Sensors International最新文献

{"title":"Multimetallic graphene-coated THz metasurface biosensor for high-sensitivity hCG detection in pregnancy testing: A simulation study","authors":"K. Vijayakumar ,&nbsp;S. Subha ,&nbsp;N.K. Anushkannan ,&nbsp;Kumaravel Kaliaperumal ,&nbsp;U. Arun Kumar","doi":"10.1016/j.sintl.2025.100351","DOIUrl":"10.1016/j.sintl.2025.100351","url":null,"abstract":"<div><div>Conventional pregnancy testing methods face significant limitations including low sensitivity, cross-reactivity issues, and requirement for sophisticated laboratory equipment, particularly in resource-limited settings. This research introduces an innovative terahertz (THz) biosensor using a graphene-metallic hybrid metasurface architecture to improve pregnancy detection by optical sensing of human chorionic gonadotropin (hCG) indicators. The sensor demonstrates remarkable performance with a maximum sensitivity of 1000 GHz/RIU achieved at the optimal resonant frequency of 0.309 THz within the 0.1–0.55 THz frequency band, corresponding to a refractive index of 1.343 RIU. The frequency-dependent sensitivity analysis reveals that the maximum sensitivity of 1000 GHz/RIU is achieved at 0.309 THz, where the electromagnetic field enhancement reaches its peak value. This optimal operating point corresponds to the fundamental resonance mode of the hybrid metasurface structure, where the coupling between the central graphene resonator and the surrounding metallic rings creates the strongest field localization. The sensitivity decreases progressively at frequencies away from this resonant peak, with values of 500 GHz/RIU at 0.310 THz and 200 GHz/RIU at 0.311 THz, demonstrating the critical importance of precise frequency tuning for optimal sensor performance. Comparative analysis shows competitive or superior performance against existing biosensor designs, offering significant potential for point-of-care pregnancy testing applications with enhanced sensitivity, real-time detection capability, and reduced sample preparation requirements.</div></div>","PeriodicalId":21733,"journal":{"name":"Sensors International","volume":"7 ","pages":"Article 100351"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel DHP-NH2 fluorescent dye for the determination of water content in organic solvents, rice, and aspirin: DFT calculations","authors":"Kittiporn Nakprasit ,&nbsp;Panyakorn Taweechat ,&nbsp;Pornthep Sompornpisut ,&nbsp;Mongkol Sukwattanasinitt ,&nbsp;Waroton Paisuwan ,&nbsp;Anawat Ajavakom","doi":"10.1016/j.sintl.2025.100349","DOIUrl":"10.1016/j.sintl.2025.100349","url":null,"abstract":"<div><div>A novel aminodihydropyridine derivative (<strong>DHP-NH₂</strong>) was synthesized from hydrazine via a tandem cyclotrimerization of methyl propiolate. <strong>DHP-NH₂</strong> distinguishably exhibited either strong fluorescence in aprotic solvents or weak fluorescence in protic solvents. Its fluorescence quenching in THF and MeCN was quantitatively determined in the presence of water content, demonstrating the limit of detection (LOD) of 0.036 %wt and 0.014 %wt in THF and MeCN, respectively. The critical hydrogen-bonding interactions between water molecules and the amino group of <strong>DHP-NH₂</strong> were found to stabilize its excited state, supporting a quenching mechanism as confirmed by DFT/TDDFT calculations. To apply this <strong>DHP-NH₂</strong> probe for on-site analysis, smartphone-based photography together with the ImageJ program was employed for the moisture detection with the LOD of 0.28 %wt in MeCN. Moreover, the trace amount of moisture in organic solvents and solid samples (rice and aspirin) was successfully detected by using this developed method. In addition, we successfully immobilized the <strong>DHP</strong> derivative onto the cellulose paper to be used as a portable test strip for determining water content in MeCN by naked-eye detection.