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Nanoenergy Advances
Volume 6
Issue 2
10.3390/nanoenergyadv6020016
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Article

Atomic-Scale Rigidity of NTO Molecular Chains Under Perturbation Investigated Using Deep Learning

by
Lingtao Zhan
1,
Tingting Wang
1,
Xiongbai Cao
1,
Jiale Zhu
1,
Huixia Yang
1,
Quanzhen Zhang
1,
Cesare Grazioli
2,
Liwei Liu
1,
Teng Zhang
1,* and
Yeliang Wang
1
1
School of Integrated Circuits and Electronics & Yangtze Delta Region Academy, Beijing Institute of Technology (BIT), Beijing 100081, China
2
CNR—Istituto Officina dei Materiali (IOM), S.S. 14 Km 163.5, 34149 Trieste, Italy
*
Author to whom correspondence should be addressed.
Nanoenergy Adv. 2026, 6(2), 16; https://doi.org/10.3390/nanoenergyadv6020016
Submission received: 27 February 2026 / Revised: 28 April 2026 / Accepted: 7 May 2026 / Published: 12 May 2026
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Abstract

The mechanical sensitivity of energetic materials is closely linked to the stability of their microstructures; however, in situ observation of their dynamic response under external mechanical stimuli at the atomic scale remains challenging. Here, we propose a deep-learning-based intelligent analysis method for scanning tunneling microscopy (STM) images of a next-generation insensitive energetic material 3-nitro-1,2,4-triazol-5-one (NTO). We design SpecMol, a lightweight segmentation network with frequency-domain awareness, which achieves high-precision segmentation and orientation recognition of individual NTO molecules in adsorption images. Building upon this, we apply localized external forces to one-dimensional NTO nanochains via in situ STM tip manipulation and quantitatively analyze the geometric evolution of their fundamental building blocks—dimers. Experimental results reveal that, following mechanical perturbation, the relative orientation angle within the dimer (averaging approximately 14.55°) remains highly stable (CCC = 0.834), confirming the remarkable structural rigidity of NTO dimers. This study provides, for the first time, direct microscopic evidence at real-space atomic resolution for the low mechanical sensitivity of NTO, elucidating that its exceptional local structural stability originates from rigid dimeric units stabilized by an extensive hydrogen-bonding network. Our findings not only deepen the fundamental understanding of the safety performance of energetic materials but also demonstrate the powerful potential of integrating artificial intelligence with advanced characterization techniques for molecular-scale functional materials research.
Keywords: scanning tunneling microscopy (STM); deep learning; 2D materials scanning tunneling microscopy (STM); deep learning; 2D materials

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MDPI and ACS Style

Zhan, L.; Wang, T.; Cao, X.; Zhu, J.; Yang, H.; Zhang, Q.; Grazioli, C.; Liu, L.; Zhang, T.; Wang, Y. Atomic-Scale Rigidity of NTO Molecular Chains Under Perturbation Investigated Using Deep Learning. Nanoenergy Adv. 2026, 6, 16. https://doi.org/10.3390/nanoenergyadv6020016

AMA Style

Zhan L, Wang T, Cao X, Zhu J, Yang H, Zhang Q, Grazioli C, Liu L, Zhang T, Wang Y. Atomic-Scale Rigidity of NTO Molecular Chains Under Perturbation Investigated Using Deep Learning. Nanoenergy Advances. 2026; 6(2):16. https://doi.org/10.3390/nanoenergyadv6020016

Chicago/Turabian Style

Zhan, Lingtao, Tingting Wang, Xiongbai Cao, Jiale Zhu, Huixia Yang, Quanzhen Zhang, Cesare Grazioli, Liwei Liu, Teng Zhang, and Yeliang Wang. 2026. "Atomic-Scale Rigidity of NTO Molecular Chains Under Perturbation Investigated Using Deep Learning" Nanoenergy Advances 6, no. 2: 16. https://doi.org/10.3390/nanoenergyadv6020016

APA Style

Zhan, L., Wang, T., Cao, X., Zhu, J., Yang, H., Zhang, Q., Grazioli, C., Liu, L., Zhang, T., & Wang, Y. (2026). Atomic-Scale Rigidity of NTO Molecular Chains Under Perturbation Investigated Using Deep Learning. Nanoenergy Advances, 6(2), 16. https://doi.org/10.3390/nanoenergyadv6020016

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