Controllable defects in monolayer graphene induced by hydrogen and argon plasma.

Graphene has attracted wide attentions since its successfully exfoliation. Honeycomb sp2 carbon lattice and Dirac semi-metal band structure make graphene a promising material with excellent mechanical strength, thermal conductivity, and carrier mobility. However, the absence of intrinsic bandgap limits its application in semiconductor. Defects in graphene is supposed to modify its band structure and lead to an opened bandgap. Many methods have been demonstrated to introduce defects into graphene, such as chemical reaction, plasma, electron beam, and laser. However, the species of defects are mostly uncontrollable in most treatment processes. In this study, we report three kinds of defects can be controllably induced in graphene via hydrogen (H2) and argon (Ar) plasma. With different parameter and feeding gas, hydrogenated graphene, graphene nanomesh and graphene with vacancies can be well obtained. The defect density can be precisely controlled by tuning plasma power and irradiation time. Morphological, spectroscopic, and electrical characterizations are performed to systematically investigate the defect evolution. Graphene nanomesh and graphene with vacancies show obvious difference for roughness and coverage, whereas the morphology of hydrogenated graphene remains similar with that of as-prepared graphene. For hydrogenated graphene, an opened bandgap of ~20 meV is detected. For graphene nanomesh and graphene with vacancies, the semiconductive on/off behaviors are observed. We believe this work can provide more details of plasma-induced defects and assist the application of graphene in semiconductor industry.

The application of computer-aided diagnosis in Breast Imaging Reporting and Data System ultrasound training for residents-a randomized controlled study.

Background: The consistency of Breast Imaging Reporting and Data System (BI-RADS) classification among experienced radiologists is different, which is difficult for inexperienced radiologists to master. This study aims to explore the value of computer-aided diagnosis (CAD) (AI-SONIC breast automatic detection system) in the BI-RADS training for residents. Methods: A total of 12 residents who participated in the first year and the second year of standardized resident training in Ningbo No. 2 Hospital from May 2020 to May 2021 were randomly divided into 3 groups (Group 1, Group 2, Group 3) for BI-RADS training. They were asked to complete 2 tests and questionnaires at the beginning and end of the training. After the first test, the educational materials were given to the residents and reviewed during the breast imaging training month. Group 1 studied independently, Group 2 studied with CAD, and Group 3 was taught face-to-face by experts. The test scores and ultrasonographic descriptors of the residents were evaluated and compared with those of the radiology specialists. The trainees' confidence and recognition degree of CAD were investigated by questionnaire. Results: There was no statistical significance in the scores of residents in the first test among the 3 groups (P=0.637). After training and learning, the scores of all 3 groups of residents were improved in the second test (P=0.006). Group 2 (52±7.30) and Group 3 (54±5.16) scored significantly higher than Group 1 (38±3.65). The consistency of ultrasonographic descriptors and final assessments between the residents and senior radiologists were improved (κ3 > κ2 > κ1), with κ2 and κ3 >0.4 (moderately consistent with experts), and κ1 =0.225 (fairly agreed with experts). The results of the questionnaire showed that the trainees had increased confidence in BI-RADS classification, especially Group 2 (1.5 to 3.5) and Group 3 (1.25 to 3.75). All trainees agreed that CAD was helpful for BI-RADS learning (Likert scale score: 4.75 out of 5) and were willing to use CAD as an aid (4.5, max. 5). Conclusions: The AI-SONIC breast automatic detection system can help residents to quickly master BI-RADS, improve the consistency between residents and experts, and help to improve the confidence of residents in the classification of BI-RADS, which may have potential value in the BI-RADS training for radiology residents. Trial Registration: Chinese Clinical Trial Registry (ChiCTR2400081672).

Fully printed minimum port flexible interdigital electrode sensor arrays.

Screen-printed interdigital electrode-based flexible pressure sensor arrays play a crucial role in human-computer interaction and health monitoring due to their simplicity of fabrication. However, the long-standing challenge of how to reduce the number of electrical output ports of interdigital electrodes to facilitate integration with back-end circuits is still commonly ignored. Here, we propose a screen-printing strategy to avoid wire cross-planes for rapid fabrication of flexible pressure sensor arrays. By innovatively introducing an insulating ink to realize electrical insulation and three-dimensional interconnection of wire crossings, the improved sensor array (4 × 4) successfully reduces the number of output ports from 17 to 8. In addition, we further constructed microstructures on the laser-etched electrode surfaces and the sensitive layer, which enabled the sensor to achieve a sensitivity as high as 17 567.5 kPa-1 in the range of 0-50 kPa. Moreover, we integrated the sensors with back-end circuits for the precise detection of tactile and physiological information. This provides a reliable method for preparing high-performance flexible sensor arrays and large-scale integration of microsensors.

