RSC Advances最新文献

Clay-based geopolymer material cement is an intriguing alternative to traditional Portland cement when looking for ecologically friendly and sustainable building materials. This material blends cutting-edge geopolymerization technologies with abundantly available clay to produce a variety of advantages, including enhanced mechanical properties and reduced carbon emissions. As the need for green building solutions grows, clay-based geopolymer cement stands out because of its superior structural performance, durability, and resistance to extreme environmental conditions. In this study, we present a complete examination of the curing conditions, structural features, and diverse applications of geopolymers, emphasizing the essential elements that determine their strength and performance. We investigated the effect of curing temperature and duration, demonstrating that favorable curing temperatures (such as 60–80 °C) can increase the strength of geopolymers, whereas excessive curing temperatures can degrade their long-term structural integrity. Pre-curing treatments, such as heat and moisture management, were also investigated for their capacity to improve the microstructural density and minimize the porosity. In addition, we investigated improved curing procedures such as autoclave and steam-saturated methods, which provide higher mechanical qualities, especially in terms of compressive strength. Herein, we discussed a variety of applications, including high-performance composites in aerospace and construction and environmental remediation employing the capacity of geopolymers to immobilize dangerous compounds. Finally, we addressed the promise of geopolymers in future sectors, such as infrastructure repair, environmentally friendly systems, and applications in medicine, emphasizing their long-term viability and versatility in current materials science.

The surface deposition of noble metals can effectively improve photocatalytic performance by the effect of electron trapping (ET) or surface plasmon resonance (SPR), but it is not clear which effect dominates the photocatalytic degradation of organic dyes under light irradiation at specific wavelengths. In this work, a series of xAg-(1−x)BiOBr (x = 0.01, 0.02, 0.03, 0.04, 0.05, and 0.06) nanosheets were synthesized via an ultrasonic-assisted chemical coprecipitation and photo-deposition reaction. The effect of light-emitting diode (LED) with different wavelengths on the photocatalytic degradation of Rhodamine B (RhB) dye was investigated. The results revealed that the hole (h⁺) and superoxide free radical (O₂˙⁻) of xAg-(1−x)BiOBr were the main reactive groups contributing to the rapid decomposition of RhB dye. However, it is worth noting that the sources of the main active groups under LED with different wavelengths were different. Notably, photocatalytic performance was enhanced by the ET effect or SPR effect of Ag particles under LED of 390 nm or 570 nm, respectively. Moreover, both the ET and SPR effects synergistically enhanced the photocatalytic performance of Ag-BiOBr under full-spectrum irradiation (xenon lamp). This work provides theoretical and experimental insights into noble metal deposition modification and accelerates the application of photocatalysis technology in the degradation of printing and dyeing wastewater.

This study focuses on the reaction mechanisms involving triphenylphosphine (PPh₃) derivatives, benzyne, and CO₂, giving mechanistic insights into two competing pathways: Path a, which involves direct C–P bond formation, and Path b, which progresses via a [2 + 2] cycloaddition. Comprehensive computational analysis by energy decomposition analysis (EDA) and deformation density insights was employed to elucidate the electronic and steric factors influencing the reactivity and selectivity of PPh₃ derivatives. The results reveal that Path b is energetically and kinetically favored. In Path a, substantial repulsive interactions (ΔE_rep), especially for electron-withdrawing substituents, hinder C–P bond formation, making this pathway unfavorable, while Path b benefits from compensatory effects between interaction energies, with electron-releasing para-substituents, such as NHMe and OMe, increasing stabilization by enhancing ΔE_orb contributions. Substituents in meta positions show greater distortion energies (ΔE_dist), which limit their stabilizing effects compared to para-substituents. The deformation density analysis of transition states (TS1(b) and TS2(b)) emphasizes the crucial role of Pauli deformation (Δρ^Pauli) and orbital deformation (Δρ^Orb) in modulating stability. Para-substituents exhibit stronger electronic effects, reducing ΔE_int more effectively than meta-substituents, which increase ΔE_dist. This positional dependence underscores the importance of substituent design in optimizing reactivity.

