Optimizing oxygen vacancies through grain boundary engineering to enhance electrocatalytic nitrogen reduction.

Electrocatalytic nitrogen reduction is a challenging process that requires achieving high ammonia yield rate and reasonable faradaic efficiency. To address this issue, this study developed a catalyst by in situ anchoring interfacial intergrown ultrafine MoO2 nanograins on N-doped carbon fibers. By optimizing the thermal treatment conditions, an abundant number of grain boundaries were generated between MoO2 nanograins, which led to an increased fraction of oxygen vacancies. This, in turn, improved the transfer of electrons, resulting in the creation of highly active reactive sites and efficient nitrogen trapping. The resulting optimal catalyst, MoO2/C700, outperformed commercial MoO2 and state-of-the-art N2 reduction catalysts, with NH3 yield and Faradic efficiency of 173.7 µg h-1 mg-1cat and 27.6%, respectively, under - 0.7 V vs. RHE in 1 M KOH electrolyte. In situ X-ray photoelectron spectroscopy characterization and density functional theory calculation validated the electronic structure effect and advantage of N2 adsorption over oxygen vacancy, revealing the dominant interplay of N2 and oxygen vacancy and generating electronic transfer between nitrogen and Mo(IV). The study also unveiled the origin of improved activity by correlating with the interfacial effect, demonstrating the big potential for practical N2 reduction applications as the obtained optimal catalyst exhibited appreciable catalytic stability during 60 h of continuous electrolysis. This work demonstrates the feasibility of enhancing electrocatalytic nitrogen reduction by engineering grain boundaries to promote oxygen vacancies, offering a promising avenue for efficient and sustainable ammonia production.

Improvement of sleep quality in isolated metastatic patients with spinal cord compression after surgery.

BACKGROUND: This study aimed to assess changes in quality of sleep (QoS) in isolated metastatic patients with spinal cord compression following two different surgical treatments and identify potential contributing factors associated with QoS improvement. METHODS: We reviewed 49 patients with isolated spinal metastasis at our spinal tumor center between December 2017 and May 2021. Total en bloc spondylectomy (TES) and palliative surgery with postoperative stereotactic radiosurgery (PSRS) were performed on 26 and 23 patients, respectively. We employed univariate and multivariate analyses to identify the potential prognostic factors affecting QoS. RESULTS: The total Pittsburgh Sleep Quality Index (PSQI) score improved significantly 6 months after surgery. Univariate analysis indicated that age, pain worsening at night, decrease in visual analog scale (VAS), increase in Eastern Cooperative Oncology Group performance score (ECOG-PS), artificial implant in focus, and decrease in epidural spinal cord compression (ESCC) scale values were potential contributing factors for QoS. Multivariate analysis indicated that the ESCC scale score decreased as an independent prognostic factor. CONCLUSIONS: Patients with spinal cord compression caused by the metastatic disease had significantly improved QoS after TES and PSRS treatment. Moreover, a decrease in ESCC scale value of > 1 was identified as a favorable contributing factor associated with PSQI improvement. In addition, TES and PSRS can prevent recurrence by achieving efficient local tumor control to improve indirect sleep. Accordingly, timely and effective surgical decompression and recurrence control are critical for improving sleep quality.

Amino-functionalized NH2-MIL-125(Ti)-decorated hierarchical flowerlike Znln2S4 for boosted visible-light photocatalytic degradation.

Developing novel heterojunction photocatalysts with visible-light response and remarkable photocatalytic activity have been verified to applying for the photodegradation of antibiotics in water environment. Herein, NH2-MIL-125(Ti) was integrated with flowerlike ZnIn2S4 to construct NH2-MIL-125(Ti)@ZnIn2S4 heterostructure using a one-pot solvothermal method. The photocatalytic performance was evaluated by the degradation of tetracycline (TC) under visible light illumination. The optimized NM(2%)@ZIS possesses a photodegradation rate (92.8%) and TOC removal efficiency (58.5%) superior to pristine components, which can be principally attributed to the positive cooperative effects of well-matched energy level positions, strong visible-light-harvesting capacity, and abundant coupling interfaces between the two. Moreover, the probable TC degradation mechanism was also clarified using the active species trapping experiments. This study inspires further design and construction of NH2-MIL-125(Ti) and ZnIn2S4 based photocatalysts for effective removal of antibiotics in water environment.

A novel glaucomatous representation method based on Radon and wavelet transform.

