Ming-Xi Wang

Predicting the ammonium nitrogen (NH4+-N) adsorption capacity of biochar is challenged by coupled, nonlinear interactions among operational variables, pyrolysis synthesis parameters, and intrinsic material properties. A unified data set comprising 425 literature-reported adsorption records from 18 studies was compiled, and preprocessing (KNN imputation and Z-score standardization) was fitted exclusively on the training subset to prevent data leakage. Among seven machine learning algorithms benchmarked with Bayesian optimization and 5-fold cross-validation, XGBoost achieved the highest test-set coefficient of determination (R2) of 0.901 and root-mean-square error of 0.555 mg g–1. TreeSHAP analysis quantitatively decoupled three driver groups, adsorption operational parameters (53.3%), biochar physicochemical properties (38.2%), and pyrolysis synthesis parameters (8.5%), and identified initial concentration, biochar oxygen content, and adsorbent dosage as the dominant variables, with oxygen content exhibiting a unimodal relationship with adsorption capacity. Partial dependence plots were further utilized to reveal nonlinear response behaviors and determine data-coverage-validated optimal operation and design windows for key variables. A graphical user interface was developed to estimate equilibrium adsorption capacity, reducing reliance on extensive laboratory trials.

Recognizing the gap between traditional verification-oriented undergraduate chemistry experiments and real-world nanomaterial research practice, we designed a comprehensive laboratory experiment integrating nanomaterial synthesis, characterization, and electrochemical sensing for third-year and senior undergraduate students majoring in analytical chemistry, environmental engineering, or materials science. This experiment focuses on addressing the limitations of enzyme-based glucose sensors (e.g., strict environmental dependence, poor stability) by constructing an enzyme-free glucose sensor using copper oxide/porous nitrogen-doped graphene (CuO/PNrGO) nanocomposites. Students sequentially synthesize porous nitrogen-doped graphene (PNrGO) via calcination, prepare CuO/PNrGO through a hydrothermal method, and fabricate the enzyme-free sensor. They characterize the nanomaterials using scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and N2 adsorption–desorption while evaluating the performance of the sensor via cyclic voltammetry (CV) and amperometric i–t curves. The optimized CuO/PNrGO sensor exhibits a high sensitivity of 2020.54 μA·mM–1·cm–2, a low detection limit of 0.13 μM, excellent anti-interference ability, and long-term stability. This experiment bridges foundational chemistry principles (electrochemistry and materials science) with interdisciplinary research, enhancing students’ experimental skills, data analysis capabilities, and scientific literacy.

The porous structure and surface chemistry of graphene aerogels (GAs), critical to their energy storage performance in aqueous supercapacitors, can be effectively modulated by postoxidation washing treatments. In this study, three graphene aerogel samples, namely water-washed graphene aerogel (GA-W), acid–water cowashed graphene aerogel (GA-A/W), and acid-washed graphene aerogel (GA-A), were prepared through hydrothermal assembly of graphene oxide with different washing protocols. Experimental characterizations and density functional theory calculations confirm that residual protons trigger the transformation of carbonyl groups to hydroxyl moieties, accompanied by lattice distortion and pore collapse. Electrochemical measurements show that GA-W with a microporous structure and intact sp2 network achieves a specific capacitance of 319 F g–1 at 1 A g–1 in 6 M KOH electrolyte via dominant electric double-layer capacitance. Benefiting from high mesopore fraction (86.7%) and moderate carbonyl retention, GA-A/W delivers a capacitance of 248 F g–1 at 1 A g–1 in 0.5 M (NH4)2SO4 electrolyte, owing to matched ion transport channels and enhanced proton-coupled electron transfer (PCET) pseudocapacitance. This work elucidates the proton-induced structural degradation mechanism of GAs and proves that washing engineering is a facile and scalable approach to develop electrolyte-adaptive carbon electrodes for aqueous supercapacitors.

The development of bifunctional electrocatalysts for energy-saving hydrogen production coupled with alcohol upgrading is highly desirable yet challenging. Herein, we report Ni nanoparticles encapsulated in N-doped carbon shells (Ni@NC-450) as a bifunctional electrocatalyst for hydrogen evolution reaction (HER) and ethanol oxidation reaction (EOR). The catalyst is derived from pyrolysis of Ni-based zeolitic imidazolate framework (Ni-ZIF) at 450 °C, a temperature that enables effective regulation of nitrogen configurations and affords a high proportion of pyridinic N. The resulting Ni@NC‑450 requires only 85 mV for HER and 1.38 V for EOR to reach 10 mA cm−2 in alkaline media. When employed in ethanol-water electrolyzer, Ni@NC-450 delivers 1.58 V at 10 mA cm−2 (162 mV below conventional water splitting), producing acetate as the major product confirmed by nuclear magnetic resonance. Furthermore, Ni@NC-450 exhibits excellent long‑term stability, retaining its catalytic activity after 1000 cyclic voltammetry cycles and 15 h of continuous operation. Systematic poisoning and acid‑etching experiments reveal that the enhanced bifunctional activity originates from the joint contribution of metallic Ni and pyridinic N. This work provides a rational design strategy for metal-organic framework (MOF)‑derived N‑doped carbon encapsulated Ni catalysts and clarifies the roles of metallic Ni and pyridinic N for hybrid water electrolysis.