The dicyanoaurate anion plays a central role in the gold mining industry. Its recovery from an aqueous solution is dominantly achieved using activated carbon, which, however, suffers from several drawbacks. Herein, we report a simple preparation of a new material containing the bambusuril macrocycle physically sorbed on the surface of silica gel particles. We utilized the ability of bambusuril to form a unique material that extracts dicyanoaurate from an aqueous solution via supramolecular host-guest interactions. Notably, the material selectively removed dicyanoaurate over dicyanoargentate. Equilibrium sorption and desorption of the anion were achieved within minutes at ambient temperature, significantly outperforming activated carbon.

Platinum-based catalysts are widely used in polymer electrolyte fuel cells (PEMFCs) due to their excellent catalytic activity for the oxygen reduction reaction (ORR) and hydrogen oxidation reaction (HOR). In this study, a PtPdIr ternary alloy catalyst was synthesized by a solution plasma (SP) sputtering process with PtPd and PtIr erelctrodes, which provides a non-equilibrium reaction field in solution. The ratio of Ir in the PtPdIr nanoparticles increased as the ratio of Ir in the PtIr electrode increased. However, the ratio reamined constant at about 10%. The size of the nanoparticles could be controlled in the range of 1–3 nm. In addition, the nanoparticles were well dispersed when supported on carbon and no agglomeration was observed. The electrochemical properties of the obtained nanoparticles were investigated in terms of ORR and HOR, and the particle-c (79 : 14 : 7) nanoparticle exhibited the highest ORR and HOR performance. XPS analysis showed that the intensity of I_Pd(II) and I_Pd(0) in particle-c (79 : 14 : 7) was at the same level, and that the chemical bonding state of these elements enhances ORR and HOR activity.

In the domain of metabolomics, the accurate identification of compounds is paramount. However, this process is hindered by the vast number of metabolites, which poses a significant challenge. In this study, a novel approach to compound identification is proposed, namely a molecular-fingerprint prediction method based on the graph attention network (GAT) model. The method involves the processing of fragmentation-tree data derived from tandem mass spectrometry (MS/MS) data computation and the subsequent processing of fragmentation-tree graph data with a technique inspired by natural language processing. The model is then trained using a 3-layer GAT model and a 2-layer linear layer. The results demonstrate the method’s efficacy in molecular-fingerprint prediction, with the prediction of molecular fingerprints from MS/MS spectra exhibiting a high degree of accuracy. Firstly, this model achieves excellent performance in receiver operating characteristic (ROC) and precision–recall curves. The factors that have the most influence on the resultant performance are identified as edge features using different training parameters. Then, better performance is achieved for accuracy and F₁ score in comparison with MetFID. Secondly, the model performance was validated by querying the molecular libraries through methods commonly used in related studies. In the results based on precursor mass querying, the proposed model achieves comparable performance with CFM-ID; in the results based on molecular formula querying, the model achieves better performance than MetFID. This study demonstrates the potential of the GAT model for compound identification tasks and provides directions for further research.

Greywater constitutes a significant portion of urban wastewater and is laden with numerous emerging contaminants that have the potential to adversely impact public health and the ecosystem. Understanding greywater's characteristics and measuring the contamination levels is crucial for designing an effective recycling system. However, wastewater treatment is an intricate process involving significant uncertainties, leading to variations in effluent quality, costs, and environmental risks. This review addresses the existing knowledge gap in utilising artificial intelligence (AI) to enhance the laundry greywater recycling process and elucidates the optimal treatment technologies for the most prevalent micropollutants, including microplastics, nutrients, surfactants, synthetic dyes, pharmaceuticals, and organic matter. The development of laundry greywater treatment technologies is also highlighted with a critical discussion of physicochemical, biological, and advanced oxidation processes (AOPs) based on their functions, methods, associated limitations, and future trends. Artificial neural networks (ANN) stand out as the most prevalent and extensively applied AI model in the domain of wastewater treatment. Utilising ANN models mitigates certain limitations inherent in traditional adsorption models, particularly by offering enhanced predictive accuracy under varied operating conditions and multicomponent adsorption systems. Moreover, tremendous success has been recorded with the random forest (RF) model, exhibiting 100% prediction accuracy for both sessile and effluent microbial communities within a bioreactor. The precise prediction or simulation of membrane fouling behaviours using AI techniques is also of paramount importance for understanding fouling mechanisms and formulating efficient strategies to mitigate membrane fouling.

