays, yet the low efficiency of blue OLEDs remains a critical bottleneck. Here, we report a new strategy to design robust Pt(II) emitters with enhanced molecular rigidity and increased locally excited character. The resulting Pt(II) emitter exhibits an extremely narrow emission spectrum peaking at 458.6 nm with a full-width at half-maximum (FWHM) of 16.0 nm and a small Huang-Rhys factor of 0.278, together with a high photoluminescence quantum efficiency of 95%. When doped into an OLED, the device emits at 464 nm with high color purity (FWHM = 19 nm) and achieves high external quantum efficiencies (EQEs) of 32.6%, 29.4%, and 26.9% at luminances of 123, 1000, and 5000 cd/m2, respectively. Notably, the device attains a record-high maximum brightness of 84,895 cd/m2 among reported deep-blue OLEDs with Commission Internationale de l'Éclairage (CIE) y-coordinate < 0.15. This work demonstrates one of the highest-performing deep-blue OLEDs reported to date, addressing the dual challenges of efficiency and brightness in this spectral region.

Journal of Environmental Engineering Technology2026DOI: 10.13205/j.hjgc.202605005

Application of Machine Learning in Water Quality Prediction and Analysis for River Cross-Sections

Water quality prediction is essential for river basin management, yet existing models often struggle with non-stationary, noisy monitoring data. This study collected water quality data from two city-level control sections in southern China from December 2020 to June 2024, including eight indicators: water temperature, turbidity, pH, conductivity, dissolved oxygen (DO), ammonia nitrogen (NH4+-N), total phosphorus (TP), and permanganate index (CODMn). To predict four key indicators (DO, NH4+-N, TP, CODMn), we developed hybrid models combining seasonal trend decomposition (STD), Bayesian hyperparameter optimization, and either random forest (RF) or XGBoost. STD smoothed and denoised the data while extracting seasonal factors; Bayesian optimization tuned model hyperparameters. Evaluation showed that the STD-Bayesian-XGBoost model achieved smaller bias errors and higher prediction accuracy than STD-Bayesian-RF. Specifically, XGBoost reduced root mean square error (RMSE) by 15-20% across all four indicators and improved the coefficient of determination (R²) to above 0.90, compared to RF's 0.85-0.88. The models were validated on southern river data, but the methodology is generalizable to other climatic and hydrological settings. This work provides a technical reference for pollution reduction and carbon management in regional watersheds.

Journal of Environmental Engineering Technology2026DOI: 10.13205/j.hjgc.202607017

Carbon Emissions Accounting and Techno-Economic Evaluation of Biochar and Organic Fertilizer Production from Distillers' Grains

Distillers' grains, the largest organic solid waste stream in the brewing industry, require efficient low-carbon valorization to support China's Dual Carbon Goals. This study employs life cycle assessment (LCA) to quantify CO2 emissions and carbon reduction benefits of two mainstream routes: pyrolysis to biochar and fermentation to organic fertilizer. Based on public process data, the total life-cycle CO2 emission for biochar production from 1 t of distillers' grains is 250.02 kg, with a carbon sequestration reduction of 120.29 kg, demonstrating superior long-term carbon fixation. In contrast, organic fertilizer production emits 536.14 kg CO2 per ton, achieving a carbon reduction of only 86.22 kg, indicating inferior mitigation performance. Techno-economic analysis reveals net profits of 471.97 CNY/t for biochar and 822.27 CNY/t for organic fertilizer, showing that the organic fertilizer route offers higher profitability. Both pathways effectively reduce CO2 emissions, with biochar prioritizing environmental sustainability and organic fertilizer excelling economically. This study provides data-driven insights for selecting organic solid waste recycling strategies, promoting low-carbon technologies, and establishing circular economy models in the brewing industry.

Journal of Environmental Engineering Technology2026DOI: 10.13205/j.hjgc.202608004

Design and Implementation of an Energy-Saving Onsite Thermal Treatment System for Hazardous Medical Organic Waste Liquid

This paper presents the design and implementation of an energy-saving onsite thermal treatment system for hazardous medical organic waste liquid. The system integrates automatic control with energy-efficient and eco-friendly operation, enabling immediate treatment at the source and reducing storage and transportation volumes by over 95%. It comprises four modules: waste liquid identification, graded thermal treatment, heat recovery, and tail gas purification. The graded thermal treatment technology effectively processes waste liquids with varying compositions and calorific values. On-site experiments were conducted on four typical medical organic waste liquids over one year. Results demonstrated effective treatment meeting safety and environmental requirements, with dioxin concentrations below 0.1 ng-TEQ/m³ and CO, NOx, and SOx emissions within permissible limits. The system offers economic benefits by eliminating long-distance transport and centralized treatment costs. This approach addresses the limitations of traditional long-chain, manual-intensive disposal methods, which pose significant safety and environmental risks.