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Laundering synthetic textiles releases a significant amount of microplastic fibers (MPFs), a major contributor to plastic pollution and one that poses numerous health hazards. When fabrics come into contact during laundering, frictional abrasion eventually causes MPFs to break off and release into the environment when the wash water is discharged. Certain polydimethylsiloxane (PDMS)-treated textiles have been shown to significantly reduce MPF release via a reduction in surface friction. However, the extent of MPF release when PDMS-coated textiles are washed with uncoated ones, the most likely real-world scenario, has yet to be investigated. In this work, a PDMS-based coating was used as a finish for polyester fabrics that were laundered with the same unfinished polyester fabric but dyed a different color such that MPF origin could be identified. To ensure the fabrics were in contact during the simulated laundering process, one piece of fabric was adhered to the bottom of a crystallization dish, while the other was sewn around a magnetic stir bar. After laboratory simulated washing, the amount of MPFs released from the different colored fabrics was counted. Both the fabric finish and the orientation were found to affect MPF release. When the bottom fabric was finished and laundered with the uncoated fabric surrounding the stir bar, MPF release was reduced by 42% for the bottom fabric, 28% for the stir bar fabric, and 37% overall. When the orientation was reversed, MPF release was reduced by 33% from the unfinished bottom fabric, 20% from the finished stir bar fabric, and 27% overall. These findings suggest that MPF release can be reduced during laundering even when some of the textiles are unfinished and that these types of finishes can reduce MPF release from unfinished fabrics.
Bimetallic single-atom catalysts (SACs) represent an emerging paradigm beyond single-atom catalysts for enabling synergistic catalysis. The homocoupling of arylboronic acids via C-B bond activation to form C-C bonds has been demonstrated to be entirely inert with Pd or Au SACs. This study achieves a breakthrough by developing a bimetallic single-atom catalyst (Pd1Au1/graphene), which delivers outstanding performance with a TON up to 19,192 and cycling stability (>90% yield after seven cycles). Through comprehensive experiments and theoretical calculations, we have identified a structure of PdN4-AuN4/graphene, where adjacent Pd and Au atoms form a synergistic active center. This promotes simultaneous aromatic boronic acid substrate activation and significantly reduces the reaction energy barrier of the oxidative addition step, thereby enabling a pathway inaccessible to isolated single atoms. This work not only provides an efficient catalyst for organic synthesis but also validates the fundamental principle of synergistic catalysis in bimetallic single-atom catalysts, offering a universal design strategy for addressing challenging transformations.
Nanoplastics generated through environmental weathering may disrupt epithelial barrier integrity by promoting oxidative damage and inflammation, yet most studies rely solely on pristine synthetic particles that lack surface chemistries representative of real-world aged plastics. Here, we investigated the biological effects of environmentally relevant nanoscale polypropylene reference material (NPPP-1) produced by laser ablation and evaluated its material-specific responses under matched particle-number exposure conditions, in comparison with pristine and ultraviolet-aged synthetic polypropylene in human small intestinal organoids (HSIOs) and human intestinal epithelial cells. NPPP-1 induced oxidative stress, with elevated reactive oxygen species (ROS), lipid peroxidation, and DNA oxidation, accompanied by upregulation of genes and proteins associated with inflammation, ROS, and cell death pathways. Functional assays revealed concomitant activation of ferroptosis, apoptosis, and pyroptosis. Ferroptosis was the primary driver of cell death, as evidenced by partial rescue of viability mediated by ferroptosis inhibitor ferrostatin-1. In HSIOs, NPPP-1 triggered nuclear translocation and accumulation of β-catenin and upregulated the Wnt target gene Axin2. Ferroptosis inhibition reduced Wnt upregulation, suggesting activation of regenerative signaling that serves to mitigate ferroptotic stress. Indeed, inhibition of this pathway increased lipid peroxidation and reduced viability, further indicating a compensatory response to counter ferroptotic stress. Imaging and spectroscopic analyses confirmed internalization of NPPP-1 within the epithelial layers, linking the presence of particles to biological effects. These findings demonstrate that environmentally relevant nanoscale plastics such as NPPP-1 elicit oxidative stress-driven cell death while simultaneously activating the Wnt/β-catenin pathway as a protective response. The combined degenerative and compensatory dynamics highlight the importance of using realistic nanoscale plastic materials and advanced 3D organoid systems to assess human health risks under conditions that mimic real-world exposures.