BACKGROUND: Glaucoma is an irreversible eye disease caused by the optic nerve injury. Therefore, it usually changes the structure of the optic nerve head (ONH). Clinically, ONH assessment based on fundus image is one of the most useful way for glaucoma detection. However, the effective representation for ONH assessment is a challenging task because its structural changes result in the complex and mixed visual patterns. METHOD: We proposed a novel feature representation based on Radon and Wavelet transform to capture these visual patterns. Firstly, Radon transform (RT) is used to map the fundus image into Radon domain, in which the spatial radial variations of ONH are converted to a discrete signal for the description of image structural features. Secondly, the discrete wavelet transform (DWT) is utilized to capture differences and get quantitative representation. Finally, principal component analysis (PCA) and support vector machine (SVM) are used for dimensionality reduction and glaucoma detection. RESULTS: The proposed method achieves the state-of-the-art detection performance on RIMONE-r2 dataset with the accuracy and area under the curve (AUC) at 0.861 and 0.906, respectively. CONCLUSION: In conclusion, we showed that the proposed method has the capacity as an effective tool for large-scale glaucoma screening, and it can provide a reference for the clinical diagnosis on glaucoma.

A Survey on Intelligent Screening for Diabetic Retinopathy.

Diabetic retinopathy (DR) is one of the leading causes of vision loss and can be effectively avoided by screening, early diagnosis and treatment. In order to increase the universality and efficiency of DR screening, many efforts have been invested in developing intelligent screening, and there have been great advances. In this paper, we survey DR screening from four perspectives: 1) public color fundus image datasets of DR; 2) DR classification and related lesion-extraction approaches; 3) existing computer-aided systems for DR screening; and 4) existing issues, challenges, and research trends. Our goal is to provide insights for future research directions on DR intelligent screening.

L-cysteine-assisted synthesis of hierarchical NiS2 hollow spheres supported carbon nitride as photocatalysts with enhanced lifetime.

Novel hierarchical NiS2 hollow spheres modified by graphite-like carbon nitride were prepared using a facile L-cysteine-assisted solvothermal route. The NiS2/g-C3N4 composites exhibited excellent photocatalytic efficiency in rhodamine B, methyl orange and ciprofloxacin degradation as compared to single g-C3N4 and NiS2, which could be due to the synergistic effects of the unique hollow sphere-like structure, strong visible-light absorption and increased separation rate of the photoinduced electron-hole pairs at the intimate interface of heterojunctions. A suitable combination of g-C3N4 with NiS2 showed the best photocatalytic performance. In addition, an electron spin resonance and trapping experiment demonstrated that the photogenerated hydroxyl radicals and superoxide radicals were the two main photoactive species in photocatalysis. A possible photocatalytic mechanism of NiS2/g-C3N4 composites under visible light irradiation is also proposed. The strategy presented here can be extended to a general strategy for constructing 3D/2D heterostructured photocatalysts for broad applications in photocatalysis.

Catalytic hydrolysis of carbonyl sulfide in blast furnace gas over Sm-Ce-Ox@ZrO2 catalyst.

Carbonyl sulfur (COS) is a prominent organic sulfur pollutant commonly found in the by-product gas generated by the steel industry. A series of Sm-doped CeOx@ZrO2 catalysts were prepared for the hydrolysis catalytic removal of COS. The results showed that the addition of Sm resulted in the most significant enhancement of hydrolysis catalytic activity. The 3% Sm2O3-Ce-Ox@ZrO2 catalyst exhibited the highest activity, achieving a hydrolysis catalytic efficiency of 100% and H2S selectivity of 100% within the temperature range of 90-180 °C. The inclusion of Sm had the effect of reducing the acidity of the catalyst while increasing weak basic sites, which facilitated the adsorption and activation of COS molecules at low temperatures. Appropriate doping of Sm proved beneficial in converting active surface chemisorbed oxygen into lattice oxygen, thereby decreasing the oxidation of intermediate products and maintaining the stability of the hydrolysis reaction.

Swin-Net: A Swin-Transformer-Based Network Combing with Multi-Scale Features for Segmentation of Breast Tumor Ultrasound Images.