A Dual-Mode Pressure and Temperature Sensor.

The emerging field of flexible tactile sensing systems, equipped with multi-physical tactile sensing capabilities, holds vast potential across diverse domains such as medical monitoring, robotics, and human-computer interaction. In response to the prevailing challenges associated with the limited integration and sensitivity of flexible tactile sensors, this paper introduces a versatile tactile sensing system capable of concurrently monitoring temperature and pressure. The temperature sensor employs carbon nanotube/graphene conductive paste as its sensitive material, while the pressure sensor integrates an ionic gel containing boron nitride as its sensitive layer. Through the application of cost-effective screen printing technology, we have successfully manufactured a flexible dual-mode sensor with exceptional performance, featuring high sensitivity (804.27 kPa-1), a broad response range (50 kPa), rapid response time (17 ms), and relaxation time (34 ms), alongside exceptional durability over 5000 cycles. Furthermore, the resistance temperature coefficient of the sensor within the temperature range of 12.5 °C to 93.7 °C is -0.17% °C-1. The designed flexible dual-mode tactile sensing system enables the real-time detection of pressure and temperature information, presenting an innovative approach to electronic skin with multi-physical tactile sensing capabilities.

Application of radiomics model based on ultrasound image features in the prediction of carpal tunnel syndrome severity.

OBJECTIVE: The aim of our study is to develop and validate a radiomics model based on ultrasound image features for predicting carpal tunnel syndrome (CTS) severity. METHODS: This retrospective study included 237 CTS hands (106 for mild symptom, 68 for moderate symptom and 63 for severe symptom). There were no statistically significant differences among the three groups in terms of age, gender, race, etc. The data set was randomly divided into a training set and a test set in a ratio of 7:3. Firstly, a senior musculoskeletal ultrasound expert measures the cross-sectional area of median nerve (MN) at the scaphoid-pisiform level. Subsequently, a recursive feature elimination (RFE) method was used to identify the most discriminative radiomic features of each MN at the entrance of the carpal tunnel. Eventually, a random forest model was employed to classify the selected features for prediction. To evaluate the performance of the model, the confusion matrix, receiver operating characteristic (ROC) curves, and F1 values were calculated and plotted correspondingly. RESULTS: The prediction capability of the radiomics model was significantly better than that of ultrasound measurements when 10 robust features were selected. The training set performed perfect classification with 100% accuracy for all participants, while the testing set performed accurate classification of severity for 76.39% of participants with F1 values of 80.00, 63.40, and 84.80 for predicting mild, moderate, and severe CTS, respectively. Comparably, the F1 values for mild, moderate, and severe CTS predicted based on the MN cross-sectional area were 76.46, 57.78, and 64.00, respectively.. CONCLUSION: This radiomics model based on ultrasound images has certain value in distinguishing the severity of CTS, and was slightly superior to using only MN cross-sectional area for judgment. Although its diagnostic efficacy was still inferior to that of neuroelectrophysiology. However, this method was non-invasive and did not require additional costs, and could provide additional information for clinical physicians to develop diagnosis and treatment plans.

Application of Machine-learning based on Radiomics Features in Differential Diagnosis of Superficial Lymphadenopathy.

OBJECTIVE: The accurate diagnosis of superficial lymphadenopathy is challenging. We aim to explore a non-invasive and accurate machine-learning method for distinguishing benign lymph nodes, lymphoma, and metastatic lymph nodes. METHODS: The clinical data and ultrasound images of 160 patients with superficial lymphadenopathy (58 benign lymph nodes, 62 lymphoma, 40 metastatic lymph nodes) admitted to our hospital from January 2020 to November 2022 were retrospectively studied. Patients were randomly divided into a training set and test set according to the ratio of 6:4. Firstly, the radiomics features of each lymph node were extracted, and then a series of statistical methods were used to avoid over-fitting. Then, the gradient boosting machine(GBM) was used to build the model. The area under receiver(AUC) operating characteristic curve, precision, recall rate and F1 value were calculated to evaluate the effectiveness of the model. RESULTS: Ten robust features were selected to build the model. The AUC values of benign lymph nodes, lymphoma and metastatic lymph nodes in the training set were 1.00, 0.98 and 0.99, and the AUC values of the test set were 0.96, 0.84 and 0.90, respectively. CONCLUSION: It was a reliable and non-invasive method for the differential diagnosis of lymphadenopathy based on the model constructed by machine learning.

