This study examined the physicochemical characteristics of water from the Tshopo River before it entered the REGIDESO SA/Kisangani treatment plant in the Democratic Republic of Congo during the year 2024. Sixty samples were collected and analyzed, with five samples taken each month at five- to seven-day intervals. Temperature (in situ), pH, turbidity, color, conductivity, oxidizable matter content, total alkalimetric titration, total hardness, free chlorine, nitrite ion content, and chloride ion content were evaluated. The following annual average values were obtained from these physicochemical analyses: temperature 26.97 ± 0.78°C; pH 6.5 ± 0.34; total alkalimetric titration 0,0± 0,0; turbidity 2.42 ± 0.30 NTU. colour 8.4±0.30PtCo; conductivity 32.65±3.96µS/Cm; oxidizable matter content 1.75, ±0.26mg/L, alkalimetric titer 0.33±0.12°F, total hardness 0.07±2.03°F, free chlorine 1.13±0.38 mg/L, nitrite ion content 0.02±0.01mg/L and chloride ion content 3.78±0.43mg/L. These water quality parameters showed highly significant differences between the four rainfall seasons of 2024, except for pH, simple alkali-sensitivity, oxidizable solids content, total hardness, nitrite ion content, and chloride ion content (respective p-values of 0.216, 0.441, 0.925, 0.598, and 0.483). This work contributed to highlighting the impact of treatment by REGIDESO SA/Kisangani on the physicochemical quality of the Tshopo River water at the REGIDESO SA/Kisangani plant. It would be important to replicate this study over three years to assess the plant's performance.

Artificial intelligence is reshaping chemical research by linking high-dimensional analytical signals with molecular, biological, environmental, and pharmaceutical information. This review synthesizes literature from 2018–2026 on chemometrics, machine learning, deep learning, graph neural networks, transformers, foundation models, multimodal learning, and generative systems. It examines data obtained from spectroscopy, chromatography, mass spectrometry, electrochemical sensors, hyperspectral imaging, process monitoring, molecular descriptors, fingerprints, SMILES, graphs, three-dimensional structures, proteins, and omics. Environmental applications include contaminant detection and quantification, suspect and non-target screening, source attribution, fate and transport prediction, ecotoxicity assessment, wastewater-treatment evaluation, and ecological-risk prioritization. Pharmaceutical applications encompass raw-material authentication, quality control, impurity profiling, formulation and drug-delivery optimization, continuous manufacturing, real-time release testing, virtual screening, molecular design, and ADMET prediction. Across both domains, AI improves nonlinear pattern recognition, structure–signal translation, candidate ranking, and multi-objective optimization; however, sophisticated models do not consistently outperform well-designed chemometric approaches, particularly with small or biased datasets. Major barriers include class imbalance, limited chemical diversity, data leakage, instrument variability, missing metadata, weak external validation, poor uncertainty calibration, limited interpretability, and regulatory concerns. Future progress requires FAIR multimodal datasets, independent validation, applicability-domain analysis, explainable and uncertainty-aware models, digital twins, federated learning, autonomous laboratories, human oversight, and sustainable computing for trustworthy scientific deployment.

Advanced multifunctional nanomaterials are emerging as versatile platforms for addressing interconnected energy and environmental challenges through tunable composition, high surface area, engineered interfaces, and nanoscale reactivity. This review critically examines the design, synthesis, functionalization, and application of metal, metal oxide, carbon-based, polymeric, porous, two-dimensional, and hybrid nanomaterials. Particular emphasis is placed on green synthesis, renewable precursors, low-impact fabrication, waste-derived feedstocks, scalability, and life-cycle sustainability. The role of surface functionalization, heteroatom doping, defect engineering, heterostructure formation, and interfacial charge transfer in controlling catalytic, optical, electrochemical, and adsorption properties is systematically discussed. Applications in heterogeneous catalysis, photocatalytic hydrogen production, carbon dioxide conversion, pollutant degradation, rechargeable batteries, supercapacitors, and emerging flexible storage devices are evaluated using structure–property–performance relationships. The review also explores nanomaterial-enabled water purification, membrane separation, antimicrobial treatment, and chemical, electrochemical, optical, and gas sensing, including integrated remediation–sensing platforms for real-time monitoring. Current barriers involving toxicity, agglomeration, instability, poor selectivity, recovery, reproducibility, standardization, and industrial scale-up are identified. Finally, future directions highlight artificial-intelligence-guided discovery, operando characterization, circular material design, self-healing systems, and safe, economical deployment, establishing a unified roadmap for sustainable multifunctional nanotechnology across energy conversion, storage, environmental remediation, and chemical detection under realistic operating conditions and across diverse environmental matrices worldwide.

The transition toward carbon-neutral chemical manufacturing requires catalytic systems that can convert abundant waste molecules into value-added chemicals under mild, renewable-energy-driven conditions. Single-atom electrocatalysts (SAECs), featuring isolated metal centers anchored on tailored supports, offer maximum atom utilization, tunable coordination environments, and well-defined active sites for complex multielectron reactions. This review critically examines the emerging role of artificial intelligence in accelerating the discovery, optimization, and mechanistic understanding of SAECs for carbon dioxide electroreduction and green ammonia synthesis through nitrate conversion. First, fundamental design principles are discussed, including metal–support interactions, coordination engineering, electronic-structure modulation, and stability limitations. Next, machine learning, density functional theory integration, high-throughput screening, explainable descriptors, and self-driving laboratory concepts are evaluated as tools for rational catalyst development. Particular emphasis is placed on CO₂ valorization pathways toward CO, formate, hydrocarbons, and alcohols, together with nitrate-to-ammonia conversion as a sustainable nitrogen-recycling strategy. Advanced in situ/operando characterization, performance benchmarking, selectivity control, and degradation mechanisms are also assessed. Finally, this review highlights current barriers related to data quality, catalyst durability, reactor design, product separation, techno-economic feasibility, and industrial scale-up. The article provides a forward-looking framework for integrating AI, atomically precise catalysis, and renewable electrosynthesis in future sustainable chemical manufacturing.