Breast cancer is one of the most common cancers in the world, especially among women. Breast tumor segmentation is a key step in the identification and localization of the breast tumor region, which has important clinical significance. Inspired by the swin-transformer model with powerful global modeling ability, we propose a semantic segmentation framework named Swin-Net for breast ultrasound images, which combines Transformer and Convolutional Neural Networks (CNNs) to effectively improve the accuracy of breast ultrasound segmentation. Firstly, our model utilizes a swin-transformer encoder with stronger learning ability, which can extract features of images more precisely. In addition, two new modules are introduced in our method, including the feature refinement and enhancement module (RLM) and the hierarchical multi-scale feature fusion module (HFM), given that the influence of ultrasonic image acquisition methods and the characteristics of tumor lesions is difficult to capture. Among them, the RLM module is used to further refine and enhance the feature map learned by the transformer encoder. The HFM module is used to process multi-scale high-level semantic features and low-level details, so as to achieve effective cross-layer feature fusion, suppress noise, and improve model segmentation performance. Experimental results show that Swin-Net performs significantly better than the most advanced methods on the two public benchmark datasets. In particular, it achieves an absolute improvement of 1.4-1.8% on Dice. Additionally, we provide a new dataset of breast ultrasound images on which we test the effect of our model, further demonstrating the validity of our method. In summary, the proposed Swin-Net framework makes significant advancements in breast ultrasound image segmentation, providing valuable exploration for research and applications in this domain.

Phosphotungstic Acid Clusters Decorated Znln2S4 Nanoflowers as Molecular-Scale S-Scheme Heterojunctions for Simultaneous H2 Evolution and Benzyl Alcohol Upgrading.

Simultaneous utilization of photogenerated electrons and holes to achieve overall redox reactions is attractive but still far from practical application. The emerging step (S)-scheme mechanism has proven to be an ideal approach to inhibit charge recombination and supply photoinduced charges with highest redox potentials. Herein, a hierarchical phosphotungstic acid (H3PW12O40, HPW)@Znln2S4 (ZISW) heterojunction was prepared through one-pot hydrothermal method for simultaneous hydrogen (H2) evolution and benzyl alcohol upgrading. The fabricated HPW-based heterojunctions indicated much enhanced visible-light absorption, promoted photogenerated charge transfer and inhibited charge recombination, owing to hierarchical architecture based on visible-light responsive Znln2S4 microspheres, and S-scheme charge transfer pathway. The S-scheme mechanism was further verified by free-radical trapping electron spin resonance (ESR) spectra. Moreover, the wettability of composite heterojunction was improved by the modification of hydrophilic HPW, contributing to gaining active hydrogen (H+) from water sustainably. The optimal ZISW-30 heterojunction photocatalyst indicated an enhanced hydrogen evolution rate of 27.59 mmol g-1 h-1 in benzyl alcohol (10 vol. %) solution under full-spectrum irradiation, along with highest benzaldehyde production rate is 8.32 mmol g-1 h-1. This work provides a promising guideline for incorporating HPW into S-scheme heterojunctions to achieve efficient overall redox reactions.

Active Learning in Brain Tumor Segmentation with Uncertainty Sampling and Annotation Redundancy Restriction.

Deep learning models have demonstrated great potential in medical imaging but are limited by the expensive, large volume of annotations required. To address this, we compared different active learning strategies by training models on subsets of the most informative images using real-world clinical datasets for brain tumor segmentation and proposing a framework that minimizes the data needed while maintaining performance. Then, 638 multi-institutional brain tumor magnetic resonance imaging scans were used to train three-dimensional U-net models and compare active learning strategies. Uncertainty estimation techniques including Bayesian estimation with dropout, bootstrapping, and margins sampling were compared to random query. Strategies to avoid annotating similar images were also considered. We determined the minimum data necessary to achieve performance equivalent to the model trained on the full dataset (α = 0.05). Bayesian approximation with dropout at training and testing showed results equivalent to that of the full data model (target) with around 30% of the training data needed by random query to achieve target performance (p = 0.018). Annotation redundancy restriction techniques can reduce the training data needed by random query to achieve target performance by 20%. We investigated various active learning strategies to minimize the annotation burden for three-dimensional brain tumor segmentation. Dropout uncertainty estimation achieved target performance with the least annotated data.

An Automated Deep Learning-Based Framework for Uptake Segmentation and Classification on PSMA PET/CT Imaging of Patients with Prostate Cancer.