Flexible Symmetric-Defection Antenna with Bending and Thermal Insensitivity for Miniaturized UAV.

Flexible conformal-enabled antennas have great potential for various developable surface-built unmanned aerial vehicles (UAVs) due to their superior mechanical compliance as well as maintaining excellent electromagnetic features. However, it remains a challenge that the antenna holds bending and thermal insensitivity to negligibly shift resonant frequency during conformal attachment and aerial flight, respectively. Here, we report a flexible symmetric-defection antenna (FSDA) with bending and thermal insensitivity. By engraving a symmetric defection on the reflective ground, the radiated unit attached to the soft polydimethylsiloxane (PDMS) makes the antenna resonate at the ISM microwave band (resonant frequency = 2.44 GHz) and conformal with a miniaturized UAV. The antenna is also insensitive to both the bending-conformal attachment (20 mm < r < 70 mm) and thermal radiation (20~100 °C) due to the symmetric peripheral-current field along the defection and the low-change thermal effect of the PDMS, respectively. Therefore, the antenna in a non-bending state almost keeps the same impedance matching and radiation when it is attached to a cylinder-back of a UAV. The flexible antenna with bending and thermal insensitivity will pave the way for more conformal or wrapping applications.

A fully integrated, standalone stretchable device platform with in-sensor adaptive machine learning for rehabilitation.

Post-surgical treatments of the human throat often require continuous monitoring of diverse vital and muscle activities. However, wireless, continuous monitoring and analysis of these activities directly from the throat skin have not been developed. Here, we report the design and validation of a fully integrated standalone stretchable device platform that provides wireless measurements and machine learning-based analysis of diverse vibrations and muscle electrical activities from the throat. We demonstrate that the modified composite hydrogel with low contact impedance and reduced adhesion provides high-quality long-term monitoring of local muscle electrical signals. We show that the integrated triaxial broad-band accelerometer also measures large body movements and subtle physiological activities/vibrations. We find that the combined data processed by a 2D-like sequential feature extractor with fully connected neurons facilitates the classification of various motion/speech features at a high accuracy of over 90%, which adapts to the data with noise from motion artifacts or the data from new human subjects. The resulting standalone stretchable device with wireless monitoring and machine learning-based processing capabilities paves the way to design and apply wearable skin-interfaced systems for the remote monitoring and treatment evaluation of various diseases.

The value of radiomics model based on ultrasound image features in the differentiation between minimal breast cancer and small benign breast masses.

BACKGROUND: Female breast cancer has surpassed lung cancer as the most common cancer, and is also the main cause of cancer death for women worldwide. Breast cancer <1 cm showed excellent survival rate. However, the diagnosis of minimal breast cancer (MBC) is challenging. OBJECTIVE: The purpose of our research is to develop and validate an radiomics model based on ultrasound images for early recognition of MBC. METHODS: 302 breast masses with a diameter of <10 mm were retrospectively studied, including 159 benign and 143 malignant breast masses. The radiomics features were extracted from the gray-scale ultrasound image of the largest face of each breast mass. The maximum relevance minimum reduncancy and recursive feature elimination methods were used to screen. Finally, 10 features with the most discriminating value were selected for modeling. The random forest was used to establish the prediction model, and the rad-score of each mass was calculated. In order to evaluate the effectiveness of the model, we calculated and compared the area under the curve (AUC) value, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the model and three groups with different experience in predicting small breast masses, and drew calibration curves and decision curves to test the stability and consistency of the model. RESULTS: When we selected 10 radiomics features to calculate the rad-score, the prediction efficiency was the best, the AUC values for the training set and testing set were 0.840 and 0.793, which was significantly better than the insufficient experience group (AUC = 0.673), slightly better than the moderate experience group (AUC = 0.768), and was inferior to the experienced group (AUC = 0.877). The calibration curve and decision curve also showed that the radiomics model had satisfied stability and clinical application value. CONCLUSION: The radiomics model based on ultrasound image features has a satisfied predictive ability for small breast masses, and is expected to become a potential tool for the diagnosis of MBC, and it is a zero cost (in terms of patient participation and imaging time).

