ArticlePNAS nexus2026
Reconstruction of 3D crystal growth from transmission optical microscopy images.
Article in PNAS nexus, 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
No grant is acknowledged in the PubMed record.
Abstract
Variations in the 3D shapes of crystalline materials can greatly affect their physical chemical properties, influencing the downstream processes required for their formulation, precision manufacturing, and transportation. Under different growth conditions, identical compounds can produce a wide variety of particle shapes and sizes, yet a comprehensive understanding of the relationship between crystallization conditions and final product properties is severely lacking. In part, this reflects the technical challenges associated with accurately recovering 3D crystal shape information during their growth. Here, we present a novel method for reconstructing the evolving 3D polyhedral shape of a single crystal in a growth cell from a sequence of transmission optical microscopy images. Our approach combines deep learning with synthetic image generation based on theoretically grounded crystallography together with accurate simulation of the refraction of light at the crystal faces, yielding robust estimates of the dynamics of the crystal shape evolution during the crystallization process. We demonstrate our approach by tracking the 3D growth and shape development of the polyhedral
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.