[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2149":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"view_count":31,"doi":32,"paper":33,"created_at":48},2149,"Smart biosensing ecosystems: Integrating nanotechnology, chemometrics, and machine learning for precision analytical applications","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.nxmate.2026.103476","The growing complexity of food, environmental, and biological matrices has intensified demand for analytical technologies that are rapid, selective, and deployable outside centralized laboratories, exposing the limitations of conventional chromatographic and single-analyte biosensing approaches. This review introduces the concept of a Smart Biosensing Ecosystem, a framework in which biorecognition elements (enzymes, antibodies, nucleic acids, aptamers, whole cells), functional nanomaterials, and electrochemical or optical transducers are integrated with chemometric preprocessing, machine-learning-based interpretation, IoT connectivity, and cloud-enabled decision support into a single analytical pipeline. Within this framework, the review examines smart biosensor architecture, biorecognition mechanisms, enzyme-based catalytic sensing, electrochemical and optical transduction physics, and a comparative evaluation of graphene, MXenes, carbon nanotubes, quantum dots, and metal-based nanomaterials for signal amplification and multiplexing. It further surveys chemometric tools (PCA, PLS, LDA) and machine-learning algorithms (SVM, Random Forest, ANN, CNN, gradient boosting, ensemble learning) for model development and validation, alongside wearable, multiplexed, and hybrid electrochemical–optical platforms applied to food-quality, environmental, and biomedical analysis. Persistent challenges, including biofouling, batch-to-batch reproducibility, scalable manufacturing, regulatory translation, explainable AI, and data governance, are critically discussed as barriers limiting laboratory-to-market translation. The review concludes that continued convergence of nanotechnology, intelligent analytics, and connected infrastructure is poised to transform biosensors from isolated transduction devices into autonomous, self-calibrating, and predictive analytical ecosystems with substantial translational potential across precision food, environmental, and biomedical monitoring.","食品、环境和生物基质的日益复杂，加剧了对快速、选择性且可在中心实验室之外部署的分析技术的需求，也暴露了传统色谱方法和单分析物生物传感方法的局限性。本综述引入了“智能生物传感生态系统”的概念，该框架将生物识别元件（酶、抗体、核酸、适配体、全细胞）、功能纳米材料以及电化学或光学换能器，与化学计量学预处理、基于机器学习的解析、物联网连接和云端决策支持整合为单一分析流程。在该框架内，本综述考察了智能生物传感器架构、生物识别机制、基于酶的催化传感、电化学与光学换能物理，并对石墨烯、MXenes、碳纳米管、量子点和金属基纳米材料在信号放大与多重检测方面进行了比较评估。文章进一步综述了用于模型开发与验证的化学计量学工具（PCA、PLS、LDA）和机器学习算法（SVM、随机森林、ANN、CNN、梯度提升、集成学习），以及应用于食品质量、环境和生物医学分析的可穿戴、多重和混合电化学–光学平台。文章批判性地讨论了持续存在的挑战，包括生物污损、批次间重现性、可规模化制造、法规转化、可解释人工智能和数据治理，这些是限制实验室到市场转化的障碍。综述得出结论：纳米技术、智能分析和互联基础设施的持续融合，有望将生物传感器从孤立的换能设备转变为自主、自校准和预测性分析生态系统，在精准食品、环境和生物医学监测方面具有巨大的转化潜力。",null,"Next Materials","2026-09-09T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,14,8,1,"综述提出智能生物传感生态系统框架，整合纳米材料、化学计量学与机器学习，对农产品质量与食品安全精准检测有较强参考价值，但属实验室前沿综述，产业落地尚远。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","机器学习","食品安全","纳米材料","农业传感器",0,"10.1016\u002Fj.nxmate.2026.103476",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":39,"card":40,"direction":46,"ingested_from":47},"W7212013524",[36],{"name":37,"orcid":38},"Kushagra Sharma","https:\u002F\u002Forcid.org\u002F0000-0003-1126-8697","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2949822826018939\u002Fpdf",{"tldr":41,"method":42,"finding":43,"direction":44,"opportunity":45},"综述提出智能生物传感生态系统，融合纳米材料、化学计量学、机器学习与物联网，实现精准分析。","综述生物识别、纳米材料、电化学\u002F光学传感、PCA\u002FPLS及SVM\u002FCNN等机器学","纳米技术、智能分析与互联基础设施融合，可将生物传感器变为自主预测性分析生态系统。","农业人工智能与决策模型","可探索面向农业食品链的智能生物传感生态系统，解决生物污损、可解释AI与数据治理等落地瓶颈。","智慧农业 \u002F 农业物联网","openalex","2026-09-11T23:30:12.070495Z"]