MethodsX最新文献

{"title":"A two-step machine learning approach for predictive maintenance and anomaly detection in environmental sensor systems","authors":"Saiprasad Potharaju ,&nbsp;Ravi Kumar Tirandasu ,&nbsp;Swapnali N. Tambe ,&nbsp;Devyani Bhamare Jadhav ,&nbsp;Dudla Anil Kumar ,&nbsp;Shanmuk Srinivas Amiripalli","doi":"10.1016/j.mex.2025.103181","DOIUrl":"10.1016/j.mex.2025.103181","url":null,"abstract":"<div><div>Environmental sensor systems are essential for monitoring infrastructure and environmental quality but are prone to unreliability caused by sensor faults and environmental anomalies. Using Environmental Sensor Telemetry Data, this study introduces a novel methodology that combines unsupervised and supervised machine learning approaches to detect anomalies and predict sensor failures. The dataset consisted of sensor readings such as temperature, humidity, CO, LPG, and smoke, with no class labels available. This research is novel in seamlessly blending unsupervised anomaly detection using Isolation Forest to create labels for data points that were previously unlabeled. Finally, these generated labels were used to train the supervised learning models such as Random Forest, Neural Network (MLP Classifier), and AdaBoost to predict anomalies in new sensor data as soon as it gets recorded. The models confirmed the proposed framework's accuracy, whereas Random Forest 99.93 %, Neural Network 99.05 %, and AdaBoost 98.04 % validated the effectiveness of the suggested framework. Such an approach addresses a critical gap, transforming raw, unlabeled IoT sensor data into actionable insights for predictive maintenance. This methodology provides a scalable and robust real-time anomaly detection and sensor fault prediction methodology that greatly enhances the reliability of the environmental monitoring systems and advances the intelligent infrastructure management.<ul><li><span>•</span><span><div>Combines Isolation Forest for anomaly labeling and supervised models for anomaly prediction.</div></span></li><li><span>•</span><span><div>Scalable and adaptable for diverse IoT applications for environmental monitoring.</div></span></li><li><span>•</span><span><div>Provides actionable insights through anomaly visualization, revealing patterns in sensor performance.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103181"},"PeriodicalIF":1.6,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135710","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":"A polyphasic method for the characterization of epiphytic diatoms growing on Gelidium corneum","authors":"María Borrego-Ramos ,&nbsp;Raquel Viso ,&nbsp;Saúl Blanco ,&nbsp;Begoña Sánchez-Astráin ,&nbsp;Camino F. de la Hoz ,&nbsp;José A. Juanes","doi":"10.1016/j.mex.2025.103188","DOIUrl":"10.1016/j.mex.2025.103188","url":null,"abstract":"<div><div>Epiphytic diatoms associated with marine macroalgae play vital ecological roles in nutrient cycling and primary production, yet their study remains limited due to the lack of standardized methodologies. This study focuses on diatom communities growing on <em>Gelidium corneum</em>, a key red alga in the Cantabrian coast (Spain). Samples were collected from two depths along the northern coast of Spain and processed using both morphological and molecular approaches. Morphological analysis involved diatom frustule preparation using hydrogen peroxide digestion, acid treatments, and permanent slide mounting, enabling identification through light microscopy. Molecular analysis employed DNA extraction and <em>rbcL</em> marker-based metabarcoding, allowing detailed taxonomic characterization. Results highlight the efficacy of combining morphological and molecular techniques to overcome the limitations of either approach individually. By standardizing procedures, we enhance the reproducibility and comparability of studies focused on diatom epiphytes. Our results highlight the ecological significance of diatom-macroalgal interactions and provide a framework for future investigations into these essential but underexplored communities.<ul><li><span>•</span><span><div>A polyphasic method was developed for studying epiphytic diatoms on <em>Gelidium corneum</em>, combining morphological and molecular tools.</div></span></li><li><span>•</span><span><div>The approach overcomes challenges in diatom characterization, including intricate host morphology and cryptic species identification.</div></span></li><li><span>•</span><span><div>Standardized protocols enhance reproducibility and offer insights into diatom-macroalgal ecological interactions.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103188"},"PeriodicalIF":1.6,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096908","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":"Optimizing colorectal polyp detection and localization: Impact of RGB color adjustment on CNN performance","authors":"Jirakorn Jamrasnarodom ,&nbsp;Pharuj Rajborirug ,&nbsp;Pises Pisespongsa ,&nbsp;Kitsuchart Pasupa","doi":"10.1016/j.mex.2025.103187","DOIUrl":"10.1016/j.mex.2025.103187","url":null,"abstract":"<div><div>Colorectal cancer, arising from adenomatous polyps, is a leading cause of cancer-related mortality, making early detection and removal crucial for preventing cancer progression. Machine learning is increasingly used to enhance polyp detection during colonoscopy, the gold standard for colorectal cancer screening, despite its operator-dependent miss rates. This study explores