The development of non-enzymatic glucose sensors for beverage analysis remains challenging due to insufficient active sites, poor conductivity, and limited stability in complex matrices. A nickel-carbon nanotube composite (Ni/CNT−600) was synthesized via in situ solvothermal deposition followed by pyrolysis at 600 °C under an inert atmosphere. The optimized Ni/CNT−600 featured uniform anchoring of Ni nanoparticles on CNTs through strong Ni–C and Ni–O–C interfacial bonds, validated by various characteristic techniques. The Ni/CNT−600 sensor exhibited exceptional sensitivity (538.48 μA mM⁻¹ cm⁻²) and an ultralow detection limit (0.003 μM) in 0.1 M NaOH at +0.65 V, surpassing many reported metal-based and enzymatic sensors. It demonstrated remarkable selectivity against key interferents (e.g., ascorbic acid, uric acid). In real beverage samples (orange juice, grape juice, cola, green tea, milk), recovery rates ranged from 95.6% to 112.8%. This work demonstrates a well-defined Ni-CNT synergistic interface that contributes to enhanced non-enzymatic glucose sensing performance, effectively addressing matrix complexity in beverages.

Titanium niobium oxide (TNO) has emerged as a promising high-safety anode material for next-generation, high-rate lithium-ion batteries. However, its practical application faces significant challenges due to inherent limitations in electronic conductivity and substantial ion migration resistance, primarily caused by particle overgrowth during conventional solid-phase sintering processes. To address these issues, we developed an innovative two-step sintering strategy to synthesize submicron-sized carbon-coated Ti₂Nb₁₀O_29-x with abundant oxygen vacancies (Ov-TNO@C). This approach features a unique carbon precoating process at 400 °C followed by controlled co-sintering with TiO₂, achieving three critical modifications: 1) carbon layer-mediated particle size control during sintering, 2) oxygen filtration during atomic interdiffusion between nano-TiO₂ and micron-sized Nb₂O₅, and 3) in situ generation of oxygen vacancies through Ti⁴⁺ → Ti³⁺ reduction. The optimized Ov-TNO@C demonstrates remarkable improvements in both electronic (1.6 × 10^-5 S cm^-1) and ionic conductivity, delivering exceptional electrochemical performance: high specific capacity (306 mAh g^-1 at 0.1 C), outstanding rate capability (181 mAh g^-1 at 10 C and 71.4 mAh g^-1 at ultrahigh 100 C), and superior cyclability (92.5% capacity retention after 2000 cycles at 10 C). This work establishes a universal strategy for engineering high-performance metal oxide electrodes through synergistic microstructure control and defect engineering, paving the way for the development of next-generation fast-charging battery systems.

Water quality monitoring in wastewater treatment is crucial due to its significant impact on environmental sustainability and public health. Small wastewater treatment facilities often encounter challenges such as limited resources, lack of advanced monitoring technologies, and difficulties in maintaining effluent quality standards. This study aims to address these challenges by proposing an integrated methodology that employs a particle swarm optimization-based long short-term memory (PSO-LSTM) model in conjunction with a random forest regression (RFR) model for the prediction of ammonia nitrogen (NH₃-N) concentrations. The methodology integrates the strengths of both models to enhance predictive accuracy and stability. The PSO algorithm optimizes hyperparameters of the LSTM, effectively searching the solution space to balance exploration and exploitation. This optimization facilitates the handling of complex non-linear data patterns while improving computational efficiency. The integrated PSO-LSTM-RFR approach demonstrated superior performance on the test set, achieving a coefficient of determination (R²) of 0.9105, a root mean square error (RMSE) of 2.9706, and a mean absolute error (MAE) of 2.5430. The results confirm that the proposed methodology significantly enhances predictive performance due to the complementary advantages of the models. The synergy between RFR and LSTM allows for effective handling of non-linear relationships and sequential data trends, leading to more accurate predictions. The findings underscore the potential of the integrated PSO-LSTM-RFR model to enhance predictive efficacy in wastewater treatment processes, ultimately contributing to more effective water quality management strategies.

The study addresses the critical issue of accurately predicting ammonia nitrogen (NH₃-N) concentration in a sequencing batch reactor (SBR) system, achieving reduced consumption through automatic control technology. NH₃-N concentration serves as a key indicator of treatment efficiency and environmental impact; however, its complex dynamics and the scarcity of measurements pose significant challenges for accurate prediction. To tackle this problem, an innovative Transformer-long short-term memory (Transformer-LSTM) network model was proposed, which effectively integrates the strengths of both Transformer and LSTM architectures. The Transformer component excels at capturing long-range dependencies, while the LSTM component is adept at modeling sequential patterns. The innovation of the proposed methodology resides in the incorporation of dissolved oxygen (DO), electrical conductivity (EC), and oxidation-reduction potential (ORP) as input variables, along with their respective rate of change and cumulative value. This strategic selection of input features enhances the traditional utilization of water quality indicators and offers a more comprehensive dataset for prediction, ultimately improving model accuracy and reliability. Experimental validation on NH₃-N datasets from the SBR system reveals that the proposed model significantly outperforms existing advanced methods in terms of root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). Furthermore, by integrating real-time sensor data with the Transformer-LSTM network and automatic control, substantial improvements in water treatment processes were achieved, resulting in a 26.9% reduction in energy or time consumption compared with traditional fixed processing cycles. This methodology provides an accurate and reliable tool for predicting NH₃-N concentrations, contributing significantly to the sustainability of water treatment and ensuring compliance with emission standards.