Latest ArticlesTraditional carbon-based catalysts often suffer from limited catalytic activity and stability in peroxymonosulfate (PMS) activation for pollutant degradation. In this study, a Si–O doped layered carbonaceous catalyst (Si/C@PDA) was synthesized using natural vermiculite as the hard template, enabling the efficient PMS activation for ultrafast degradation of tetracycline (TC) (kobs-TC = 0.228 min–1). The Si/C@PDA/PMS system demonstrated strong resistance to environmental matrix interference by enhanced singlet oxygen (1O2) generation and electron transfer processes (ETP). The experimental and density functional theory results prove that the Si–O site induced the cleavage of adsorbed PMS to promote the generation of 1O2 through the self-decomposition of peroxymonosulfate anion radical (SO5•–) and the disproportionation of superoxide radical (O2•–). Meanwhile, the Si–O site also served as the electron transfer bridge facilitating electron migration from the pollutants to the Si/C@PDA/PMS*. In addition, the Si/C@PDA/PMS system integrated with membrane filtration technology achieved continuous degradation of TC with nearly 100% removal efficiency under a low oxidant dosage (0.1 mmol/L PMS). This work underscores the pivotal role of Si–O doping in modulating active sites to selectively promote nonradical pathways, thereby promoting effective pollutant degradation in complex water matrices and offering strategic insights for developing nonradical-dominated carbon-based catalysts.
Although porous carbons (PCs) are promising materials for environmental remediation, their synthesis strategies face high cost, strong device corrosion and severe environmental hazards. This review overviews sustainable organic salt-derived PCs (OAPCs), emphasizing their dual role as activators and self-activators. Meanwhile, six different organic salts are identified for the unique decomposition pathways and activation mechanisms, governing hierarchical porous formation, heteroatom doping, etc. Crucially, the dimensional control of OAPCs from 0D to 3D is min discussed, which favors optimizing ion transport and surface accessibility. The environmental applications of OAPCs are further assessed, proving high potential value in the adsorption, capacitive deionization (CDI), peroxymonosulfate (PMS) activation, electro-Fenton (EF) and microwave absorption. These advancements mainly rely on the tailored morphology and porosity, high conductivity and improved hydrophilicity. Overall, this review provides new insights into sustainable OAPCs, bridging fundamental mechanisms to practical environmental solutions.
A novel in-situ method for creating a CO2-responsive switchable deep eutectic solvent (SDES) was developed for the homogeneous liquid-liquid microextraction (HLLME) of 18 polychlorinated biphenyls (PCBs) compounds from water samples. Following extraction, the compounds were separated and analyzed by gas chromatography-triple quadrupole tandem mass spectrometry (GC–MS/MS). Diethanolamine (DEA) was employed as the hydrogen bond acceptor (HBA), while 3-methoxyphenol (3-MP) served as the hydrogen bond donor (HBD). The in-situ formed CO2-responsive SDES was used as the extractant, representing a green alternative to traditional toxic organic reagents. Under ideal extraction conditions, a strong linear relationship was established over the concentration range of 0.1–100 ng/mL. The limits of detection (LODs) and quantification (LOQs) were found to be between 0.001–0.040 ng/mL and 0.004–0.134 ng/mL. The preconcentration factors (PFs) varied from 49 to 76, while both intra-day and inter-day precision (RSDs) remained under 6.8%. Ultimately, the method proved successful in detecting 18 PCBs in environmental water samples, with satisfactory recovery rates. These results demonstrate that the method is not only green and sustainable, but also exhibits excellent methodological performance, rendering it highly promising for applications in the field of HLLME.