the impact of RGB color adjustment on Convolutional Neural Network (CNN) models for improving polyp detection and localization in colonoscopic images. Using datasets from Harvard Dataverse for training and internal validation, and LDPolypVideo-Benchmark for external validation, RGB color adjustments were applied, and YOLOv8s was used to develop models. Bayesian optimization identified the best RGB adjustments, with performance assessed using mean average precision (mAP) and F₁-scores. Results showed that RGB adjustment with 1.0 R-1.0 G-0.8 B improved polyp detection, achieving an mAP of 0.777 and an F₁-score of 0.720 on internal test sets, and localization performance with an F₁-score of 0.883 on adjusted images. External validation showed improvement but with a lower F₁-score of 0.556. While RGB adjustments improved performance in our study, their generalizability to diverse datasets and clinical settings has yet to be validated. Thus, although RGB color adjustment enhances CNN model performance for detecting and localizing colorectal polyps, further research is needed to verify these improvements across diverse datasets and clinical settings.<ul><li><span>•</span><span><div><strong>RGB Color Adjustment</strong>: Applied RGB color adjustments to colonoscopic images to enhance the performance of Convolutional Neural Network (CNN) models.</div></span></li><li><span>•</span><span><div><strong>Model Development</strong>: Used YOLOv8s for polyp detection and localization, with Bayesian optimization to identify the best RGB adjustments.</div></span></li><li><span>•</span><span><div><strong>Performance Evaluation</strong>: Assessed model performance using mAP and F₁-scores on both internal and external validation datasets.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103187"},"PeriodicalIF":1.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096629","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":"QuantumNet: An enhanced diabetic retinopathy detection model using classical deep learning-quantum transfer learning","authors":"Manish Bali ,&nbsp;Ved Prakash Mishra ,&nbsp;Anuradha Yenkikar ,&nbsp;Diptee Chikmurge","doi":"10.1016/j.mex.2025.103185","DOIUrl":"10.1016/j.mex.2025.103185","url":null,"abstract":"<div><div>Diabetic Retinopathy (DR), a diabetes-related eye condition, damages retinal blood vessels and can lead to vision loss if undetected early. Precise diagnosis is challenging due to subtle, varied symptoms. While classical deep learning (DL) models like CNNs and ResNet's are widely used, they face resource and accuracy limitations. Quantum computing, leveraging quantum mechanics, offers revolutionary potential for faster problem-solving across fields like cryptography, optimization, and medicine. This research introduces QuantumNet, a hybrid model combining classical DL and quantum transfer learning to enhance DR detection. QuantumNet demonstrates high accuracy and resource efficiency, providing a transformative solution for DR detection and broader medical imaging applications. The method is as follows:<ul><li><span>•</span><span><div>Evaluate three classical deep learning models—CNN, ResNet50, and MobileNetV2—using the APTOS 2019 blindness detection dataset on Kaggle to identify the best-performing model for integration.</div></span></li><li><span>•</span><span><div>QuantumNet combines the best-performing classical DL model for feature extraction with a variational quantum classifier, leveraging quantum transfer learning for enhanced diagnostics, validated statistically and on Google Cirq using standard metrics.</div></span></li><li><span>•</span><span><div>QuantumNet achieves 94.11 % accuracy, surpassing classical DL models and prior research by 11.93 percentage points, demonstrating its potential for accurate, efficient DR detection and broader medical imaging applications.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103185"},"PeriodicalIF":1.6,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096628","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":"Implementation of a practical sand constitutive model coupled with the high cycle accumulation framework in PLAXIS","authors":"Pishun Tantivangphaisal ,&nbsp;David M.G. Taborda ,&nbsp;Stavroula Kontoe","doi":"10.1016/j.mex.2025.103183","DOIUrl":"10.1016/j.mex.2025.103183","url":null,"abstract":"<div><div>A modification of the high-cycle accumulation (HCA) framework coupled with a practical constitutive model for sands and its numerical implementation as a user-defined soil model in PLAXIS is presented. The implemented model is compared against data from the original high-cyclic tests in Karlsruhe fine sand and more recent laboratory tests in Dunkirk sand. A reference 15 MW offshore wind turbine monopile foundation subject to lateral cyclic wave loading is used in an engineering design scenario at three different load levels to verify the current numerical implementation.</div><div>Details include:<ul><li><span>•</span><span><div>Modifications made to the HCA framework to couple it with a practical sand constitutive model,</div></span></li><li><span>•</span><span><div>Implementation of an efficient workflow to switch between low and high cycle constitutive equations in PLAXIS, and</div></span></li><li><span>•</span><span><div>Verification of the implementation at single element and boundary value problem scales.