Acute radiation-induced lung injury is a serious and potentially life-threatening complication of radiotherapy for thoracic malignancies or accidental radiation exposure, characterized by high incidence, limited treatment options, and substantial mortality. To address the lack of effective therapies for preventing and treating radiation-induced lung injury, we developed an engineered nanoplatform, BAT-exo@Au, generated by functionalizing exosomes derived from young brown adipose tissue (BAT) with 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-polyethylene glycol-thiol (DSPE-PEG-SH) and gold nanoparticles via chloroauric acid (HAuCl4) incubation. Our results show that BAT-exo@Au was efficiently internalized by irradiated lung tissue and exerted radioprotective effects by suppressing reactive oxygen species production and attenuating radiation-induced inflammatory responses. In addition, BAT-exo@Au mitigated radiation-induced epithelial-mesenchymal transition while enhancing tumor radiosensitivity, suggesting a dual therapeutic advantage. Mechanistically, BAT-exo@Au reduced apoptosis and preserved mitochondrial membrane potential after radiation in vitro. Transcriptomic analysis identified G protein-coupled receptor 183 (Gpr183) as a potential downstream target, showing upregulation after radiation but downregulation following BAT-exo@Au treatment. Further in vitro experiments demonstrated that BAT-exo@Au promoted the interaction between Gpr183 and the E3 ubiquitin ligase NEDD4, facilitating Gpr183 ubiquitination and proteasomal degradation. This study suggests that exosomes derived from young BAT may serve as a therapeutic strategy for the prevention of radiation-induced lung injury. In conclusion, BAT-exo@Au shows promise as a preventive approach for radiation-induced lung injury, potentially through modulation of Gpr183 via enhanced Gpr183-NEDD4 interaction and ubiquitination.
To gain molecular-level insights into cisplatin and its metal analogs, we performed a comprehensive investigation of the geometric and electronic structures of cis-[M-(NH3)2Cl2] (M = CoI, RhI, IrI, NiII, PdII, PtII, CuIII, AgIII, and AuIII), along with their bonding characteristics and hydrolysis behaviors. All complexes adopt a planar, four-coordinate geometry, with the d x 2 - y 2 orbital predominantly involved in ligand interactions. EDA-NOCV analyses indicate that the total interaction energy increasing from Group 9 to 11 with σ-interactions dominates the metal-ligand bonding. cis-[M-(NH3)2Cl2] with M = CoI, RhI, and IrI were found to be unstable, exhibiting a tendency to form five-coordinated species. In contrast, cis-[M-(NH3)2Cl2] complexes (M = NiII, PdII, PtII, CuIII, AgIII, and AuIII) undergo ligand exchange reactions via penta-coordinate trigonal-bipyramidal-like transition states. Their monoaquated derivatives all demonstrated high reactivity toward guanine. Considering their hydrolysis behaviors, cis-[AuIII(NH3)2Cl2]+ emerges as a potential candidate for antitumor applications.
Modulating the spatial position of active sites on MOF support offers a promising strategy for selective H2O2 activation. However, the mechanism for regulating the selectivity of key intermediates in H2O2 activation reactions is still unclear. Herein, we design two Pt-based nanozymes by integrating Au@Pt nanoparticles (NPs) into the external surface (Au@Pt/ZIF-8) and the pores (Au@Pt@ZIF-8) of ZIF-8, and systematically study the intrinsic relationship between the spatial position of metal NPs and the selectivity of H2O2 activation. The experimental results indicate that the Au@Pt@ZIF-8 with more Pt-N bonds at the interface exhibits excellent catalytic activity, mainly producing singlet oxygen (1O2), while Au@Pt/ZIF-8 with fewer bonds is mainly dominated by hydroxyl radicals (•OH). Mechanism studies reveal that Au@Pt@ZIF-8 with more Pt-N bonds produces more electron-deficient Pt sites, significantly reducing the energy barrier of the critical step (*OH → *O), and promoting the generation of 1O2. On the contrary, the Pt sites in Au@Pt/ZIF-8 have higher electron density, resulting in a higher energy barrier for the key step, and facilitating the accumulation of *OH. Importantly, a highly sensitive biosensor based on the Au@Pt@ZIF-8 nanozyme is successfully constructed for organophosphorus pesticide detection, with a limit of detection of 1 ng L-1. This work not only elucidates the atomic-level mechanism by which spatial position regulates catalytic behavior via interfacial chemical bonding, but also provides new insights for the rational design of efficient H2O2 activation.
