Removal of mercury from aqueous solution using mesoporous silica nanoparticles modified with polyamide receptor

Adsorbents from rice husk and shrimp shell for effective removal of heavy metals and reactive dyes in water

Widespread contamination by heavy metals (HMs) and dyes poses a major health risk to people and ecosystems requiring effective treatment. In this work, rice husk (RH) and shrimp shells were extracted to obtain amorphous silica and chitosan, respectively, which were utilized to produce nano-chitosan-coated silica (NCCS). To ensure the stability of the nanoparticles, silica was freeze-dried after being coated with nano-chitosan. Functional groups (-NH2, -OH, P]O) from chitosan nanoparticles (CNPs) were introduced to the surface of silica during this process. Dyes such as brilliant green (BG), methylene blue (MB) and reactive brown (RB) as well as HMs (Cr6+, Pb2+, Cd2+, Ni2+) were removed by adsorbents. CNPs showed the highest adsorption capacity for RB (59.52 mg/g) among dyes and Cr6+ (42.55 mg/g) among HMs. CNPs showed the highest adsorption capacity for HMs among different adsorbents. Although NCCS and CNPs showed similar adsorption capabilities for HMs and dyes, NCCS showed the best stability. The adsorption performance decreased as RB > Cr6+ > MB > BG > Pb2+ > Cd2+ > Ni2+. The adsorption reactions followed both pseudo-first-order and second-order kinetics, and was spontaneous from thermodynamic analysis. In summary, the waste-derived adsorbents demonstrated excellent potential for removing HMs and dyes from water, while supporting effective management solid waste.

Extraction of heavy metals from wastewater using amine-modified mesoporous silica

Presence of heavy metals in wastewater is a critical environmental issue, and efficient extraction of the metals remains a challenging task. In this study, the adsorption behavior of Ce(III), Hg(II), and Cu(II) metal ions using MCM-48 material modified with acid and base functional groups was examined. The modified materials were characterized using various techniques, including XRD, BET, FT-IR, NMR, and SEM, which revealed that the materials' properties remained unchanged after modification. The adsorption capacity of the modified materials for metal ions was then evaluated and was found that the amine-modified MCM-48 material exhibited the highest adsorption efficiency. Precisely, the amine-modified material achieved an adsorption capacity of 97% for Ce(III), 98% for Hg(II), and 90% for Cu(II) after 180 min of adsorption. These results highlight the effectiveness of amine functionalization in enhancing the adsorption capacity of silica material for heavy metals.

Improvement in adsorption of Hg2+ from aqueous media using sodium-type fine zeolite grains

To increase the adsorption capability of Hg²⁺ from aqueous media, we prepared sodium-type fine zeolite grains with various particle sizes (denoted as ZE1, ZE2 and ZE3). The particle sizes of ZE1, ZE2 and ZE3 were 16.363 ± 0.365, 1.454 ± 0.357 and 0.607 ± 0.377 μm, respectively. Moreover, the CEC, specific surface area and pore volume were in the order ZE1 (42 mmol/g and 23.5 m²/g) < ZE2 (72 mmol/g and 67.1 m²/g) < ZE3 (135 mmol/g and 176.6 m²/g). Subsequently, the Hg²⁺ adsorption capability was investigated. The performance of tested agents on Hg²⁺ adsorbed was in the order ZE1 (5.0 mg/g) < ZE2 (9.4 mg/g) < ZE3 (20.2 mg/g). It was concluded that fine crystalline zeolite was important in enhancing the adsorption capability of Hg²⁺. In addition, the mechanism of adsorption of Hg²⁺ on the ZE samples was evaluated. Our results suggested that Hg²⁺ was exchanged with sodium ions in the interlayers of ZE samples with correlation coefficients of 0.966-0.979. Our findings revealed that these ZE samples constitute potential agents for the adsorption of Hg²⁺ from aqueous media.

A Comprehensive Review of Synthesis, Applications and Future Prospects for Silica Nanoparticles (SNPs)

Silica nanoparticles (SNPs) have shown great applicability potential in a number of fields like chemical, biomedical, biotechnology, agriculture, environmental remediation and even wastewater purification. With remarkably instinctive properties like mesoporous structure, high surface area, tunable pore size/diameter, biocompatibility, modifiability and polymeric hybridizability, the SNPs are growing in their applicable potential even further. These particles are shown to be non-toxic in nature, hence safe to be used in biomedical research. Moreover, the molecular mobilizability onto the internal and external surface of the particles makes them excellent carriers for biotic and non-biotic compounds. In this respect, the present study comprehensively reviews the most important and recent applications of SNPs in a number of fields along with synthetic approaches. Moreover, despite versatile contributions, the applicable potential of SNPs is still a tip of the iceberg waiting to be exploited more, hence, the last section of the review presents the future prospects containing only few of the many gaps/research extensions regarding SNPs that need to be addressed in future work.

Mesoporous silica based recyclable probe for colorimetric detection and separation of ppb level Hg2+ from aqueous medium

Functional mesoporous silica probes, MCM-TFM and SBA-TFM, have been synthesized with varying pore sizes and having S-donor sites judiciously selected to bind soft metal centers. The soft S-donor centers are contributed by the thiol functional groups that are introduced into the silica matrices by functionalization with tris(4-formylphenyl)amine followed by 2-aminothiophenol. The materials rapidly and selectively detect Hg2+ colorimetrically and the change in color profile can be perceived through bare eyes. The probes can decontaminate the pollutant heavy metal from aqueous medium at ppb level and the materials are recyclable and reusable for several separation cycles.

Artificial Neural Network Approach for Modelling of Mercury Ions Removal from Water Using Functionalized CNTs with Deep Eutectic Solvent

Multi-walled carbon nanotubes (CNTs) functionalized with a deep eutectic solvent (DES) were utilized to remove mercury ions from water. An artificial neural network (ANN) technique was used for modelling the functionalized CNTs adsorption capacity. The amount of adsorbent dosage, contact time, mercury ions concentration and pH were varied, and the effect of parameters on the functionalized CNT adsorption capacity is observed. The (NARX) network, (FFNN) network and layer recurrent (LR) neural network were used. The model performance was compared using different indicators, including the root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (R²) and relative error (RE). Three kinetic models were applied to the experimental and predicted data; the pseudo second-order model was the best at describing the data. The maximum RE, R² and MSE were 9.79%, 0.9701 and 1.15 × 10^-3, respectively, for the NARX model; 15.02%, 0.9304 and 2.2 × 10^-3 for the LR model; and 16.4%, 0.9313 and 2.27 × 10^-3 for the FFNN model. The NARX model accurately predicted the adsorption capacity with better performance than the FFNN and LR models.