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Conversion of seaweed waste to biochar for the removal of heavy metal ions from aqueous solution: A sustainable method to address eutrophication problem in water bodies

Ravindiran G, Rajamanickam S, Ramalingam M, Hayder G, Sathaiah BK, Gaddam MKR, et al.

Environ Res, 2024 Jan 15;241:117551.
PMID: 37939801 DOI: 10.1016/j.envres.2023.117551

The present study investigated the sustainable approach for wastewater treatment using waste algal blooms. The current study investigated the removal of toxic metals namely chromium (Cr), nickel (Ni), and zinc (Zn) from aqueous solutions in batch and column studies using biochar produced by the marine algae Ulva reticulata. SEM/EDX, FTIR, and XRD were used to examine the adsorbents' properties and stability. The removal efficiency of toxic metals in batch operations was investigated by varying the parameters, which included pH, biochar dose, initial metal ion concentration, and contact time. Similarly, in the column study, the removal efficiency of heavy metal ions was investigated by varying bed height, flow rate, and initial metal ion concentration. Response Surface Methodology (Central Composite Design (CCD)) was used to confirm the linearity between the observed and estimated values of the adsorption quantity. The packed bed column demonstrated successful removal rates of 90.38% for Cr, 91.23% for Ni, and 89.92% for Zn heavy metals from aqueous solutions, under a controlled environment. The breakthrough analysis also shows that the Thomas and Adams-Bohart models best fit the regression values, allowing prior breakthroughs in the packed bed column to be predicted. Desorption studies were conducted to understand sorption and elution during different regeneration cycles. Adding 0.3 N sulfuric acid over 40 min resulted in the highest desorption rate of the column and adsorbent used for all three metal ions.
Fulltext Fabrication and performance analysis of ZnO/Ge/porous Si photodetectors for near-infrared detection

Qasim Mogdad MM, Rahman AA, Ahmed NM, Rajamanickam S, Almessiere MA

Heliyon, 2025 Feb 28;11(4):e42426.
PMID: 40034303 DOI: 10.1016/j.heliyon.2025.e42426

This paper reports on the fabrication of zinc oxide (ZnO)/germanium nanoparticles (Ge NPs)/porous silicon (PSi) photodetector for near-infrared (NIR) detection. Ge NPs are synthesized via pulsed laser ablation in liquid (PLAL) followed by spray coating onto the porous Si substrate and subsequent deposition of a ZnO layer. Field emission scanning electron microscopy (FESEM) confirms the presence of Ge NPs, along with the formation of Ge microwires and a mesh-like Ge pattern on the porous Si surface, attributed to Ge NP supersaturation during spray coating. Ge NPs act as a source of photogenerated electrons, transferring them to the ZnO layer. Additionally, the Ge microwire network facilitates barrier-dominated conduction, further contributing to the generation and transfer of photogenerated electrons. The device achieves its best performance at a bias voltage of 6 V under illumination with 805 nm light, a light intensity of 1.44 mW cm2, and a switching frequency of 6.5 Hz and responsivity of 0.16 A W⁻1.
Ensemble stacking of machine learning models for air quality prediction for Hyderabad city in India

Ravindiran G, Karthick K, Rajamanickam S, Datta D, Das B, Shyamala G, et al.

iScience, 2025 Feb 21;28(2):111894.
PMID: 40051831 DOI: 10.1016/j.isci.2025.111894

Hyderabad, one of the rapidly developing cities in India, is facing with severe air pollution due to rapid urbanization, industrial operations, and climatic factors. To alleviate the negative impact on human health and the environment, accurate monitoring and forecasting of air quality are essential. This research utilized various machine learning models, such as XGBoost, LarsCV, Bayesian Ridge, AdaBoost, and ensemble stacking methods, to forecast the air quality index (AQI) using data from August 2016 to October 2023, which included 18 different air pollutants, including meteorological parameters. The ensemble stacking method showed excellent performance, attaining high training (R2 = 0.994) and validation (R2 = 0.999) accuracy with low error metrics (mean absolute error [MAE] = 0.496, mean square error [MSE] = 0.429, root-mean-square error [RMSE] = 0.655). These results highlight the efficacy of ensemble stacking for AQI prediction, providing crucial information for policymakers to formulate strategies to reduce air pollution's effects on public health and environmental sustainability.
Impact of air pollutants on climate change and prediction of air quality index using machine learning models