Pesticide residues in food remain a major threat to human health and ecosystems, yet routine monitoring still relies on centralized, multistep analytical workflows which are poorly suited to rapid and field-deployable detection. In this work, we introduce a rationally designed hexagonal honeycomb metal-insulator-metal (MIM) plasmonic metasurface which functions as a robust, wafer-scale surface/plasmon-enhanced Raman spectroscopy (SERS) platform for pesticide quantification in real food matrices. The MIM honeycomb architecture simultaneously creates highly concentrated electromagnetic hotspots at the excitation wavelength and a plasmonic antenna effect that radiates the Stokes-shifted Raman signals back, effectively multiplying the Raman signal and enabling sensitive detection of multiple fungicides and insecticides directly in cucumber extracts. We show that characteristic vibrational fingerprints can be reliably captured for several representative pesticides (metalaxyl, boscalid, famoxadone, thiamethoxam, etoxazole, cypermethrin) across realistic concentration ranges and in the presence of complex matrix backgrounds, achieving subppm limits of detection that approach or fall below current regulatory maximum residue limits. To convert raw spectra into actionable readouts, we integrate our process flow with a deep feed-forward (DFF) artificial intelligence model pipeline that performs automated spectral preprocessing and supervised learning for both pesticide identification and residue-level classification with respect to regulatory thresholds. This AI-enabled MIM-SERS platform establishes a generalizable route toward compact, high-throughput instruments for multiresidue pesticide surveillance in real food samples, with broader implications for molecular diagnostics and environmental monitoring.
The complexity of chemical mixtures in the environment challenges their in-depth risk assessment due to the diverse compounds in use and the lack of experimental toxicity data. In silico models can be used to fill data gaps for compounds with unknown toxic potency. QSAR models typically distinguish only between active and inactive compounds, providing no information about the levels of activity. In this study, a quantitative structure-activity relationship (QSAR) model that classifies compounds into multiple activity levels was developed to address data gaps in the levels of aryl hydrocarbon receptor-mediated (AhR) activity of compounds commonly detected in environmental samples. Its practical applicability has been demonstrated on highly complex mixtures of aquatic pollutants from the Joined Danube Survey to prioritize the most relevant compounds for experimental assessment. The model's performance showed high sensitivity and specificity, with weighted overall accuracy ranging from 77 to 87%. The combination of experimental and QSAR predicted data was used to calculate site-specific AhR activity, which was compared to the overall AhR activity detected by in vitro bioassays. Experimental testing confirmed the ability of the QSAR model to identify compounds with high AhR activity, including benzonaphthothiophene, perylene, acridone, and triphenylene, and prioritize the most relevant suspected effect drivers. Our model can predict toxic potency and thus prioritize the potential bioactive compounds based on specific activity levels. Our study shows that when QSAR models are used for compound prioritization, several factors must be considered: cytotoxicity, solubility, the high rate of false positives for low-toxicity compounds, and the model's applicability domain.
The Bonn Challenge and the UN Decade on Ecosystem Restoration promote global forest restoration, while the implementation mechanisms and their ecological effects remain insufficiently understood. This study focuses on China's Natural Forest Protection Program (NFPP) as a case study to address this gap. The study uses a causal machine learning approach, i.e., the forest doubly robust learner, to investigate the individual treatment effect by dividing the NFPP implementation into three phases (1-5, 6-14, and 15-20 years) to capture short-term and long-term policy impacts. Provincial-level panel data (1998-2020), incorporating indicators of the natural environment, socioeconomic factors, and ecological governance are used. The results show that the NFPP significantly reduced soil erosion after 15 years of implementation. The policy's effectiveness differed regionally, contingent on nonlinear thresholds that delineate specific ″efficiency traps″ and ″safe operating spaces″. Crucially, driving mechanisms underwent a structural transition, shifting from early anthropogenic disturbance dominance to mature natural background regulation. Mitigation outcomes were constrained by stressors such as extreme rainfall and population density. Notably, excessive afforestation in specific regions failed to yield benefits, exemplifying the adverse trade-offs of violating ecological thresholds. These findings underscore the critical need for long-term commitment and precision governance to ensure sustainable ecological resilience.
