Juanjuan Liu, Ruijia Sun, Xuan Bao, Jiefu Yang, Yanling Chen, Bijun Tang, Zheng Liu
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引用次数: 0
摘要
香料在人们的日常生活中发挥着至关重要的作用。它的重要性横跨从治疗到个人护理等各个领域,因此了解和准确识别香味至关重要。要充分利用香味的潜力,就必须进行高效、精确的香味感应和识别。然而,目前的香味传感器面临着一些限制,特别是在高精度地检测和区分复杂的香味特征方面。为了应对这些挑战,在香味传感器中使用原子薄材料已成为一种开创性的方法。这些原子超薄传感器的特点是灵敏度和选择性更强,与传统传感技术相比有显著改进。此外,机器学习(ML)与香味传感的结合也为该领域带来了新的机遇。应用于香味传感的 ML 算法促进了四个关键领域的进步:准确识别香味、精确区分不同香味、提高微妙香味的检测阈值以及预测香味特性。这篇综合评论深入探讨了原子薄材料和 ML 在香味传感中的协同应用,深入分析了这些技术如何在这一领域掀起革命,并深入探讨了它们在增强对香味的理解和利用方面的当前应用和未来潜力。
Machine Learning Driven Atom-Thin Materials for Fragrance Sensing.
Fragrance plays a crucial role in the daily lives. Its importance spans various sectors, from therapeutic purposes to personal care, making the understanding and accurate identification of fragrances essential. To fully harness the potential of fragrances, efficient and precise fragrance sensing and identification are necessary. However, current fragrance sensors face several limitations, particularly in detecting and differentiating complex scent profiles with high accuracy. To address these challenges, the use of atom-thin materials in fragrance sensors has emerged as a groundbreaking approach. These atom-thin sensors, characterized by their enhanced sensitivity and selectivity, offer significant improvements over traditional sensing technology. Moreover, the integration of Machine Learning (ML) into fragrance sensing has opened new opportunities in the field. ML algorithms applied to fragrance sensing facilitate advancements in four key domains: accurate fragrance identification, precise discrimination between different fragrances, improved detection thresholds for subtle scents, and prediction of fragrance properties. This comprehensive review delves into the synergistic use of atom-thin materials and ML in fragrance sensing, providing an in-depth analysis of how these technologies are revolutionizing the field, offering insights into their current applications and future potential in enhancing the understanding and utilization of fragrances.
期刊介绍:
Small serves as an exceptional platform for both experimental and theoretical studies in fundamental and applied interdisciplinary research at the nano- and microscale. The journal offers a compelling mix of peer-reviewed Research Articles, Reviews, Perspectives, and Comments.
With a remarkable 2022 Journal Impact Factor of 13.3 (Journal Citation Reports from Clarivate Analytics, 2023), Small remains among the top multidisciplinary journals, covering a wide range of topics at the interface of materials science, chemistry, physics, engineering, medicine, and biology.
Small's readership includes biochemists, biologists, biomedical scientists, chemists, engineers, information technologists, materials scientists, physicists, and theoreticians alike.