Ravindiran G, Rajamanickam S, Kanagarathinam K, Hayder G, Janardhan G, Arunkumar P, et al.

Environ Res, 2023 Dec 15;239(Pt 1):117354.
PMID: 37821071 DOI: 10.1016/j.envres.2023.117354

The impact of air pollution in Chennai metropolitan city, a southern Indian coastal city was examined to predict the Air Quality Index (AQI). Regular monitoring and prediction of the Air Quality Index (AQI) are critical for combating air pollution. The current study created machine learning models such as XGBoost, Random Forest, BaggingRegressor, and LGBMRegressor for the prediction of the AQI using the historical data available from 2017 to 2022. According to historical data, the AQI is highest in January, with a mean value of 104.6 g/gm, and the lowest in August, with a mean AQI value of 63.87 g/gm. Particulate matter, gaseous pollutants, and meteorological parameters were used to predict AQI, and the heat map generated showed that of all the parameters, PM2.5 has the greatest impact on AQI, with a value of 0.91. The log transformation method is used to normalize datasets and determine skewness and kurtosis. The XGBoost model demonstrated strong performance, achieving an R2 (correlation coefficient) of 0.9935, a mean absolute error (MAE) of 0.02, a mean square error (MSE) of 0.001, and a root mean square error (RMSE) of 0.04. In comparison, the LightGBM model's prediction was less effective, as it attained an R2 of 0.9748. According to the study, the AQI in Chennai has been increasing over the last two years, and if the same conditions persist, the city's air pollution will worsen in the future. Furthermore, accurate future air quality level predictions can be made using historical data and advanced machine learning algorithms.
Fulltext Quality Control of Fetal Wharton's Jelly Mesenchymal Stem Cells-Derived Small Extracellular Vesicles

Krishnan I, Ng CY, Kee LT, Ng MH, Law JX, Thangarajah T, et al.

Int J Nanomedicine, 2025;20:1807-1820.
PMID: 39963415 DOI: 10.2147/IJN.S497586

BACKGROUND: Quality control (QC) is an important element in ensuring drug substances' safety, efficacy, and quality. The dosing regimen for sEVs can be in the form of protein concentration or the number of particles based on the results of a series of quality controls applied as in-process control.
METHODS: Wharton's Jelly Mesenchymal Stem Cells (WJMSCs) were isolated from four independent umbilical cord samples and were characterized following the International Society for Cellular Therapy (ISCT) guidelines. Small extracellular vesicles (sEVs) were isolated separately from these four WJMSCs samples using the Tangential Flow Filtration (TFF) method and were characterized per Minimal Information for Studies of Extracellular Vesicles (MISEV2018) guidelines. Each isolated and concentrated sEV preparation was standardized and its purity was determined by the ratio of the number of particles to protein concentration.
RESULTS: All the WJMSCs samples passed the Mesenchymal Stem Cells (MSCs) characterization QC tests. Qualitatively, EVs-positive markers (CD63 and TSG101) and intact bilipid membrane vesicles were detected in all the sEV preparations. Quantitatively, the protein and particle concentrations revealed that all the sEV preparations were "impure" with < 1.5 × 109 particles/µg protein. Albumin was co-isolated in all the sEV preparations.
CONCLUSION: In short, all characterized and standardized individual and pooled sEV preparations were deemed "impure" due to albumin co-isolation using the TFF method. For therapeutic development, it is essential to report protein and particle concentrations in EV preparations based on these QC results.