Core-hole decay processes in sulfur ions from S2- to S+6 were systematically investigated by using relativistic quantum electrodynamics. Fine-structure wave functions were generated via multiconfiguration Dirac-Fock methods to model decay pathways, including Auger and radiative channels. Auger processes were treated using configuration interaction, with Auger transition rates calculated through distorted-wave and isolated-resonance approximations. Radiative decay rates were determined for multipoles using relaxed-orbital oscillator strengths. The simulations reveal that Auger electron kinetic energies decrease monotonically with increasing oxidation state, while Auger transition intensities vary sensitively with ionization state. These variations are driven by changes in electronic configuration, which modulate Coulomb and Breit interactions, thereby altering energy gaps and intensity ratios across transitions. While radiative decay remains weak in all sulfur ionization states due to the low atomic number, K-shell decay exhibits a significantly higher radiative contribution than L-shell decay. Additionally, higher ionization leads to a slight reduction in the Auger-to-fluorescence decay ratio. Notably, spin-orbit coupling in 2p core-shells exerts a pronounced influence on Auger transition probabilities and X-ray fluorescence yields, though its impact diminishes with increasing ionization.
Induction periods are routinely observed in metal-nanoparticle (nanozyme)-catalyzed nitroarene reduction, yet it remains unclear whether they are universal and how their length depends on the metal, the hydride-generating reductant, and the substrate. Here, we show that the induction period is not an inherent feature of nitroarene reduction but a kinetically modulated state that arises from the competition among nitroarene reduction, the oxygen reduction reaction (ORR), and the hydrogen evolution reaction (HER). By combining real-time absorbance kinetics with localized surface plasmon resonance, open-circuit potential monitoring, and photographic assessment of H2 bubble evolution, we relate the observed kinetic differences to the interfacial electron density, the hydridic character of surface-adsorbed H atoms (H*), and the availability of active surface sites. Across Ag, Au, Pt, and Pd nanoparticles (NPs), the highly hydridic NaBH4 induces a prolonged ORR-dominated induction period for 4-nitrophenol reduction through a sequential pathway. In contrast, the milder NH3-BH3 and the more reactive substrate 4-nitro-1-naphthol favor a concurrent pathway in which the ORR and nitroarene reduction proceed simultaneously. Under N2-saturated conditions, the reduction rates are governed by the balance between k'dissociation and k'HER: Pt NPs exhibit sluggish kinetics with NaBH4 because of H*-induced site blocking and rapid HER, whereas Ag NPs are most active because they minimize the competing HER. With NH3-BH3, in contrast, Pt NPs are most active because the moderated supply of e- and H* maintains sufficient surface availability. Together, these results provide a predictive competition map that can guide the rational design of nanocatalysts and nanozymes for use in complex aqueous and environmental media.
Mercury biomonitoring in freshwater fish is foundational for environmental science, public health, and Indigenous community well-being, given mercury's persistence and toxicity. Accurate characterization of length-mercury relationships is central to environmental monitoring, risk assessment, and the development of fish consumption guidelines. However, most monitoring programs rely on a single default model, typically a log-log or power regression, which may misrepresent true patterns across heterogeneous lake-species combinations. This study introduces a decision-based regression framework that evaluates multiple candidate models using predefined statistical criteria and incorporates sensitivity analyses to assess model stability. Applying this framework to community-based monitoring data from northern Ontario revealed substantial variability in the form and strength of length-mercury relationships. No model type was universally optimal; several lake-species groups exhibited weak or absent relationships, indicating that automatic regression-based approaches can produce misleading estimates. Sensitivity analyses (leave-one-out cross-validation and outlier diagnostics) identified model fragility in data-limited or biologically heterogeneous groups, highlighting the need for explicit uncertainty evaluation. This flexible and transparent approach improves methodological rigor, supports defensible ecological interpretation, and strengthens mercury exposure estimates. This framework reduces the risk of biased exposure estimates, strengthens the scientific defensibility of consumption guidelines, and provides a reproducible, adaptable modeling workflow that can be adopted across environmental monitoring programs, particularly those working in northern, remote, or community-led contexts.
