Sub-monolayer tracking of molecular states at buried interfaces
Abstract
Phenomena responsible for emerging technologies originate from novel molecular states at buried interfaces. Yet quantitatively resolving these features is out of reach in most cases. Resonant soft X-ray scattering (RSoXS) has demonstrated sensitivity to these local states but is limited due to a lack of accurate interpretive models and spatial resolution. Here we demonstrate a machine learning (ML)-optimized optical model that establishes the direct and quantitative interpretation of molecular orientation, chemical identity and packing states with sub-monolayer resolution - reaching beyond the diffraction limit. We discover a gradient evolution of molecular orientation and packing emerging from a pinned state directly at the channel-dielectric interface of an organic transistor, indicative of competing surface versus bulk growth dynamics that contradict models based on substrate patterning. This non-destructive insight into buried interfacial formation and final configuration opens possibilities in controlling interfacial states and phenomena in next-generation technologies.
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BibTeX
@misc{heilman2026submonolayer,
title = {{Sub-monolayer tracking of molecular states at buried interfaces}},
author = {Harlan Heilman and Em Biegler and Sushil Adhikari and Victor Murcia and Tanner Melody and Fred Woodall and Lars Thomsen and Obaid Alqahtani and Brian A. Collins},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.31924},
eprint = {2610.31924},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.31924},
}
RIS
TY - UNPB
TI - Sub-monolayer tracking of molecular states at buried interfaces
AU - Harlan Heilman
AU - Em Biegler
AU - Sushil Adhikari
AU - Victor Murcia
AU - Tanner Melody
AU - Fred Woodall
AU - Lars Thomsen
AU - Obaid Alqahtani
AU - Brian A. Collins
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.31924
UR - https://arxiv.org/abs/2610.31924
AB - Phenomena responsible for emerging technologies originate from novel molecular states at buried interfaces. Yet quantitatively resolving these features is out of reach in most cases. Resonant soft X-ray scattering (RSoXS) has demonstrated sensitivity to these local states but is limited due to a lack of accurate interpretive models and spatial resolution. Here we demonstrate a machine learning (ML)-optimized optical model that establishes the direct and quantitative interpretation of molecular orientation, chemical identity and packing states with sub-monolayer resolution - reaching beyond the diffraction limit. We discover a gradient evolution of molecular orientation and packing emerging from a pinned state directly at the channel-dielectric interface of an organic transistor, indicative of competing surface versus bulk growth dynamics that contradict models based on substrate patterning. This non-destructive insight into buried interfacial formation and final configuration opens possibilities in controlling interfacial states and phenomena in next-generation technologies.
ER -