</div></div>","PeriodicalId":21733,"journal":{"name":"Sensors International","volume":"7 ","pages":"Article 100349"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and implementation of a microwave microstrip sensor with convolutional neural network for real-time milk spoilage detection","authors":"Ali Khoshchehre ,&nbsp;Mohammad Amir Sattari ,&nbsp;Umer Hameed Shah ,&nbsp;Gholam Hossein Roshani","doi":"10.1016/j.sintl.2025.100353","DOIUrl":"10.1016/j.sintl.2025.100353","url":null,"abstract":"<div><div>Milk spoilage detection plays a pivotal role in safeguarding food safety and minimizing waste within the dairy sector, although conventional chemical assays remain labor-intensive, invasive, and expensive. The present investigation introduces a non-invasive microwave microstrip sensor coupled with a convolutional neural network (CNN) for real-time assessment of milk spoilage progression. The sensor, modeled and optimized using Advanced Design System (ADS) software to exhibit dual passbands (1807–2466 MHz and 3604–4426 MHz), was fabricated on an RT/Duroid 4003 substrate and evaluated using 10 commercial milk samples (3 % fat) procured sequentially over 10 days and maintained at 21 °C. Measurements of the S21 transmission parameter (101 frequency points per spectrum, with five replicates per sample yielding 50 spectra in total) demonstrated a substantial amplitude disparity, notably at 2166 MHz, where the difference between the freshest (day 10) and most spoiled (day 1) samples attained 7.02 dB—equivalent to approximately 105 times the mean standard deviation (0.067 dB)—facilitating robust differentiation of dielectric alterations attributable to microbial degradation. A one-dimensional CNN was trained on preprocessed spectral data augmented fivefold with white Gaussian noise using five variable standard deviations (σ = 0.20–0.60 dB) to simulate real-world measurement fluctuations, expanding the dataset from 50 to 250 spectra and attaining a training accuracy of 95.5 % and a validation accuracy of 90 %. This hybrid methodology surpasses traditional approaches in terms of rapidity and non-destructiveness, providing a viable framework for milk quality surveillance with applicability to other perishable commodities.</div></div>","PeriodicalId":21733,"journal":{"name":"Sensors International","volume":"7 ","pages":"Article 100353"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A yield classification prediction framework based on intelligent agricultural sensor data and meta heuristic optimization ensemble learning","authors":"Wenqing Ding,&nbsp;Tingting Sun,&nbsp;Jing Zhang","doi":"10.1016/j.sintl.2026.100393","DOIUrl":"10.1016/j.sintl.2026.100393","url":null,"abstract":"<div><div>Potatoes, as an important global food crop, currently rely heavily on default parameters or a single search algorithm for model optimization in potato yield prediction research. The performance of gradient boosting models such as XGBoost (eXtreme Gradient Boosting) is highly dependent on hyperparameter configuration, and the grid search method suffers from insufficient spatial exploration, resulting in significant sensitivity to parameters that affect classification results. Therefore, the goal of this study is to enhance the accuracy and stability of yield level classification and to propose an XGBoost model framework with GA (Genetic Algorithm)-PSO (Particle Swarm Optimization) hybrid optimization. This paper constructs an 18-dimensional phenological, hydrothermal, and soil feature set based on measured sensor data from 2015 to 2023 and classifies yields into three levels: low, medium, and high. Subsequently, we use PSO as the backbone to achieve fast parameter convergence and periodically introduce GA genetic crossover and mutation to enhance population diversity, thereby avoiding local optimal traps. The optimization objective function is set to maximize the 5-fold cross-validated macro F1 score. The results show that after GA-PSO optimization, the macro F1 of the model increases to 0.872 ± 0.01, which is 10.66% higher than that of the unoptimized XGBoost (0.788 ± 0.02), and 2.47%, 1.04%, and 1.63% higher than those of grid search (0.851 ± 0.02), single PSO optimization (0.863 ± 0.01), and single GA optimization (0.858 ± 0.01), respectively. The accuracy and Kappa coefficient also reach 0.861 and 0.786, respectively, which are the highest among the compared methods. The SHAP interpretation results further validate that the interaction interval between the temperature suitability index and growing degree days (Idx_T = 0.70–0.85, GDD = 900–1040 °C d) is the key threshold for yield transition. GA-PSO effectively improves model robustness and parameter tuning efficiency through a hybrid mechanism that combines fast convergence and search jumping, providing highly reliable decision support for hierarchical management and water-fertilizer strategy formulation in smart agriculture.