Advanced detection systems increasingly rely on infrared (IR) imaging to overcome the limitations of visible light cameras in adverse environments such as fog, rain, and low-light conditions. However, the effectiveness of IR detection remains fundamentally constrained by the low emissivity contrast between targets and their backgrounds. Here, we present a plasmonic metal-dielectric-metal nanostructure comprising a porous anodic aluminum oxide (AAO) dielectric layer sandwiched between an aluminum substrate and a surface Au nanoparticle layer that enables near-independent modulation of visible reflectance (400-800 nm) and long-wave infrared emissivity (8-14 μm). The decoupling mechanism exploits the distinct characteristic length scales governing each spectral band: visible reflectance is controlled by Fabry-Pérot cavity interference and plasmonic absorption of the Au nanoparticle layer, while infrared emissivity is governed by the intrinsic phonon absorption of the AAO layer and is insensitive to Au coverage. Using scalable anodic oxidation and screen-printing fabrication, we achieve tunable visible reflectance (R = 0.2-0.9) and infrared emissivity (ε = 0.1-0.87). Applied to infrared-enhanced license plate detection, our patterned plates achieve an average recognition rate of ∼45% under adverse environmental conditions, compared to ∼5% for conventional plates. This work offers a scalable route to multispectral patterned surfaces for infrared imaging, thermal sensing, and anticounterfeiting applications.
Understanding the interaction between polymers and proteins is of interest for researchers in medicine, biology, food science, and water treatment, among other fields. The goal may be to create strong interactions with enzymes to improve their catalytic stability, while in nanomedicine and biomedical engineering, the focus is often on reducing protein adsorption on polymer surfaces. Researchers have developed libraries of polymers with various monomer combinations and tested their binding to different proteins to better understand these interactions. In this work, we aimed to identify the polymer with the highest or lowest binding affinity to all proteins, respectively, using Gaussian Process Regression (GPR). However, incorporating categorical features such as the type of monomer has not been widely applied in GPR. Here we compare a range of process models, which were coined Multiplicative kernel, Additive kernel, Easy to interpret Gaussian Process model (EzGP), Latent Variable Gaussian Processes (LVGP), and the Latent Map Gaussian Processes (LMGP) by their developers. The LVGP model was found to perform best on the polymer-protein data set, where the output for binding strength was given by Förster resonance energy transfer (FRET), which can be used to help generate large data sets for machine learning (ML). The polymer that had the highest affinity to glucose oxidase (GOx), uricase (Uri), casein (Cas), trypsin (Trp), carbonic anhydrase (CAn) and bovine serum albumin (BSA) carried positive charges as well as hydrophobic benzyl groups. Negatively charged monomers dominated the polymer that rejected the most proteins intermixed with some cationic units, reminiscent of zwitterionic polymers.
Chemically propelled micro/nanomotors (CMNMs) generally need external fields or environmental cues to perform time-variable motion, severely limiting their ability to execute complex tasks and their application scenarios. This study introduces a z-axis structural encoding strategy for CMNMs, analogous to 3D integration in microelectronics, by radially stacking multilayers with different catalytic activities in sequence on one hemisphere of a particle. As a proof of concept, we demonstrate that polystyrene-Au-Pt Janus micromotors exhibit a time-variable shift from inert-side-leading to active-side-leading motion, with the transition time tunable through the thickness and microscale morphology of the outer metal layer. Experiments and phenomenological simulations show that the motion transformation mechanism is governed by the permeation dynamics of fuel H2O2 in the stacked outer metal layers, which enables H2O2 to reach the inner interface and switch the propulsion mechanism from Pt-catalyzed decomposition to Au-Pt bimetallic self-electrophoresis. For the as-designed CMNMs, the motion speed, leading-side orientation, and transition time can be programmed in temporal sequence by their intrinsic structure parameters, including the layer number, thickness, microstructure, and composition of the stacked active layers. This z-axis multilayer architecture strategy proposed herein provides a large room for CMNMs to encode versatile autonomous motions in a temporal sequence by an intrinsic structure.
Gold-centered self-assemblies exhibit unique structures, physicochemical and pharmacokinetic properties, which are widely explored for the applications of catalysis, analytic devices, optical devices, and biomedicines. Most of these materials are based on gold nanoparticles (AuNPs) and gold nanoclusters (AuNCs). However, the use of Au(III) complexes as building blocks for functionally tailored self-assembled architectures remains underexplored. Herein, we develop a facile self-assembly strategy using gold-nicotinamide complex as a building block to construct uniform nanospheres. Remarkably, these nanoparticles can undergo reversible disassembly and reassembly to give cubic nanoparticles upon pH stimulation via alkaline or acidic additions. Through systematic experimental and theoretical calculations, we identified the driving force and rules for these behaviors: the primary building blocks were formed via metal coordination between Au and nicotinamide, which further self-organized into uniform spherical nanoparticles through noncovalent interactions (π-π stacking and hydrogen-bonding N-H···O═C between amide groups). The mechanistic insights reveal that pH-mediated changes alter the balance of these interactions, enabling morphological switching. This work establishes valuable design principles for stimuli-responsive organometallic nanomaterials and highlights the critical role of ligand architecture in controlling self-assembly behaviors.