Cyanuric acid (CA), a stable triazine-based compound widely used in chlorinated disinfectants, has emerged as a recalcitrant contaminant frequently detected in aquatic environments. Due to its high chemical stability and electron-deficient structure, CA resists degradation by conventional oxidative processes. Here, a tandem UV/sulfite (SF)-based photochemical reduction-electrochemical oxidation (EO) process was developed to achieve complete mineralization of CA. Hydrated electrons generated in the UV/SF process efficiently induce triazine ring-opening, while EO further oxidizes the resulting intermediates. Density functional theory calculations and quenching experiments revealed that CA exhibits high activation barriers toward direct oxidation by •OH and SO4•−, explaining their limited reactivity. In contrast, direct electron transfer to the boron-doped diamond (BDD) anode dominates CA degradation in the EO process, albeit with sluggish kinetics due to the high oxidation potential. The tandem system effectively lowers the oxidative energy barrier via reductive fragmentation. Moreover, the UV/SF-EO process demonstrated reliable and effective performance in natural waters and industrial effluents. This study not only highlights the synergistic benefits of coupling reduction and oxidation for the treatment of persistent organic pollutants but also provides mechanistic insight into CA degradation pathways, offering a promising strategy for mitigating CA pollution in complex water environments.
The availability of extensive catalytic data from experiments and computations has facilitated the utilization of machine learning (ML) in developing electrocatalysts for the electrocatalytic nitrogen reduction reaction (eNRR). ML has emerged as an effective paradigm to accelerate the discovery and optimization of eNRR catalysts, leveraging its capabilities in automated processing, interpretation, and prediction of high-performance candidates. Crucially, ML enabled the construction of precise data-driven models, establishing key structure-property relationships between material characteristics (e.g., electronic structure, surface properties) and target catalytic performance (activity, selectivity, stability). This review systematically summarized recent advances in ML for eNRR. ML and deep learning methodologies are examined, focusing on two critical aspects: high-throughput catalyst screening and electrocatalytic process optimization. A critical assessment is provided on the commonalities and challenges in model development, encompassing dataset construction, optimal model selection, and interpretability. Finally, current limitations of ML in eNRR research are discussed and the future perspectives and opportunities for the field are also outlined.
Macroalgae, as the third-generation biomass energy source, is considered the most promising form of renewable energy to replace fossil-based energy resources. Hydrothermal liquefaction (HTL) is a low-carbon conversion technology that transforms macroalgae into liquid biofuels by decomposing and converting macroalgal biomass. This review, based on a summary of recent literature, first outlines the environmental risks associated with macroalgae blooms and highlights the benefits of utilizing hydrothermal liquefaction technology for macroalgae treatment. Subsequently, it elaborates on the key factors influencing the hydrothermal liquefaction of macroalgae, including reaction temperature, reaction time, catalyst type, and solution conditions. The results indicate that bio-oil production and consumption occur concurrently during the hydrothermal liquefaction of macroalgae. As reaction temperature and time increase, the bio-oil yield exhibits a trend of first increasing and then decreasing. When the reaction temperature is excessively high or the reaction time is prolonged, the bio-oil consuming reactions gradually dominate, resulting in reduced bio-oil yields. Compared to homogeneous catalysts, heterogeneous catalysts have garnered greater attention due to their superior catalytic performance and recyclability. The findings demonstrate that mixed solutions are more conducive to bio-oil production than aqueous solutions. Furthermore, this review examines the latest advancements in hydrothermal liquefaction technologies, as well as the elemental migration occurring during the process. The current challenges associated with bio-oils include their high production costs and complex compositions, necessitating further investigation and forming the cornerstone of future research endeavors.
Iron-based metal-organic frameworks (MOFs) stand out as promising catalysts for heterogeneous electro-Fenton (HEF) treatment of micropollutants, yet their performance is often constrained by the limited accessibility of active sites and sluggish electron transfer. Herein, a facile phosphine-assisted amorphization strategy was proposed to convert crystalline MIL-88B(Fe) into a P-coordinated amorphous counterpart (aMIL-88B(Fe)-P) featuring abundant asymmetric P–Fe–O moieties. The amorphous architecture ensures abundant exposure of active sites and enhanced mass transport, while the asymmetric coordination environment upshifts the Fe d-band center toward the Fermi level, thereby lowering the energy barrier for H2O2 activation. Notably, the aMIL-88B(Fe)-P-catalyzed EF system attained complete naproxen removal within 90 min, with a pseudo-first-order kinetic rate constant of 0.038 min-1, outperforming all comparative trials and many reported HEF systems. Moreover, the system demonstrated broad-spectrum micropollutant removal, favorable catalyst reusability, and remarkable scalability in a flow-through electrochemical device, confirming strong practical applicability. This work highlights the potential of combining structural amorphization with precise coordination engineering to fully unlock the catalytic potential of MOFs for advanced EF applications.