</div></div>","PeriodicalId":21733,"journal":{"name":"Sensors International","volume":"7 ","pages":"Article 100393"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148565129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-sensitivity fiber optic sensor for carbon dioxide monitoring in IoT-Enabled biogas systems","authors":"P. Thaisongkroh ,&nbsp;V. Onpol ,&nbsp;S. Pullteap","doi":"10.1016/j.sintl.2026.100377","DOIUrl":"10.1016/j.sintl.2026.100377","url":null,"abstract":"<div><div>This research presents a high-sensitivity fiber optic refractometric sensor integrated with Internet of Things technology for real-time monitoring of carbon dioxide in biogas applications, addressing critical industrial requirements for continuous gas composition analysis while providing remote accessibility and electromagnetic immunity. The sensing mechanism employs polyhexamethylene biguanide (PHMB) functionalized coating applied to de-cladded fiber regions, enhancing selectivity toward carbon dioxide detection through evanescent wave interactions at the core-cladding interface, where carbon dioxide-induced refractive index variations produce measurable optical intensity changes in single-mode fiber architecture. Experimental validation demonstrates robust performance across carbon dioxide concentrations ranging from 34.7% to 93.3%, achieving a high sensitivity of 0.196%/s with response and recovery times of approximately 120 s and 180 s, respectively, while multiple measurement cycles confirm excellent repeatability and stability without baseline drift or signal degradation. The integrated Internet of Things platform enables seamless data acquisition and web-based visualization through secure interfaces, facilitating real-time monitoring with 3-s update intervals and maintaining an average latency of 70 ms for immediate response capabilities. Microstructural analysis confirms chemical stability and adhesion integrity of the PHMB coating following extended carbon dioxide exposure, ensuring long-term operational reliability for continuous monitoring applications. This fiber optic-Internet of Things integration demonstrates significant advantages for industrial gas sensing applications, including compact design, remote monitoring capabilities, and enhanced safety protocols that support process optimization in biogas production facilities while addressing electromagnetic interference concerns common in industrial environments, indicating strong feasibility for commercial deployment in high-sensitivity real-time monitoring systems.</div></div>","PeriodicalId":21733,"journal":{"name":"Sensors International","volume":"7 ","pages":"Article 100377"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-sensitivity terahertz metasensor for cervical cancer Diagnosis: Graphene modulation and XGBoost-Assisted optimization","authors":"Vaijayanthimala J ,&nbsp;Vaishnavi K ,&nbsp;Arun Kumar U ,&nbsp;Dhivya R","doi":"10.1016/j.sintl.2025.100350","DOIUrl":"10.1016/j.sintl.2025.100350","url":null,"abstract":"<div><div>Cervical cancer remains a major cause of mortality, particularly in low-resource settings where traditional cytology-based screening faces challenges such as limited infrastructure and trained personnel. To address this, we present a terahertz (THz) graphene-enhanced metasurface biosensor enabling rapid, label-free detection of cervical cancer biomarkers without complex sample preparation or expensive labs. Using finite element method (FEM) simulations, we demonstrate that tuning graphene's chemical potential from 0.1 to 0.9 eV significantly modulates peak absorption from 0.223 to 1.316, providing a wide dynamic range for sensitive detection across varying sample concentrations. The sensor exhibits robust angular stability, with absorption increasing from 0.546 to 1.306 as the incident light angle shifts from 0° to 80°, ensuring reliable performance without precise optical alignment. Refractive index sensing experiments reveal frequency shifts of 50 GHz and consistently high absorption (55.16 %–56.54 %), achieving a sensitivity of 300 GHz per refractive index unit (RIU) and a figure of merit of 12 RIU⁻¹. To enhance diagnostic accuracy, we integrated an XGBoost machine learning algorithm that analyzes the complex spectral data, achieving 86 % prediction accuracy with low error rates. This combination of advanced sensing and AI-assisted analysis offers a promising, cost-effective solution for cervical cancer screening in resource-limited environments.