Stacking transfer of wafer-scale graphene-based van der Waals superlattices.

High-quality graphene-based van der Waals superlattices are crucial for investigating physical properties and developing functional devices. However, achieving homogeneous wafer-scale graphene-based superlattices with controlled twist angles is challenging. Here, we present a flat-to-flat transfer method for fabricating wafer-scale graphene and graphene-based superlattices. The aqueous solution between graphene and substrate is removed by a two-step spinning-assisted dehydration procedure with the optimal wetting angle. Proton-assisted treatment is further used to clean graphene surfaces and interfaces, which also decouples graphene and neutralizes the doping levels. Twist angles between different layers are accurately controlled by adjusting the macroscopic stacking angle through their wafer flats. Transferred films exhibit minimal defects, homogeneous morphology, and uniform electrical properties over wafer scale. Even at room temperature, robust quantum Hall effects are observed in graphene films with centimetre-scale linewidth. Our stacking transfer method can facilitate the fabrication of graphene-based van der Waals superlattices and accelerate functional device applications.

Stack growth of wafer-scale van der Waals superconductor heterostructures.

Two-dimensional (2D) van der Waals (vdW) heterostructures have attracted considerable attention in recent years1-5. The most widely used method of fabrication is to stack mechanically exfoliated micrometre-sized flakes6-18, but this process is not scalable for practical applications. Despite thousands of 2D materials being created, using various stacking combinations1-3,19-21, hardly any large 2D superconductors can be stacked intact into vdW heterostructures, greatly restricting the applications for such devices. Here we report a high-to-low temperature strategy for controllably growing stacks of multiple-layered vdW superconductor heterostructure (vdWSH) films at a wafer scale. The number of layers of 2D superconductors in the vdWSHs can be precisely controlled, and we have successfully grown 27 double-block, 15 triple-block, 5 four-block and 3 five-block vdWSH films (where one block represents one 2D material). Morphological, spectroscopic and atomic-scale structural analyses reveal the presence of parallel, clean and atomically sharp vdW interfaces on a large scale, with very little contamination between neighbouring layers. The intact vdW interfaces allow us to achieve proximity-induced superconductivity and superconducting Josephson junctions on a centimetre scale. Our process for making multiple-layered vdWSHs can easily be generalized to other situations involving 2D materials, potentially accelerating the design of next-generation functional devices and applications22-24.

Trapping Hydrogen Molecules between Perfect Graphene.

There is a lack of deep understanding of hydrogen intercalation into graphite due to many challenges faced during characterization of the systems. Therefore, a suitable route to trap isolated hydrogen molecules (H2) between the perfect graphite lattices needs to be found. Here we realize the formation of hydrogen bubbles in graphite with controllable density, size, and layer number. We find that the molecular H2 cannot be diffused between nor escape from the defect-free graphene lattices, and it remains stable in the pressurized bubbles up to 400 °C. The internal pressure of H2 inside the bubbles is strongly temperature dependent, and it decreases as the temperature rises. The proton permeation rate can be estimated at a specific plasma power. The producing method of H2 bubbles offers a useful way for storing hydrogen in layered materials, and these materials provide a prospective research platform for studying nontrivial quantum effects in confined H2.

Editorial for the Special Issue on Flexible and Wearable Sensors.

Flexible wearable sensors have garnered significant interest in the fields of human-computer interaction, materials science, and biomedicine [...].

Application of ultrasound images-based radiomics in carpal tunnel syndrome: Without measuring the median nerve cross-sectional area.

