Precision ChemistryPub Date : 2025-07-03eCollection Date: 2025-09-22DOI: 10.1021/prechem.5c00033
Ruofan Yang, Zhengwei Zhang, Xiang Lan, Rong Wu, Fangping Ouyang, Jun He
{"title":"Progress and Challenges in the Synthesis of Two-Dimensional Lateral Heterostructures.","authors":"Ruofan Yang, Zhengwei Zhang, Xiang Lan, Rong Wu, Fangping Ouyang, Jun He","doi":"10.1021/prechem.5c00033","DOIUrl":"10.1021/prechem.5c00033","url":null,"abstract":"<p><p>Two-dimensional (2D) lateral heterostructures, an interesting class of nanostructures, have shown great promise in optoelectronics and nanoelectronics due to their unique electronic and optical properties. In recent years, significant progress has been made in the controlled growth of 2D lateral heterostructures. However, challenges remain in areas such as material selection and compatibility, interface quality, and precise control over the growth process. High-quality interfaces are critical for the optoelectronic performance of these heterostructures, yet ensuring uniformity and consistency during fabrication continues to be a major obstacle. This review provides a comprehensive overview of the recent developments in the controlled growth of 2D lateral heterostructures. It examines the fabrication methods for various types of 2D lateral heterostructures and their associated challenges. The review also discusses the properties and potential applications of these heterostructures, aiming to offer a deeper understanding of their preparation, characteristics, and future prospects. By identifying existing challenges and opportunities in the fabrication process, this work seeks to guide future advancements in the field and support the efficient large-scale production of high-quality 2D lateral heterostructures.</p>","PeriodicalId":29793,"journal":{"name":"Precision Chemistry","volume":"3 9","pages":"492-515"},"PeriodicalIF":6.2,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12458039/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145151042","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}
Precision ChemistryPub Date : 2025-07-01eCollection Date: 2025-09-22DOI: 10.1021/prechem.5c00028
Célia Sahli, Tiffany Thanhtruc Pham, Kenry
{"title":"Machine-Learning-Assisted Analysis of Patient Clinical Biomarkers to Improve Ovarian Cancer Diagnosis.","authors":"Célia Sahli, Tiffany Thanhtruc Pham, Kenry","doi":"10.1021/prechem.5c00028","DOIUrl":"10.1021/prechem.5c00028","url":null,"abstract":"<p><p>The unavailability of accurate and reliable methods for early ovarian cancer detection represents a major gap in ovarian cancer diagnosis and management. The emergence and recent integration of machine learning with cancer diagnostic techniques, particularly biomarker-based blood tests, have the potential to improve the selectivity and sensitivity of ovarian cancer detection substantially. Herein, we leverage a series of machine learning and statistical approaches to analyze clinically relevant data sets of more than 300 patients with ovarian tumors and 47 blood-obtained features to distinguish between cancerous and benign tumors. We found that HE4, CA125, menopausal status, and age were some of the most important features distinguishing cancerous from benign ovarian tumors in all patient populations. Age was noted to be a critical feature with cancer discriminatory power only in premenopausal patients but less so in postmenopausal patients. Systematic consideration of patient menopausal status, types of machine learning algorithms, and number of clinical features is necessary prior to ovarian cancer screening to yield more accurate and reliable diagnostic results. Overall, this study provides deeper insight into the use of machine learning, feature selection, and other relevant quantitative approaches to advance ovarian cancer diagnosis to improve patient outcomes.</p>","PeriodicalId":29793,"journal":{"name":"Precision Chemistry","volume":"3 9","pages":"554-566"},"PeriodicalIF":6.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12458010/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145151124","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}
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