</div></div>","PeriodicalId":21733,"journal":{"name":"Sensors International","volume":"7 ","pages":"Article 100350"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated multi-modal data fusion framework incorporating mechanical sonar for 3D underwater boundary reconstruction","authors":"Jenjira Sukmanee,&nbsp;Sarawuth Srinakaew,&nbsp;Ramil Kesvarakul","doi":"10.1016/j.sintl.2026.100390","DOIUrl":"10.1016/j.sintl.2026.100390","url":null,"abstract":"<div><div>Accurate and reliable reconstruction of three-dimensional underwater environments remains a persistent challenge, particularly when employing low-cost acoustic sensors onboard compact robotic platforms. In this work, an integrated multi-modal data-fusion framework is presented, which combines a mechanical scanning sonar, an inertial measurement unit (IMU), and a depth sensor to generate high-fidelity 3D boundary representations of underwater scenes. The proposed framework incorporates motion-compensation and depth-anchoring mechanisms that mitigate the effects of slow update rates and motion distortions inherent to mechanical scanning sonars; hence, two-dimensional acoustic profiles are consistently extended into boundary-layer point clouds.</div><div>The controlled experiments were conducted in a saltwater pool environment by using ground-truth anchors for quantitative validation. The fusion system achieved a root-mean-square error of 6.2 cm, a mean absolute deviation of 4.8 cm, and a standard deviation of 2.1 cm, thereby demonstrating reliable sub-decimeter accuracy. Compared with sonar-only reconstructions, which yielded a root-mean-square error of 9.4 cm, the proposed fusion approach improved accuracy by approximately 35 percent. Vertical consistency was also maintained, with average deviations across depth layers of 5.5 cm.</div><div>The reconstructed maps accurately represent both linear and curved geometries, confirming the robustness and accuracy of the proposed framework. Experimental results demonstrate that this low-cost multi-sensor fusion approach is a useful and efficient method for various marine applications, such as aquaculture monitoring, underwater infrastructure inspection, and confined space surveys.</div></div>","PeriodicalId":21733,"journal":{"name":"Sensors International","volume":"7 ","pages":"Article 100390"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148565941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual electrochemical approaches for rapid gonorrhea DNA diagnosis","authors":"Abdulhadee Yakoh ,&nbsp;Anaïs Charles ,&nbsp;Panisak Boonamnaj ,&nbsp;Sudkate Chaiyo ,&nbsp;Sirirat Rengpipat","doi":"10.1016/j.sintl.2025.100345","DOIUrl":"10.1016/j.sintl.2025.100345","url":null,"abstract":"<div><div>Gonorrhea, caused by <em>Neisseria gonorrhoeae</em>, requires rapid diagnostics, especially with the post-pandemic surge in cases. Traditional PCR methods need specialized equipment and trained personnel, underscoring the need for alternative tools. Electrochemical biosensors offer a sensitive, portable solution but have limited reporting for gonorrhea detection. This study develops two electrochemical methods: label-free (signal-off) and labeling (signal-on). The label-free approach uses two DNA sequences and the [Fe(CN)₆]^3-/^4- (potassium ferricyanide/ferrocyanide) redox indicator. The labeling method employs a molecularly amplified DNA sandwich assay with ferrocene-labeled helpers for signal amplification. The label-free method achieved a limit of detection (LOD) of 2.1 nM and a linear dynamic range (LDR) of 10–500 nM, while the labeling method showed an LOD of 4.8 pM and an LDR of 0.5–1000 nM. To enhance practicality, Near Field Communication (NFC)-enabled sensing was used during non-invasive urine sample testing, enabling real-time, wireless detection without sophisticated instruments. This confirmed the superior performance of the labeling method. Molecular dynamics simulations provided insights into structural dynamics, linking experimental data with computational models. This integrated approach highlights the importance of selecting methods based on sensitivity, cost, and ease of use, advancing gonorrhea DNA biosensing technologies.