ArticleACS central science2020
Single-Nanoparticle Orientation Sensing by Deep Learning.
Article in ACS central science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed, 21 citations in OpenAlex.
- Seeing Beyond RGB Capabilities: Data-Driven and Physics-Guided Broadband Spectral Extrapolation of Plasmonic Nanostructures by Deep Learning.ACS photonics · 2026Article
- Machine learning reshapes the paradigm of nanomedicine research.Acta pharmaceutica Sinica. B · 2026Review
- Deep Learning Integration in Optical Microscopy: Advancements and Applications.Microscopy research and technique · 2026Review
- Dipole Determination by Polarimetric Spectroscopy Yielding the Orientation of Gold Nanorods.Small science · 2025Article
- Emergence and impact of theranostic-nanoformulation of triple therapeutics for combination cancer therapy.Smart medicine · 2024Review
- Ligand Separation on Nanoconstructs Affects Targeting Selectivity to Protein Dimers on Cell Membranes.Nano letters · 2024Article
- Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?Nano today · 2022Article
- Hyperspectral dark field optical microscopy for orientational imaging of a single plasmonic nanocube using a physics-based learning method.Nanoscale advances · 2022Article
- Optical Microscopy Systems for the Detection of Unlabeled Nanoparticles.International journal of nanomedicine · 2022Review
- Coal Wall and Roof Segmentation in the Coal Mine Working Face Based on Dynamic Graph Convolution Neural Networks.ACS omega · 2021Article
- Nanoparticle Shape Determines Dynamics of Targeting Nanoconstructs on Cell Membranes.Journal of the American Chemical Society · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
This paper describes a computational imaging platform to determine the orientation of anisotropic optical probes under differential interference contrast (DIC) microscopy. We established a deep-learning model based on data sets of DIC images collected from metal nanoparticle optical probes at different orientations. This model predicted the in-plane angle of gold nanorods with an error below 20°, the inherent limit of the DIC method. Using low-symmetry gold nanostars as optical probes, we demonstrated the detection of in-plane particle orientation in the full 0-360° range. We also showed that orientation predictions of the same particle were consistent even with variations in the imaging background. Finally, the deep-learning model was extended to enable simultaneous prediction of in-plane and out-of-plane rotation angles for a multibranched nanostar by concurrent analysis of DIC images measured at multiple wavelengths.
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.