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103183"},"PeriodicalIF":1.6,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096989","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":"Classification of Stages 1,2,3 and Preplus, Plus disease of ROP using MultiCNN_LSTM classifier","authors":"Ranjana Agrawal ,&nbsp;Sucheta Kulkarni ,&nbsp;Madan Deshpande ,&nbsp;Anita Gaikwad ,&nbsp;Rahee Walambe ,&nbsp;Ketan V. Kotecha","doi":"10.1016/j.mex.2025.103182","DOIUrl":"10.1016/j.mex.2025.103182","url":null,"abstract":"<div><div>Retinopathy of prematurity (ROP) is a retinal disorder that can cause blindness in premature infants with low birth weight. Early detection and timely treatment are crucial to prevent blindness associated with ROP. It's essential to identify the stage and presence of Plus disease accurately when examining retinal images of at-risk infants. We are developing an explainable automated ROP screening system for the HVDROPDB datasets. The fundus images were classified as without stage (Normal)/with Stage (ROP) by segmenting the ridge. Stages 1–3 were classified using machine Learning (ML) models.<ul><li><span>•</span><span><div>This study aims to improve accuracy of Stages 1–3 classification and identify Pre-plus/ Plus disease using MultiCNN_LSTM networks. This is accomplished by using multiple CNNs (Convolutional Neural Networks) to extract features and LSTM (Long Short-Term Memory) classifier to classify images.</div></span></li><li><span>•</span><span><div>Cropped STAGE dataset and HVDROPDB-PLUS dataset are constructed with RetCam and Neo images.</div></span></li><li><span>•</span><span><div>The proposed networks outperform individual CNNs and CNN_LSTM networks in terms of accuracy and F1 score.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103182"},"PeriodicalIF":1.6,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135711","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":"A protocol to assess the Knee cartilage thickness in healthy older adults and analyze its correlation with patient-reported outcomes","authors":"Dias Tina Thomas ,&nbsp;Charu Eapen ,&nbsp;Atmananda S. Hegde ,&nbsp;Prajwal Prabhudev Mane ,&nbsp;Ajit R. Mahale","doi":"10.1016/j.mex.2025.103179","DOIUrl":"10.1016/j.mex.2025.103179","url":null,"abstract":"<div><div>Knee osteoarthritis (KOA)is a degenerative joint condition affecting about 240 million people worldwide with rising incidences in India. The progressive nature of the disease leads to pain, reduced mobility, and diminished quality of life. Despite extensive global research, there is a lack of normative data on the cartilage thickness specific to the Indian population, which is crucial to understanding the nature of the disease progression. Thereby this study aims to establish normative cartilage thickness values in healthy Indian adults and correlate these values to the knee injury and osteoarthritis outcome score (KOOS). Using ultrasonography, the cartilage thickness will be measured in 100 healthy individuals. Baseline cartilage values will be linked to the various domains of the KOOS score to evaluate early cartilage degeneration and its impact on function. This research will address the gap in Indian-specific data, including early detection and management of KOA and improving clinical decision-making for better outcomes and quality of life.<ul><li><span>•</span><span><div>Establish normative knee cartilage thickness in healthy Indian population using USG.</div></span></li><li><span>•</span><span><div>Helps identify KOA in early stages through USG-based cartilage thickness evaluation</div></span></li><li><span>•</span><span><div>Enables clinicians to target rehabilitation efforts effectively and potentially improve patients' outcomes and quality of life.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103179"},"PeriodicalIF":1.6,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096553","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":"A novel method to determine background concentrations and spatial distributions of heavy metals in soil at large scale using machine learning coupled with remote sensing-terrain attributes","authors":"Magboul M. Sulieman ,&nbsp;Fuat Kaya ,&nbsp;Abdullah S. Al-Farraj ,&nbsp;Eric C. Brevik","doi":"10.1016/j.mex.2025.103180","DOIUrl":"10.1016/j.mex.2025.103180","url":null,"abstract":"<div><div>Soil heavy metals are among the most hazardous materials in the environment. Their harmful effects can extend to surrounding systems (air, plants, water), and given the appropriate conditions may ultimately have negative effects on human health. Thus, preventing pollution and protecting pristine soils and preindustrial areas from human activities that lead to the concentration of heavy metals (HMs) is a priority. Here, a novel methodology was proposed to establish background concentrations of eight soil HMs, cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn), and digitally map their spatial distributions in an area (i.e., harrats region) that has not yet been impacted by industrial activity. The proposed methodology combined measurements of the target HMs and fifty-two environmental covariates (ECOVs) derived from 2017 to 2021 Landsat 8/9 OLI and Shuttle Radar Topography Mission (SRTM)-derived terrain attributes. Random forest and stepwise multiple linear regression models were further used to digitally map the studied HMs. The methodology is important for any future environmental pollution/monitoring studies in the area and can be applied in other similar environments. Machine learning algorithms show great ability to use available environmental variables and investigate the relationships between the factors influencing HMs accumulation under a given soil environment. The proposed methodology was effective for describing HMs spatial variability in the environments investigated.