A small library of twenty-seven novel 3-amino-2,6-disubstituted-4,5,7-trifluorobenzofurans was successfully synthesized with the compounds formed in low to good yield using a tandem S_NAr-cyclocondensation reaction of 4-substituted perfluorobenzonitriles with α-hydroxycarbonyl compounds employing DBU as base. The compounds were prepared as part of a medicinal chemistry project to develop novel fluorinated heterocyclic leads and were characterised by ¹H and ¹⁹F NMR spectroscopy, IR spectroscopy, high resolution mass spectrometry and elemental analysis. The X-ray crystal structure of the 2-(4-methoxybenzoyl)-6-morpholino derivative was determined, which showed the benzoyl substituent to be coplanar with the benzofuran ring, and to form a hydrogen bond to the 3-amino group. Attempts to synthesise the corresponding 3-unsubstituted or 3-methyl analogues using 4-substituted perfluoro-benzaldehydes or acetophenones were unsuccessful, with cleavage of the carbonyl group occurring. A mechanistic study indicated that alkoxide ions attacked the carbonyl group, rather than effecting S_NAr reaction at C-2, leading to loss of a perfluoroaryl anion which was trapped with D₂O.

The conventional method for preparing V₂O₅ from vanadium-rich leachate suffers from three significant drawbacks: low purity, excessive ammonium consumption, and the generation of high-ammonia–nitrogen wastewater. To address these challenges, this study introduces an integrated process involving D2EHPA saponification extraction, hydrolysis vanadium precipitation, and ammonium purification for the production of high-purity V₂O₅ from high-impurity vanadium-rich liquid. After three-stage counter-current extraction at a 60% saponification degree, 40 vol% D2EHPA concentration, an initial pH of 1.8, a phase ratio (O/A) of 2 : 1, and an extraction time of 8 minutes, followed by three-stage counter-current stripping at 2 mol L⁻¹ H₂SO₄ concentration, a phase ratio (O/A) of 2 : 1, and stripping time of 20 minutes, the concentrations of Fe²⁺ and Al³⁺ in the stripping solution were 0.034 g L⁻¹ and 0.439 g L⁻¹, respectively. These contaminants were effectively eliminated with removal efficiencies of 98.78% and 97.93%. At an ammonium addition coefficient of 1, V₂O₅ was prepared with 99.9% purity using the hydrolysis vanadium precipitation-ammonium salt purification approach, which consumed 83% less ammonium salt compared to the ammonium precipitation method. This study significantly reduces ammonium salt usage and provides a scalable, environmentally friendly process for high-purity V₂O₅ production.

In this study, we investigated the fragmentation behavior of oligosaccharides, specifically α-cyclodextrin (α-CD) and maltohexaose, using TiO₂ nanoparticles as ionizing substrates in SALDI-MS. Both compounds exhibited selective fragmentation at glycosidic bonds, with characteristic fragment ions for α-CD observed at m/z = 835, 673, 511, and 349. Similar fragmentation patterns were obtained with other metal oxide semiconductors (ZnO, Fe₂O₃, WO₃), while non-oxide semiconductors (CdS, CdSe) showed no fragmentation or molecular ion signals. The suppression of fragmentation upon glucose addition suggests the involvement of photoinduced holes, indicating that charge separation and oxide properties of the substrate contribute to the reaction. These findings demonstrate that SALDI-MS using oxide semiconductors enables controlled fragmentation of oligosaccharides and offers potential for structural analysis of glycans and photocatalytic surface reaction studies.