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Here, a competitive magnetic nanomechanical sensor (CMNS) is presented for biomarker analysis under severe sample-volume constraints. The method combines capillary-based microcantilever sampling with magnetic-force amplification. Capillary-based sample loading provides a practical technical means for handling and analyzing trace-volume samples. In this platform, target molecules compete with magnetic nanoparticles for binding to recognition probes immobilized on the microcantilever surface. This competition regulates nanoparticle loading on the cantilever and is transduced into amplified differential mechanical deflection under an external magnetic field. This sensing format eliminates the dual-epitope requirement of conventional sandwich assays, making it particularly advantageous for small-molecule targets, while also reducing dependence on target size and structural complexity and remaining applicable to larger biomolecular targets. To elucidate the sensing mechanism and identify the key factors governing the improvement of sensing sensitivity, a mechanics-based theoretical model is established. Compared with conventional stress-mode nanomechanical sensing, CMNS achieves markedly enhanced differential deflection signals and sensitivity through magnetic-force amplification. CMNS enables detection using as little as 950 nL of the sample, and for a representative trace biomarker, interleukin-6 (IL-6) in ocular samples, the minimum detectable concentration reaches as low as 0.1 fg mL\u20131, with further demonstration in clinical vitreous fluid specimens. CMNS therefore provides a minimally invasive and robust strategy for precision diagnostics based on targeted sampling and is readily extendable to trace-biomarker analysis in ocular fluids, embryo assessment, cerebrospinal fluid, and other sample-limited biomedical scenarios.<\/jats:p>","DOI":"10.1021\/acs.analchem.6c02093","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T02:50:29Z","timestamp":1783997429000},"page":"21486-21501","source":"Crossref","is-referenced-by-count":0,"title":["Ultrasensitive\nMagnetic Nanomechanical Sensing Based\non Competitive Mechanism for Detection of Biomarkers in Trace Samples"],"prefix":"10.1021","volume":"98","author":[{"given":"Yipeng","family":"Yang","sequence":"first","affiliation":[{"name":"University of Science and Technology of China , , , ,","place":["Hefei, Anhui, PR China, 230027"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiubo","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China , , , ,","place":["Hefei, Anhui, PR China, 230027"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihan","family":"Qiao","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China , , , ,","place":["Hefei, Anhui, PR China, 230027"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiarui","family":"Gan","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China , , , ,","place":["Hefei, Anhui, PR China, 230027"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Li","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China , , ,","place":["Hefei, China, 230031"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Dong","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China , , , ,","place":["Hefei, Anhui, PR China, 230001"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8359-3363","authenticated-orcid":true,"given":"Qingchuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China , , , ,","place":["Hefei, Anhui, PR China, 230027"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2075-1106","authenticated-orcid":true,"given":"Shangquan","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China , , , ,","place":["Hefei, Anhui, PR China, 230027"]},{"name":"Chinese Academy of Sciences , , 15 Beisihuan West Road , ,","place":["Beijing, China, 100190"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,7,13]]},"reference":[{"key":"2026072808082153700_cit1","doi-asserted-by":"publisher","first-page":"4956","DOI":"10.1109\/JBHI.2025.3543907","article-title":"Enhancing image retrieval\nperformance with generative models in siamese networks","volume":"29","author":"Golfe","year":"2025","journal-title":"IEEE J. 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