Uptake segmentation and classification on PSMA PET/CT are important for automating whole-body tumor burden determinations. We developed and evaluated an automated deep learning (DL)-based framework that segments and classifies uptake on PSMA PET/CT. We identified 193 [18F] DCFPyL PET/CT scans of patients with biochemically recurrent prostate cancer from two institutions, including 137 [18F] DCFPyL PET/CT scans for training and internally testing, and 56 scans from another institution for external testing. Two radiologists segmented and labelled foci as suspicious or non-suspicious for malignancy. A DL-based segmentation was developed with two independent CNNs. An anatomical prior guidance was applied to make the DL framework focus on PSMA-avid lesions. Segmentation performance was evaluated by Dice, IoU, precision, and recall. Classification model was constructed with multi-modal decision fusion framework evaluated by accuracy, AUC, F1 score, precision, and recall. Automatic segmentation of suspicious lesions was improved under prior guidance, with mean Dice, IoU, precision, and recall of 0.700, 0.566, 0.809, and 0.660 on the internal test set and 0.680, 0.548, 0.749, and 0.740 on the external test set. Our multi-modal decision fusion framework outperformed single-modal and multi-modal CNNs with accuracy, AUC, F1 score, precision, and recall of 0.764, 0.863, 0.844, 0.841, and 0.847 in distinguishing suspicious and non-suspicious foci on the internal test set and 0.796, 0.851, 0.865, 0.814, and 0.923 on the external test set. DL-based lesion segmentation on PSMA PET is facilitated through our anatomical prior guidance strategy. Our classification framework differentiates suspicious foci from those not suspicious for cancer with good accuracy.

DE-JANet: A unified network based on dual encoder and joint attention for Alzheimer's disease classification using multi-modal data.

Structural magnetic resonance imaging (sMRI), which can reflect cerebral atrophy, plays an important role in the early detection of Alzheimer's disease (AD). However, the information provided by analyzing only the morphological changes in sMRI is relatively limited, and the assessment of the atrophy degree is subjective. Therefore, it is meaningful to combine sMRI with other clinical information to acquire complementary diagnosis information and achieve a more accurate classification of AD. Nevertheless, how to fuse these multi-modal data effectively is still challenging. In this paper, we propose DE-JANet, a unified AD classification network that integrates image data sMRI with non-image clinical data, such as age and Mini-Mental State Examination (MMSE) score, for more effective multi-modal analysis. DE-JANet consists of three key components: (1) a dual encoder module for extracting low-level features from the image and non-image data according to specific encoding regularity, (2) a joint attention module for fusing multi-modal features, and (3) a token classification module for performing AD-related classification according to the fused multi-modal features. Our DE-JANet is evaluated on the ADNI dataset, with a mean accuracy of 0.9722 and 0.9538 for AD classification and mild cognition impairment (MCI) classification, respectively, which is superior to existing methods and indicates advanced performance on AD-related diagnosis tasks.

Ternary dual S-scheme In2O3/SnIn4S8/CdS heterojunctions for boosted light-to-hydrogen conversion.

Developing artificial S-scheme systems with highly active catalysts is significant to long-term solar-to-hydrogen conversion. Herein, CdS nanodots-modified hierarchical In2O3/SnIn4S8 hollow nanotubes were synthesized by an oil bath method for water splitting. Benefiting from the synergy among the hollow structure, tiny size effect, matched energy level positions, and abundant coupling heterointerfaces, the optimized nanohybrid attains an impressive photocatalytic hydrogen evolution rate of 110.4 µmol/h, and the corresponding apparent quantum yield reaches 9.7% at 420 nm. On In2O3/SnIn4S8/CdS interfaces, the migration of photoinduced electrons from both CdS and In2O3 to SnIn4S8via intense electronic interactions contributes to the ternary dual S-scheme modes, which are beneficial to promote faster spatial charge separation, deliver better visible light-harvesting ability, and provide more reaction active sites with high potentials. This work reveals protocols for rational design of on-demand S-scheme heterojunctions for sustainably converting solar energy into hydrogen in the absence of precious metals.

Oxygen vacancy modulation in interfacial engineering Fe3O4 over carbon nanofiber boosting ambient electrocatalytic N2 reduction.

The oxygen vacancy modulation of interface-engineered Fe3O4 nanograins over carbon nanofiber (Fe@CNF) was achieved to improve electrocatalytic nitrogen reduction reaction (NRR) activity and stability via facile electrospinning and tuning thermal procedure. The optimal catalyst calcined at 800 ℃ (Fe@CNF-800) was endowed with abundant nanograin boundaries and optimized oxygen vacancy (Vo) concentration of iron oxides, thereby affording 37.1 µg h-1 mgcat.-1 (-0.2 V vs. reversible hydrogen electrode (RHE)) NH3 yield and rational Faraday efficiency (10.2%), with 13.6 times atomic activity enhancement compared to of that commercial Fe3O4. The interfacial effect of assembled nanograins in particles correlated with the formation of Vo and more intrinsic active sites, which is conducive to the trapping and activation of nitrogen (N2). The in-situ X-ray photoelectron spectroscopy (XPS) measurement revealed the real consumption of adsorbed oxygen when introducing N2 by the trapping effect of Vo. Density-Functional-Theory (DFT) calculation validates the promotive hydrogenation effect and elimination of hydrogen intermediate (H*) interacted with N2 transferring toward oxygen of the support. The optimal catalyst shows a lasting NRR activity at least 90 h, outperforming most reported Fe-based NRR catalysts.

