ArticleACS nano2026
Step-Scan Interferometry for High-Fidelity Hyperspectral Nanoscopy.
Article in ACS nano, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Fourier transform infrared nanospectroscopy (nano-FTIR) is an increasingly adopted characterization method that leverages decades of established knowledge in infrared spectroscopy at the nanoscale. It enables detailed characterization of composite materials and nanophotonic systems. Besides the rapid adoption and tremendous possibilities, the nanoscale nature of these measurements poses additional challenges for infrared spectroscopy. The current implementations of hyperspectral image acquisition at high spatial resolution suffer from significant artifacts due to thermal instabilities, which heavily affect positioning. As a result, the spatial and spectral fidelity of the measurements can be unreliable for long acquisitions. Here, we propose an alternative nano-FTIR measurement methodology based on step-scan interferometry and image registration. We demonstrate the spatial and spectral fidelity of our method and show how it enables the collection of high-quality, large data sets, making it possible to apply machine learning in photonics research to characterize nanoscale heterogeneity.
Indexed as
Identifiers
What 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.