PURPOSE: By constructing a prediction model of carpal tunnel syndrome (CTS) based on ultrasound images, it can automatically and accurately diagnose CTS without measuring the median nerve cross-sectional area (CSA). METHODS: A total of 268 wrists ultrasound images of 101 patients diagnosed with CTS and 76 controls in Ningbo NO.2 Hospital from December 2021 to August 2022 were retrospectively analyzed. The radiomics method was used to construct the Logistic model through the steps of feature extraction, feature screening, reduction, and modeling. The area under the receiver operating characteristic curve was calculated to evaluate the performance of the model, and the diagnostic efficiency of the radiomics model was compared with two radiologists with different experience. RESULTS: The 134 wrists in the CTS group included 65 mild CTS, 42 moderate CTS, and 17 severe CTS. In the CTS group, 28 wrists median nerve CSA were less than the cut-off value, 17 wrists were missed by Dr. A, 26 wrists by Dr. B, and only 6 wrists were missed by radiomics model. A total of 335 radiomics features were extracted from each MN, of which 10 features were significantly different between compressed and normal nerves, and were used to construct the model. The area under curve (AUC) value, sensitivity, specificity, and accuracy of the radiomics model in the training set and testing set were 0.939, 86.17%, 87.10%, 86.63%, and 0.891, 87.50%, 80.49%, and 83.95%, respectively. The AUC value, sensitivity, specificity, and accuracy of the two doctors in the diagnosis of CTS were 0.746, 75.37%, 73.88%, 74.63% and 0.679, 68.66%, 67.16%, and 67.91%, respectively. The radiomics model was superior to the two-radiologist diagnosis, especially when there was no significant change in CSA. CONCLUSION: Radiomics based on ultrasound images can quantitatively analyze the subtle changes in the median nerve, and can automatically and accurately diagnose CTS without measuring CSA, especially when there was no significant change in CSA, which was better than radiologists.

A Review of Epidermal Flexible Pressure Sensing Arrays.

In recent years, flexible pressure sensing arrays applied in medical monitoring, human-machine interaction, and the Internet of Things have received a lot of attention for their excellent performance. Epidermal sensing arrays can enable the sensing of physiological information, pressure, and other information such as haptics, providing new avenues for the development of wearable devices. This paper reviews the recent research progress on epidermal flexible pressure sensing arrays. Firstly, the fantastic performance materials currently used to prepare flexible pressure sensing arrays are outlined in terms of substrate layer, electrode layer, and sensitive layer. In addition, the general fabrication processes of the materials are summarized, including three-dimensional (3D) printing, screen printing, and laser engraving. Subsequently, the electrode layer structures and sensitive layer microstructures used to further improve the performance design of sensing arrays are discussed based on the limitations of the materials. Furthermore, we present recent advances in the application of fantastic-performance epidermal flexible pressure sensing arrays and their integration with back-end circuits. Finally, the potential challenges and development prospects of flexible pressure sensing arrays are discussed in a comprehensive manner.

Growth of wafer-scale chromium sulphide and selenide semiconductor films.

Two-dimensional (2D) transition metal chalcogenides have attracted enormous attention due to their stunning properties and great prospects for applications. Most of the reported 2D materials have layered structure, and non-layered transition metal chalcogenides are rare. Particularly, chromium chalcogenides are highly complexed in terms of structural phases. Researches on their representative chalcogenides, Cr2S3and Cr2Se3, are insufficient and most of them focus on individual crystal grains. In this study, large-scale Cr2S3and Cr2Se3films with controllable thickness are successfully grown, and their crystalline qualities are confirmed by multiple characterizations. Moreover, the thickness-dependent Raman vibrations are investigated systematically, presenting slight redshift with increasing thickness. The fundamental physical properties of grown Cr2S3and Cr2Se3films, including optical bandgap, activation energy and electrical properties, are measured with different thicknesses. The 1.9 nm thick Cr2S3and Cr2Se3films show narrow optical bandgap of 0.732 and 0.672 eV, respectively. The electrical properties of Cr2S3films demonstratep-type semiconductor behaviours, while the Cr2Se3films exhibit no gate response. This work can provide a feasible method for growing large-scale Cr2S3and Cr2Se3films, and reveal fundamental information of their physical properties, which is helpful for future applications.

Electrical switching of ferro-rotational order in nanometre-thick 1T-TaS2 crystals.

Hysteretic switching of domain states is a salient characteristic of all ferroic materials and the foundation for their multifunctional applications. Ferro-rotational order is emerging as a type of ferroic order that features structural rotations, but control over state switching remains elusive due to its invariance under both time reversal and spatial inversion. Here we demonstrate electrical switching of ferro-rotational domain states in the charge-density-wave phases of nanometre-thick 1T-TaS2 crystals. Cooling from the high-symmetry phase to the ferro-rotational phase under an external electric field induces domain state switching and domain wall formation, which is realized in a simple two-terminal configuration using a volt-scale bias. Although the electric field does not couple with the order due to symmetry mismatch, it drives domain wall propagation to give rise to reversible, durable and non-volatile isothermal state switching at room temperature. These results offer a route to the manipulation of ferro-rotational order and its nanoelectronic applications.