Personalized next-best action recommendation with multi-party interaction learning for automated decision-making.

Automated next-best action recommendation for each customer in a sequential, dynamic and interactive context has been widely needed in natural, social and business decision-making. Personalized next-best action recommendation must involve past, current and future customer demographics and circumstances (states) and behaviors, long-range sequential interactions between customers and decision-makers, multi-sequence interactions between states, behaviors and actions, and their reactions to their counterpart's actions. No existing modeling theories and tools, including Markovian decision processes, user and behavior modeling, deep sequential modeling, and personalized sequential recommendation, can quantify such complex decision-making on a personal level. We take a data-driven approach to learn the next-best actions for personalized decision-making by a reinforced coupled recurrent neural network (CRN). CRN represents multiple coupled dynamic sequences of a customer's historical and current states, responses to decision-makers' actions, decision rewards to actions, and learns long-term multi-sequence interactions between parties (customer and decision-maker). Next-best actions are then recommended on each customer at a time point to change their state for an optimal decision-making objective. Our study demonstrates the potential of personalized deep learning of multi-sequence interactions and automated dynamic intervention for personalized decision-making in complex systems.

Unsupervised Heterogeneous Coupling Learning for Categorical Representation.

Complex categorical data is often hierarchically coupled with heterogeneous relationships between attributes and attribute values and the couplings between objects. Such value-to-object couplings are heterogeneous with complementary and inconsistent interactions and distributions. Limited research exists on unlabeled categorical data representations, ignores the heterogeneous and hierarchical couplings, underestimates data characteristics and complexities, and overuses redundant information, etc. The deep representation learning of unlabeled categorical data is challenging, overseeing such value-to-object couplings, complementarity and inconsistency, and requiring large data, disentanglement, and high computational power. This work introduces a shallow but powerful UNsupervised heTerogeneous couplIng lEarning (UNTIE) approach for representing coupled categorical data by untying the interactions between couplings and revealing heterogeneous distributions embedded in each type of couplings. UNTIE is efficiently optimized w.r.t. a kernel k-means objective function for unsupervised representation learning of heterogeneous and hierarchical value-to-object couplings. Theoretical analysis shows that UNTIE can represent categorical data with maximal separability while effectively represent heterogeneous couplings and disclose their roles in categorical data. The UNTIE-learned representations make significant performance improvement against the state-of-the-art categorical representations and deep representation models on 25 categorical data sets with diversified characteristics.

Poly(azure C)-coated CoFe Prussian blue analogue nanocubes for high-energy asymmetric supercapacitors.

Prussian blue analogues are considered as promising supercapacitor electrode materials due to their high theoretical capacitance and low cost. Yet, they suffer from poor electronic conductivity and cycling life. Here, a redox dye polymer, poly(azure C) (PAC), is in-situ grown uniformly on CoFe Prussian blue analogue (CoFePBA). As a polymer mediator, the PAC coating on each PBA not only enhances the electronic conductivity and surface area, but also improves the structural stability and specific capacitance of PBA. As a result, the optimized CoFePBA@PAC possesses ultrahigh specific capacitance (968.67 F g-1 at 1 A g-1), superior rate performance (665.78 F g-1 at 10 A g-1), and excellent long-cycling stability (92.45% capacity retention after 2000 cycles). As an application, a fabricated CoFePBA@PAC//AC asymmetric supercapacitor (AC = activated carbon) maintains 84.7% capacitance retention in 2000 cycles at 1 A g-1 and displays a superior specific energy of 29.16 W h kg-1 at the power density of 799.78 W kg-1. These results demonstrate that redox dye polymer-coated PBAs with outstanding performance have a promising prospect in the field of energy storage.

Mesoporous poorly crystalline α-Fe2O3 with abundant oxygen vacancies and acid sites for ozone decomposition.

