Adsorption of antibiotics by bentonite-chitosan composite: Phenomenological modeling and physical investigation of the adsorption process

Investigation of the photoelectric properties of nanostructured CeO2/Cu2O heterojunction: Photocatalytic degradation of sulfadiazine in water

The rapid complexation of photogenerated electrons-holes with copper (Cu) greatly limits the large-scale application of cuprous oxide (Cu2O) as a photocatalyst. Therefore, using a hydrothermal method, a type Ⅱ heterojunction structure was constructed by modifying Cu2O with cerium (IV) oxide (CeO2). The CeO2/Cu2O heterojunction photocatalyst effectively increased the photogenerated electron density and reduced the surface transfer impedance. The improved separation of photogenerated electron-hole pairs resulted in excellent photocatalytic activity. Consequently, the sulfadiazine (SDZ) degradation rate by CeO2/Cu2O reached 87.5%. Furthermore, after five cycles, the SDZ degradation rate remained as high as 78.5%, demonstrating the good stability of CeO2/Cu2O. The SDZ degradation intermediates were analyzed using high-performance liquid chromatography-tandem mass spectrometry, and possible degradation pathways were proposed. Trapping agent experiments, and energy band structure calculations revealed that CeO2/Cu2O photocatalyzes SDZ degradation via a type Ⅱ heterojunction charge transfer mechanism. Finally, the total organic carbon showed that SDZ eventually decomposed to CO2 and H2O, with complete SDZ degradation. This study provides a reference for the preparation of visible light-responsive photocatalysts.

Efficient removal of aqueous ciprofloxacin antibiotic by ZnO/CuO-bentonite composites synthesized via carbon-bed pyrolysis of bentonite and metal co-precipitation

Antibiotics are of emerging concern due to their widespread use, lack of adequate treatment, and for their potential to threaten human health and the environment. Here, a facile fabrication approach for synthesizing ZnO/CuO-bentonite composites was investigated via carbon-bed pyrolysis of bentonite followed by ZnO/CuO co-precipitation. Sorbents were synthesized using a range of bentonite pyrolysis temperatures, metal oxide contents, and ZnO:CuO mass ratios. The ZnO/CuO-bentonite composites exhibited diverse functional groups, excellent mesoporosity, and high specific surface area (135.0 m2 g-1, four times that of pyrolyzed bentonite-only control). Ciprofloxacin removal was maximized at a bentonite pyrolysis temperature of 450 °C, a total metal oxide content of 25 %, and a Zn/Cu ratio between 95:5 and 93:7, and this material had an observed experimental CIP adsorption of 451 mg g-1 and a calculated maximum adsorption capacity of 1249.3 mg g-1. This excellent CIP sorption ability was attributed to its abundant surface active sites and multiple sorption mechanisms, including hydrogen-bond interaction, ion exchange, and electrostatic interaction. These results illustrate that ZnO/CuO-bentonite composite sorbents have excellent potential for use in environmental remediation and water treatment applications.

Highly Efficient Adsorption of Norfloxacin by Low-Cost Biochar: Performance, Mechanisms, and Machine Learning-Assisted Understanding

This study employed potassium carbonate (K2CO3) activation using ball milling in conjunction with pyrolysis to produce biochar from one traditional Chinese herbal medicine Atropa belladonna L. (ABL) residue. The resulting biochar KBC800 was found to possess a high specific surface area (S BET = 1638 m2/g) and pore volume (1.07 cm3/g), making it effective for removing norfloxacin (NOR) from wastewater. Batch adsorption tests confirmed its effectiveness in eliminating NOR, along with its excellent resistance to interference from impurity ions or antibiotics. Notably, the maximum experimental NOR adsorption capacity on KBC800 was 666.2 mg/g at 328 K, surpassing those of other biochar materials reported. The spontaneous and endothermic adsorption of NOR on KBC800 could be better suited to the Sips model. Additionally, KBC800 adsorbs NOR mainly by pore filling, with electrostatic attraction, π-π EDA interactions, and hydrogen bonds also contributing significantly. The machine learning model revealed that NOR adsorption on the biochar was significantly affected by the initial concentration, followed by S BET and average pore size. Based on the random forest model, it is demonstrated that biochar is able to adsorb NOR effectively. It is noteworthy that the use of low-cost pharmaceutical wastes to produce adsorbents for emerging contaminants such as antibiotics could have greater potential for future practical applications under the ongoing dual carbon policy.

Machine learning and statistical physics modeling of tetracycline adsorption using activated carbon derived from Cynometra ramiflora fruit biomass

The current investigation reports the usage of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN), the two recognized machine learning techniques in modelling tetracycline (TC) adsorption onto Cynometra ramiflora fruit biomass derived activated carbon (AC). Many characterization methods utilized, confirmed the porous structure of synthesized AC. ANN and ANFIS models utilized pH, dose, initial TC concentration, mixing speed, time duration, and temperature as input parameters, whereas TC removal percentage was designated as the output parameter. The optimized configuration for the ANN model was determined as 6-8-1, while the ANFIS model employed trimf input and linear output membership functions. The obtained results showed a strong correlation, indicated by high R2 values (ANNR2: 0.9939 & ANFISR2: 0.9906) and low RMSE values (ANNRMSE: 0.0393 & ANFISRMSE: 0.0503). Apart from traditional isotherms, the dataset was fitted to statistical physics models wherein, the double-layer with a single energy satisfactorily explained the physisorption mechanism of TC adsorption. The sorption energy was 21.06 kJ/mol, and the number of TC moieties bound per site (n) was found to be 0.42, conclusive of parallel binding of TC molecules to the adsorbent surface. The adsorption capacity at saturation (Qsat) was estimated to be 466.86 mg/g - appreciably more than previously reported values. These findings collectively demonstrate that the AC derived from C. ramiflora fruit holds great potential for efficient removal of TC from a given system, and machine learning approaches can effectively model the adsorption processes.

Comparison of modeling, optimization, and prediction of important parameters in the adsorption of cefixime onto sol-gel derived carbon aerogel and modified with nickel using ANN, RSM, GA, and SOLVER methods

Today, the main goal of many researchers is the use of high-performance, economically and industrially justified materials, as well as recyclable materials in removing organic and dangerous pollutants. For this purpose, sol-gel derived carbon aerogel modified with nickel (SGCAN) was used to remove Cefixime from aqueous solutions. The influence of important parameters in the cefixime adsorption onto SGCAN was modeled and optimized using artificial neural network (ANN), response surface methodology (RSM), genetic algorithm (GA), and SOLVER methods. R software was applied for this purpose. The design range of the runs for a time was in the range of 5 min-70 min, concentration in the range of 5 mg L-1 to 40 mg L-1, amount of adsorbent in the range of 0.05 g L-1 to 0.15 g L-1, and pH in the range of 2.0-11. The results showed that the ANN model due to lower Mean Squared Error (MSE), Sum of Squared Errors (SSE), and Root Mean Squared Error (RMSE) values and also higher R2 is a superior model than RSM. Also, due to the superiority of ANN over the RSM model, the optimum results were calculated based on GA. Based on GA, the highest Cefixime adsorption onto SGCAN was obtained in pH, 5.98; reaction time, 58.15 min; initial Cefixime concentration, 15.26 mg L-1; and adsorbent dosage, 0.11 g L-1. The maximum adsorption capacity of Cefixime onto SGCAN was determined to be 52 mg g-1. It was found the pseudo-second-order model has a better fit with the presented data.