</div><div><ul><li><span>•</span><span><div>The proposed method is a novel way to predict soil HMs and their spatial distribution over large areas.</div></span></li><li><span>•</span><span><div>Remote sensing/digital elevation models (DEMs)-derived ECOVs are useful for predicting and digitally mapping soil HMs, thus important for future environmental monitoring studies.</div></span></li><li><span>•</span><span><div>Explainable algorithms (i.e., RF and SMLR) are able to utilize ECOVs for HMs prediction and to establish background concentrations over large areas.</div></span></li></ul>Therefore, the combination of machine learning and RS/DEMs-based ECOVs is crucial to overcome the disadvantages of HMs determination via conventional methods.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103180"},"PeriodicalIF":1.6,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096993","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":"Lane and Traffic Sign Detection for Autonomous Vehicles: Addressing Challenges on Indian Road Conditions","authors":"H. S. Gowri Yaamini ,&nbsp;Swathi K J ,&nbsp;Manohar N ,&nbsp;Ajay Kumar G","doi":"10.1016/j.mex.2025.103178","DOIUrl":"10.1016/j.mex.2025.103178","url":null,"abstract":"<div><div>Accurate and precise detection of lanes and traffic signs is predominant for the safety and efficiency of autonomous vehicles and these two significant tasks should be addressed to handle Indian traffic conditions. There are several state-of-art You Only Live Once (YOLO) models trained on benchmark datasets which fails to cater the challenges of Indian roads. To address these issues, the models need to be trained with a wide variety of Indian data samples for the autonomous vehicles to perform better in India. YOLOv8 algorithm has its challenges but gives better precision results and YOLOv8 nano variant is widely used as it is computationally less complex comparatively. Through rigorous evaluations of diverseness in the datasets, the proposed YOLOv8n transfer learning models exhibits remarkable performance with a mean Average Precision (mAP) of 90.6 % and inference speed of 117 frames per second (fps) for lane detection whereas, a notable mAP of 81.3 % for traffic sign detection model with a processing speed of 56 fps.<ul><li><span>•</span><span><div>YOLOv8n Transfer Learning approach by adjusting architecture for lane and traffic sign detection in Indian diverse Urban, Suburban, and Highway scenarios.</div></span></li><li><span>•</span><span><div>Dataset with 22,400 images of normal and complex Indian scenarios include crude weathering of roads, traffic conditions, diverse tropical weather conditions, partially occluded and partially erased lanes, and traffic signs.</div></span></li><li><span>•</span><span><div>The model performance with notable precision and frame wise inference.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103178"},"PeriodicalIF":1.6,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135805","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":"A straightforward Py-GC/MS methodology for quantification of microplastics in tap water","authors":"Alexander Ccanccapa-Cartagena ,&nbsp;Anandu Nair Gopakumar ,&nbsp;Maryam Salehi","doi":"10.1016/j.mex.2025.103173","DOIUrl":"10.1016/j.mex.2025.103173","url":null,"abstract":"<div><div>This study introduces a cost-effective and streamlined Pyrolysis Gas Chromatography-Mass Spectrometry (Py-GC/MS) methodology for detecting and quantifying microplastics in tap water, focusing on seven common polymers. Unlike conventional approaches relying on expensive pyrolyzate libraries, this method identifies pyrolysis fragments by matching their <em>m/z</em> values with commercially available mass spectral libraries (Wiley Registry 12th Edition/NIST 2020) and confirms findings using pure polymer standards. Recovery was evaluated using two approaches, demonstrating that analysis of the entire filter provided more accurate results compared to extrapolation from subsections. The method exhibited excellent linearity for all targeted polymers (R² &gt; 0.996) and achieved detection limits as low as 0.01 µg for polystyrene (PS) and up to 2.59 µg for polyethylene (PE). Application to tap water samples revealed consistent detection of PS, ranging from 2.532 to 2.571 ng/L in morning samples and 0.867 to 1.540 ng/L in afternoon samples, with polypropylene and PE below the limit of quantification (&lt;LOQ). This method provides a reliable, efficient, and cost-effective tool for routine laboratory analysis of microplastics in tap water and other environmental matrices.<ul><li><span>•</span><span><div>A 23-minute Py-GC/MS method efficiently quantifies microplastics in tap water.</div></span></li><li><span>•</span><span><div>Cost-effective strategy using commercially available mass spectral libraries.</div></span></li><li><span>•</span><span><div>Accurate quantification with ng/L sensitivity validated by pure polymer standards.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103173"},"PeriodicalIF":1.6,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096909","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}