In this work, mesoporous poorly crystalline hematite (α-Fe2O3) was prepared using mesoporous silica (KIT-6) functionalized with 3-[(2-aminoethyl)amino]propyltrimethoxysilane as a hard template (SMPC-α-Fe2O3). The disordered atomic arrangement structure of SMPC-α-Fe2O3 promoted the formation of oxygen vacancies, which was confirmed using X-ray photoelectron spectroscopy (XPS), O2-temperature-programmed desorption (TPD), H2-temperature-programmed reduction (TPR), and in situ diffuse reflectance infrared Fourier transform (DRIFT) analyses. Density functional theory calculations (DFT) also proved that reducing the crystallinity of α-Fe2O3 decreased the formation energy of oxygen vacancies. TPD and in situ DRIFT analyses of NH3 adsorption suggested that the surface acidity of SMPC-α-Fe2O3 was considerably higher than those of mesoporous poorly crystalline α-Fe2O3 (MPC-α-Fe2O3) and highly crystalline α-Fe2O3 (HC-α-Fe2O3). The oxygen vacancies and acid sites formed on α-Fe2O3 surface are beneficial for ozone (O3) decomposition. Compared with MPC-α-Fe2O3 and HC-α-Fe2O3, SMPC-α-Fe2O3 exhibited a higher removal efficiency for 200-ppm O3 at a space velocity of 720 L g-1 h-1 at 25 ± 2 °C under dry conditions. Additionally, in situ DRIFT and XPS results suggested that the accumulation of peroxide (O22-) and the conversion of O22- to lattice oxygen over the oxygen vacancies caused catalyst deactivation. However, O22- could be desorbed completely by continuous N2 purging at approximately 350 °C. This study provides significant insights for developing highly active α-Fe2O3 catalysts for O3 decomposition.

Retinal Vessel Segmentation Based on B-COSFIRE Filters in Fundus Images.

Retinal vessel extraction plays an important role in the diagnosis of several medical pathologies, such as diabetic retinopathy and glaucoma. In this article, we propose an efficient method based on a B-COSFIRE filter to tackle two challenging problems in fundus vessel segmentation: (i) difficulties in improving segmentation performance and time efficiency together and (ii) difficulties in distinguishing the thin vessel from the vessel-like noise. In the proposed method, first, we used contrast limited adaptive histogram equalization (CLAHE) for contrast enhancement, then excerpted region of interest (ROI) by thresholding the luminosity plane of the CIELab version of the original RGB image. We employed a set of B-COSFIRE filters to detect vessels and morphological filters to remove noise. Binary thresholding was used for vessel segmentation. Finally, a post-processing method based on connected domains was used to eliminate unconnected non-vessel pixels and to obtain the final vessel image. Based on the binary vessel map obtained, we attempt to evaluate the performance of the proposed algorithm on three publicly available databases (DRIVE, STARE, and CHASEDB1) of manually labeled images. The proposed method requires little processing time (around 12 s for each image) and results in the average accuracy, sensitivity, and specificity of 0.9604, 0.7339, and 0.9847 for the DRIVE database, and 0.9558, 0.8003, and 0.9705 for the STARE database, respectively. The results demonstrate that the proposed method has potential for use in computer-aided diagnosis.

Wastewater preinteraction accelerates the photoaging of disposable box-derived polystyrene microplastics in water.

Before being discharged into natural environment, almost all of microplastics (MPs) interact with wastewater constituents in wastewater treatment plants (WWTPs). This study investigated the photoaging of disposable box-derived polystyrene (PS) mediated by real wastewater by simulating the case flowing from WWTPs to natural water. Results showed that wastewater influent pretreatment significantly enhanced the photoaging of PSMPs through the sorption of wastewater constituents, e.g., 2.02 times of increase in photooxidation after 30 d of ultraviolet irradiation. Fulvic acid was identified as the leading contributor for the enhancing effect of wastewater relative to other wastewater constituents such as Cl, CO32-, NO3- and clay particles. In-depth mechanism analysis showed that the observed enhancement was critically controlled by the photosensitization effect of wastewater itself and the enhanced utilization of PSMPs for ultraviolet energy. Specifically, various sorbed wastewater constituents can not only generate higher concentrations of •OH and O2⋅- than clean MPs without constituents, but also reinforce the utilization of PSMPs for light energy due to the increased dispersion in solution by increasing hydrophilicity and surface charges. Also, the light-shielding effect was induced by wastewater, but was less important. This study bridges wastewater source and MP aging and fate and suggests the shortened lifetime of (micro)plastic samples via WWTP input to deepen our understanding of MP pollution in the environment.