ArticleNature photonics2025
Trionic All-optical Biological Voltage Sensing via Quantum Statistics.
Article in Nature photonics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Optical Detection of Cellular Signals at Material Interfaces.Advanced healthcare materials · 2026Review
- Review
- Neural vs Neuromorphic Interfaces: Where Are We Standing?Chemical reviews · 2025Review
- Bioelectricity, Moving On….Bioelectricity · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Quantum confinement in monolayer semiconductors results in optical properties intricately linked to electrons, which can be manipulated by external electric fields. These optoelectronic features offer untapped potential for studying biological electrical activity. In addition to their relatively high quantum yields, picosecond level emission lifetimes make these materials particularly promising for monitoring biological voltages with high spatiotemporal resolution. To this end, we investigate exciton to trion conversion in angstrom thick semiconductors to experimentally demonstrate label-free, dual-polarity, all-optical detection of electrical activity, via changes in photoluminescence, in cardiomyocyte cultures with ultrahigh temporal resolution. We devise a physical model to elucidate that this conversion process is inherently governed by the quantum statistics of the background electrons induced by biological activity. We show that monolayer MoS
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.