Exosomes have emerged as promising biomarkers for noninvasive cancer diagnosis, while highly sensitive, reliable, and convenient assays remain challenging, particularly with conventional single-signal approaches. Herein, we report an exosome-tailored dual-mode self-referencing biosensing platform on a single gold nanowire (AuNW) substrate for the accurate and sensitive quantification of HepG2-derived exosomes, integrating surface-enhanced Raman scattering (SERS) and electrochemical readouts on a AuNW substrate with dual-functional Au nanotag amplification. The platform features three exosome-specific design strategies: CD63 aptamer-functionalized DNA tetrahedrons (AD-NTH) with well-controlled probe orientation, spacing, and accessibility to enhance exosome capture efficiency and mitigate interfacial steric hindrance; dual recognition via CD63-mediated capture and AFP-mediated signal tagging for improved specificity toward HepG2-derived exosomes; and a ratiometric SERS/electrochemical dual-mode readout on a unified substrate to enhance signal reliability. This self-referencing mechanism effectively suppresses nonspecific interference and signal fluctuation, while the dual-functional Au nanotags provide amplified signal outputs. The platform achieves detection limits of 8.41 × 102 particles mL-1 (SERS) and 1.07 × 103 particles mL-1 (electrochemical). Clinical evaluation using serum samples from 10 liver cancer patients and 10 healthy volunteers shows statistically significant differentiation (P < 0.0001), supporting its promising application in noninvasive early screening and diagnosis of liver cancer.
Synthetic polymers are widespread in modern life and pose growing environmental problems, especially in agriculture, where water management and soil health are crucial. Eco-friendly materials that balance performance and environmental safety are desperately needed as sustainable alternatives remain understudied. This study emphasizes the potential of lignin, a naturally occurring, abundant, and underutilized biopolymer, and its conversion into lignin-based hydrogels. Lignin hydrogels offer distinct benefits for agricultural applications due to their inherent antibacterial, biodegradable, and biocompatible properties. Their ability to swell improves soil water retention, promotes plant development in drought-prone areas, and permits regulated release of fertilizer. Lignin-based hydrogels can promote sustainable agricultural methods and lessen the dependency on synthetic polymers by customizing these characteristics. This study points to potential advances in green polymer technology by highlighting their capacity to bridge the gap between environmental stewardship and agricultural production.
An investigation, within dispersion-corrected density functional theory, of the electronic and magnetic behavior of diatomic molecules adsorbed on modified MoS2 monolayers containing coinage metals embedded in sulfur trivacancies is reported. The selective electronic responses induced by gas adsorption highlight the strong potential of these systems for gas-sensing applications. Attention is given to small diatomic molecules of environmental relevance, such as NO, CO, and O2, whose mitigation and/or detection remain critical challenges. Results demonstrate that incorporation of group 11 metals into sulfur vacancies significantly enhances the adsorption capability of MoS2 monolayers. Strong adsorption energies, reaching up to -46.157 kcal·mol-1 (-2.001 eV), are observed, especially in Cu-doped systems, indicating their suitability for gas capture and sensing. Density of states and projected density of states analyses reveal notable changes in the electronic structure upon adsorption. In particular, a copper trimer embedded into a trivacancy of sulfur exhibits state splitting upon CO adsorption on it, identifying it as a promising chemiresistive sensor candidate. The Ag- and Au-doped systems interacting with NO display pronounced modifications in their PDOS profiles and magnetic moments, with total magnetic moments exceeding 1 μB and half-metallic behavior that favors spin-up electron conduction. These charge-transfer and magnetic responses enable tunable electronic and magnetic properties. This work provides valuable insight into defect-engineered MoS2 monolayers and establishes embedded coinage metals as effective design elements for advanced environmental monitoring and pollutant detection sensors but limited by the continuous introduction of defect states in the bandgap.