Boron nitride-enabled printing of a highly sensitive and flexible iontronic pressure sensing system for spatial mapping.

Recently, flexible iontronic pressure sensors (FIPSs) with higher sensitivities and wider sensing ranges than conventional capacitive sensors have been widely investigated. Due to the difficulty of fabricating the nanostructures that are commonly used on electrodes and ionic layers by screen printing techniques, strategies for fabricating such devices using these techniques to drive their mass production have rarely been reported. Herein, for the first time, we employed a 2-dimensional (2D) hexagonal boron nitride (h-BN) as both an additive and an ionic liquid reservoir in an ionic film, making the sensor printable and significantly improving its sensitivity and sensing range through screen printing. The engineered sensor exhibited high sensitivity (Smin> 261.4 kPa-1) and a broad sensing range (0.05-450 kPa), and it was capable of stable operation at a high pressure (400 kPa) for more than 5000 cycles. In addition, the integrated sensor array system allowed accurate monitoring of wrist pressure and showed great potential for health care systems. We believe that using h-BN as an additive in an ionic material for screen-printed FIPS could greatly inspire research on 2D materials for similar systems and other types of sensors. Hexagonal boron nitride (h-BN) was employed for the first time to make iontronic pressure sensor arrays with high sensitivity and a broad sensing range by screen printing.

Machine Learning-Enhanced Flexible Mechanical Sensing.

To realize a hyperconnected smart society with high productivity, advances in flexible sensing technology are highly needed. Nowadays, flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device's software. Significant research efforts have been devoted to improving materials, sensing mechanism, and configurations of flexible sensing systems in a quest to fulfill the requirements of future technology. Meanwhile, advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of sensors. Machine learning (ML) as an important branch of artificial intelligence can efficiently handle such complex data, which can be multi-dimensional and multi-faceted, thus providing a powerful tool for easy interpretation of sensing data. In this review, the fundamental working mechanisms and common types of flexible mechanical sensors are firstly presented. Then how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated, which includes health monitoring, human-machine interfaces, object/surface recognition, pressure prediction, and human posture/motion identification. Finally, the advantages, challenges, and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are discussed. These will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.

The underlying mechanisms of PM2.5 and O3 synergistic pollution in East China: Photochemical and heterogeneous interactions.

The rapid development of Chinese cities is accompanied by air pollution. Although the implementation of air pollution control strategies in recent years has alleviated PM2.5 pollution, O3 pollution and the synergistic pollution of PM2.5 and O3 have become more serious. To understand the underlying chemical interaction mechanisms between PM2.5 and O3, we applied the modified Weather Research and Forecasting model with Chemistry (WRF-Chem) to study the effects of aerosol-photolysis feedback and heterogeneous reactions on the two pollutants and revealed the contribution of different mechanisms in different seasons and regions in Yangtze River Delta (YRD) in eastern China. We found that, through the aerosol-photolysis feedback, PM2.5 decreased the surface photolysis rates JNO2 and JO1D, resulting in a decrease in O3 concentration in the VOC-sensitive area and a slight increase in the NOx-sensitive area. The heterogeneous reactions reduced O3 concentration in the YRD in spring, autumn and winter by consuming HxOy. While in summer, the heterogeneous absorption of NOx decreased O3 in the NOx-sensitive areas and increased O3 in the VOC-sensitive areas. Heterogeneous reactions also promoted the secondary formation of fine sulfate and nitrate aerosols, especially in winter. Through the combined effect of two chemical processes, PM2.5 can lead to a decrease in O3 concentration of -3.3 ppb (-7.6 %), -2.2 ppb (-4.0 %), -2.9 ppb (-6.3 %), and - 5.9 ppb (-18.7 %), in spring, summer, autumn and winter in YRD. Therefore, if the PM2.5 concentration decreases, the weakening effect of PM2.5 on the ozone concentration will be reduced, resulting in the aggravation of ozone pollution. This study is important for understanding the synergistic pollution mechanism and provides a scientific basis for the coordinated control of urban air pollution.