WS2 significantly enhances the degradation of sulfachloropyridazine by Fe(III)/persulfate

Tungsten sulfide highly boosted Fe(III)/peroxymonosulfate system for rapid degradation of cyclohexanecarboxylic acid: Performance, mechanisms, and applications

In this study, the Fe(III)/WS2/peroxymonosulfate (PMS) system was found to remove up to 97% of cyclohexanecarboxylic acid (CHA) within 10 min. CHA is a model compound for naphthenic acids (NAs), which are prevalent in petroleum industrial wastewater. The addition of WS2 effectively activated the Fe(III)/PMS system, significantly enhancing its ability to produce reactive oxidative species (ROS) for the oxidation of CHA. Further experimental results and characterization analyses demonstrated that the metallic element W(IV) in WS2 could provide electrons for the direct reduction of Fe(III) to Fe(II), thus rapidly activating PMS and initiating a chain redox process to produce ROS (SO4•-, •OH, and 1O2). Repeated tests and practical exploratory experiments indicated that WS2 exhibited excellent catalytic performance, reusability and anti-interference capacity, achieving efficient degradation of commercial NAs mixtures. Therefore, applying WS2 to catalyze the Fe(III)/PMS system can overcome speed limitations and facilitate simple, economical engineering applications.

Decolouration of azo dyes by radicals generated from K2S2O8 automatically in homogeneous aqueous solutions

In this work, at 15 °C and 25 °C, authors study the decolouration reactions (or degradation reactions or oxidation reactions) of 0.10 mM azo dyes (methyl orange and congo red) by (1) K2S2O8 and (2) Co2+/K2S2O8, expounding the roles of K2S2O8 and Co2+: K2S2O8 can oxidize azo dyes automatically in water by radicals; Co2+ has catalysis for oxidation of K2S2O8, whether Co2+ is from CoSO4 or Co(NO3)2, CoCl2, CoAc2. The decolouration degree of azo dyes in all systems quickens with the increase of the initial concentration of K2S2O8 and the reaction temperature. In the presence and absence of Co2+, comparative experiments are done between the oxidation of K2S2O8 and oxone, K2S2O8 shows weaker oxidation than oxone. The decolouration reaction of methyl orange (MO) in the system of (0.10 mM MO + 2.00 mM K2S2O8) accords with the second order reaction, the reaction rate constants are 4.789 M-1 min-1 at 15 °C and 5.894 M-1 min-1 at 25 °C respectively, the activation energy Ea is 14813 J/mol, and Arrhenius equation is k = 2.328 exp[-14813/(RT)].

AI-enhanced adsorption modeling: Challenges, applications, and bibliographic analysis

Inorganic and organic contaminants, such as fertilisers, heavy metals, and dyes, are the primary causes of water pollution. The field of artificial intelligence (AI) has received significant interest due to its capacity to address challenges across various fields. The use of AI techniques in water treatment and desalination has recently shown useful for optimising processes and dealing with the challenges of water pollution and scarcity. The utilization of AI in the water treatment industry is anticipated to result in a reduction in operational expenditures through the lowering of procedure costs and the optimisation of chemical utilization. The predictive capabilities of artificial intelligence models have accurately assessed the efficacy of different adsorbents in removing contaminants from wastewater. This article provides an overview of the various AI techniques and how they can be used in the adsorption of contaminants during the water treatment process. The reviewed publications were analysed for their diversity in journal type, publication year, research methodology, and initial study context. Citation network analysis, an objective method, and tools like VOSviewer are used to find these groups. The primary issues that need to be addressed include the availability and selection of data, low reproducibility, and little proof of uses in real water treatment. The provision of challenges is essential to ensure the prospective success of AI associated with technologies. The brief overview holds importance to everyone involved in the field of water, encompassing scientists, engineers, students, and stakeholders.

Effect of dissolved organic matter on sulfachloropyridazine photolysis in liquid water and ice

Dissolved organic matter (DOM) is an ubiquitous component of environmental snow and ice, which can absorb light and produce reactive species (RS) and thus is of importance in ice photochemistry. The photodegradation of sulfachloropyridazine (SCP) without and with DOM present in liquid water and ice were investigated in this study. The photodegradation rate constants for SCP without DOM present was enhanced by 52.5 % in ice relative to liquid water, likely due to the enhanced role of SCP self-sensitized RS in ice. DOM significantly promoted SCP photolysis in both liquid water and ice, which was mainly attributed to roles of singlet oxygen (1O2) and triplet excited-state DOM (3DOM*) generated from DOM. 1O2 production from DOM was significantly enhanced in ice relative to liquid water. Hydroxyl radical (•OH) production from DOM in ice was similar to those in liquid water. Enhancement in 3DOM* production in ice was observed at low DOM concentrations. Suwannee River Fulvic Acid (SRFA) and Elliott Soil Humic Acid (ESHA) exhibited differences in RS production in liquid water and ice, as well as in enhancement of 1O2 and 3DOM* produced in ice relative to liquid water. DOM induced reaction pathways of SCP different from those without DOM present, and therefore affected toxicity of SCP photoproducts. There were differences in photodegradation pathways of SCP as well as in toxicity of SCP photoproducts between liquid water and ice.

Tungsten disulfide (WS2) is a highly active co-catalyst in Fe(III)/H2O2 Fenton-like reactions for efficient acetaminophen degradation

The most important factor that restricts the decomposition of H₂O₂ in the Fe³⁺/H₂O₂ reaction is the slow cycling efficiency of reducing Fe³⁺ to Fe²⁺. In this study, the addition of tungsten disulfide (WS₂) as a co-catalyst achieved a rapid cycling of the reaction rate-limiting step and a significant enhancement of H₂O₂ decomposition, which resulted in the effective degradation of acetaminophen (APAP). Results show that 99.6% of APAP (5 mg L^-1) could be degraded by H₂O₂/Fe³⁺/WS₂ system within 2.5 min. The conversion of Fe³⁺ to Fe²⁺ occurred mainly on the surface of WS₂ due to the redox reaction of the exposed W⁴⁺ active sites with Fe³⁺ after the unsaturated S atoms were bound to protons. Electron paramagnetic resonance (EPR) and radical quenching experiments evaluated the contribution of hydroxyl radical (•OH) and superoxide radical (O₂^•-) in the degradation of pollutants. WS₂ showed good recoverability after four cycles of the reaction. This study provides a new perspective to improve the efficiency of Fe³⁺/H₂O₂ and provides a reference for the involvement of transition metal sulfides in advanced oxidation processes (AOPs).

Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives

It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.