</div></div>","PeriodicalId":21733,"journal":{"name":"Sensors International","volume":"7 ","pages":"Article 100345"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Printing the future of strain measurement: Flexible sensors via additive manufacturing for wearables, robotics, and smart infrastructure","authors":"Shuvodeep De ,&nbsp;Shuo Xu ,&nbsp;Ya Tang ,&nbsp;Amiee Jackson ,&nbsp;Peter L. Wang ,&nbsp;Shalini J. Rukmani ,&nbsp;Eric MacDonald ,&nbsp;Xianhui Zhao ,&nbsp;Chad Duty ,&nbsp;Alex Roschli ,&nbsp;Gianni Stano ,&nbsp;Gianluca Percoco ,&nbsp;Adam Stevens ,&nbsp;Yan Li","doi":"10.1016/j.sintl.2026.100375","DOIUrl":"10.1016/j.sintl.2026.100375","url":null,"abstract":"<div><div>This review presents the evolution of strain sensor technologies from traditional bonded wire models to next-generation flexible designs enabled by advanced materials and additive manufacturing through 3D printing. It highlights breakthroughs in fabrication techniques particularly fused filament fabrication (FFF), direct ink writing (DIW), and vat photopolymerization (VPP) that address the limitations of conventional approaches, including complex multi-step processing and alignment challenges. The incorporation of novel materials such as conductive polymers and hybrid composites is shown to significantly enhance key performance parameters like sensitivity, mechanical durability, and strain sensing range. The convergence of cutting-edge manufacturing, material science, and computational modeling signals a paradigm shift in strain sensing, with broad implications for emerging applications in aerospace, biomedicine, soft robotics, and beyond. This work underscores the importance of continued interdisciplinary collaboration to fully realize the potential of flexible strain sensor technologies in adaptive, high-performance systems.</div></div>","PeriodicalId":21733,"journal":{"name":"Sensors International","volume":"7 ","pages":"Article 100375"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation-driven dual-band Graphene–Silver terahertz metasurface biosensor integrated with machine learning for mode-resolved hemoglobin detection","authors":"Gunasekaran Thangavel ,&nbsp;V. Joseph Michael Jerard ,&nbsp;K. Manivannan ,&nbsp;A.N. Sasikumar ,&nbsp;Vairaprakash Selvaraj ,&nbsp;Manjunathan Alagarsamy","doi":"10.1016/j.sintl.2025.100357","DOIUrl":"10.1016/j.sintl.2025.100357","url":null,"abstract":"<div><div>Accurate hemoglobin measurement is essential for diagnosing hematological disorders and monitoring cardiovascular health. This study introduces a terahertz metasurface biosensor combined with machine learning algorithms for rapid and non-invasive hemoglobin detection in clinical settings. The metasurface exhibits dual-band resonance in 2 THz regions, achieving a sensitivity of 450 GHz/RIU within a refractive index range of 1.34–1.43 RIU, with corresponding frequency shifts of 40 GHz and 30 GHz. Machine learning models, including Random Forest, Support Vector Machines, and Neural Networks, enhance the sensor's analytical capability. Across four clinical categories—normal, mild anemia, moderate anemia, and severe anemia—the models attain 96.5 percent classification accuracy, with recall and precision scores above 0.94. Ensemble learning reduces the root mean square error to 0.28 g/dL, while denoising methods increase the signal-to-noise ratio by 16.3 dB. The biosensor supports real-time analysis, requires minimal sample volume, and eliminates complex preparation, making it suitable for continuous hemoglobin monitoring and cardiovascular health management.</div></div>","PeriodicalId":21733,"journal":{"name":"Sensors International","volume":"7 ","pages":"Article 100357"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}