ReviewDiscover nano2026
Graphene-based nanomaterials in biosensing: current advances, challenges, and future directions for precision diagnostics.
Review in Discover nano, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundGraphene-based nanomaterials (GBNs), including graphene oxide, reduced graphene oxide, graphene quantum dots, and their composites, combine ultra-high surface area, superior conductivity, and tunable optical properties, making them powerful materials for ultra-sensitive biosensing. AIM AND SCOPE: Unlike earlier descriptive, platform-specific surveys, this review is organised around structure-property-performance relationships and applies a uniform critical lens to the primary literature.
methodsWe examine optical (fluorescence, surface plasmon resonance, colorimetric, SERS), electrochemical, and field-effect transistor (FET) biosensors. We compare them head-to-head on sensitivity, reproducibility, scalability, and clinical readiness. KEY
findingsGBN platforms detect pathogens such as SARS-CoV-2, cancer biomarkers, metabolites, and environmental toxins with high sensitivity and selectivity. However, many reported ultra-low detection limits are extrapolated from buffer calibrations rather than measured, and they are rarely validated in blinded clinical cohorts. Some are also physically implausible given the number of target molecules in the assay volume. CHALLENGES: Key barriers are cytotoxicity, non-specific binding, irreproducible synthesis, and regulatory validation. For FETs specifically, the main obstacles are Debye screening, baseline drift, and poor wafer-scale yield.
conclusionsAI-assisted signal processing, wearable and implantable devices, and 3D-printed multi-analyte lab-on-a-chip systems are critical future directions. Addressing these challenges through interdisciplinary effort could make GBN biosensors a foundation for personalized, point-of-care healthcare.
Indexed as
Identifiers
42918966What OpenQuestion holds
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.