The effective FeⅡ/FeⅢ cycling and rapid activation of oxidants are the key problems that expand the application of iron-based Fenton-like reaction in water purification. Herein, bamboo-like Fe-Mo bimetallic catalysts (FeMo@CNT) were prepared to activate peroxydisulfate (PDS) for organic pollutants removal. FeMo@CNT/PDS system can completely degrade bisphenol (BPA) within 30 min (kobs = 0.444 min-1, which was 17.1 times that of Fe@CNT/PDS system). Results showed that singlet oxygen (1O2) and sulfate radical (SO4•-) were dominant reactive oxygen species in FeMo@CNT/PDS/BPA system, where 1O2 was derived from superoxide radical (O2•-) oxidized by SO4•-. Mo species in FeMo@CNT/PDS system could accelerate the O–O breaking in PDS and reduce the energy barrier of 1O2 production, thus achieving the increase of both SO4•- and 1O2. Meanwhile, FeMo@CNT/PDS system exhibited good catalytic activity with wide pH and multiple environmental substrates. The high oxidation performance after 23 h in dynamic experiments further demonstrated the stability of this system. And the toxicity of the original pollutants was further declined, indicating that the FeMo@CNT/PDS system was ecologically friendly. This work provides new insights for solving hindered FeⅡ/FeⅢ cycle and proves a new mechanism of 1O2 generation in SR-AOPs.
Genetically encoded sensors have been extensively utilized for the analysis of biomarkers. However, their applications remain limited by the scarcity of analyte-binding proteins or peptides and the challenges associated with translating analyte recognition into a quantifiable signal. Herein, we report a novel class of activable chimeric sensors that harness bioluminescent resonance energy transfer (BRET) between nanoluciferase proteins and fluorescent small-molecule probes. We demonstrated that the responsiveness of these hybrid sensors can be significantly enhanced by modulating the electrostatic surface potential of the fusion protein in the vicinity of the recognition site. As proof of concept, we engineered two chimeric bioluminescent sensors, Bp7-pH and Bp7-NO, for the detection of pH and nitric oxide (NO), respectively. Both sensors exhibited strong BRET signal modulation upon exposure to their target analytes, leading to distinct and quantifiable shifts in luminescence color. Importantly, we successfully applied these sensors for field-deployable imaging of pH and NO dynamics in a murine wound infection model. Given the vast array of existing fluorescent molecular sensors, the detection targets of these chimeric bioluminescent sensors can be easily modified by altering the recognition moieties. Collectively, this work establishes a generalizable and tunable design strategy for developing highly sensitive chimeric bioluminescent sensors, with broad applicability in physiological and pathological monitoring.
How to precisely discriminate homologous microRNA sequences from each other has always been a big challenge. Herein, we proposed an artificial mismatch-assisted Cas12a crRNA splicing mechanism that enables completely interference-free discrimination of homologous microRNAs with single-nucleotide resolution. Specifically, a truncated crRNA (tcrRNA) can recover its ability to activate Cas12a trans-cleavage activity when spliced with a miRNA. Since the miRNA splicing region itself is relatively short, rationally tuning the tcrRNA splicing length can render the proposed system with high sensitivity for base-variation discrimination. More importantly, we innovatively introduced an extra artificial mismatch in the miRNA splicing region, which enables the system to distinguish single-nucleotide variants in an interference-free manner. In this way, only the perfectly matched target miRNA can trigger the complete assembly of the spliced crRNA to active Cas12a, while non-target RNAs even with a single-base mismatch cannot. This mechanism ensures that the target miRNA can be accurately hooked even in the presence of a large excess of non-target homologous RNAs (100-fold). The discrimination capability of the proposed method has been proven by achieving the interference-free discrimination of the members within the let-7 family, which can hardly be achieved by conventional hybridization-based amplification methods. This work fully taps into the potential of the Cas12a system in interference-free homologous miRNA discrimination, offering a new tool for precise RNA analysis in molecular diagnostics.
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