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Researcher profile

Zhenzhen Qin

Zhenzhen Qin contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial Intelligencecond-mat.mtrl-sciHuman-Computer Interactionphysics.atom-phphysics.chem-phphysics.class-phphysics.comp-ph

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Published work

4 published item(s)

preprint2026arXiv

Making AI Drafts Count: A Quality Threshold in Audio Description Workflows

Audio description (AD) narrates visual elements in video for blind and low-vision audiences. Recent work has shown that giving novice describers an AI-generated draft to start from helps produce higher-quality AD and lowers the barrier to entry. What remains an open question is how draft quality shapes the editing process. We investigate this through GenAD, an AD generation pipeline that incorporates accessibility guidelines and contextual video information, and RefineAD, an editing interface for human revisions. Human-AI contributions are measured across text, timing, and delivery. In a within-subjects study, we compared authoring from scratch against editing AI drafts of varying quality. GenAD drafts cut completion time by more than half and significantly reduced cognitive load. In contrast, baseline drafts generated from simple, unguided prompts offered only modest benefits, pointing to a minimum quality threshold for effectiveness. Qualitative findings suggest this threshold is content-dependent; as visual complexity increases, so does the quality needed from AI drafts. We propose this as a design principle: effective AI assistance should clear a quality threshold suited to the target content, rather than simply be present.

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preprint2022arXiv

Accessing negative Poisson`s ratio of graphene by machine learning interatomic potentials

The negative Poisson`s ratio (NPR) is a novel property of materials, which enhances the mechanical feature and creates a wide range of application prospects in lots of fields, such as aerospace, electronics, medicine, etc. Fundamental understanding on the mechanism underlying NPR plays an important role in designing advanced mechanical functional materials. However, with different methods used, the origin of NPR is found different and conflicting with each other, for instance, in the representative graphene. In this study, based on machine learning technique, we constructed a moment tensor potential (MTP) for molecular dynamics (MD) simulations of graphene. By analyzing the evolution of key geometries, the increase of bond angle is found to be responsible for the NPR of graphene instead of bond length. The results on the origin of NPR are well consistent with the start-of-art first-principles, which amend the results from MD simulations using classic empirical potentials. Our study facilitates the understanding on the origin of NPR of graphene and paves the way to improve the accuracy of MD simulations being comparable to first-principle calculations. Our study would also promote the applications of machine learning interatomic potentials in multiscale simulations of functional materials. *Author

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preprint2022arXiv

The consistent behavior of negative Poissons ratio with interlayer interactions

Negative Poissons ratio (NPR) is of great interest due to the novel applications in lots of fields. Films are the most commonly used form in practical applications, which involves multiple layers. However, the effect of interlayer interactions on the NPR is still unclear. In this study, based on first principles calculations, we systematically investigate the effect of interlayer interactions on the NPR by comparably studying single-layer graphene, few-layer graphene, h-BN, and graphene-BN heterostructure. It is found that they almost have the same geometry-strain response. Consequently, the NPR in bilayer graphene, triple-layer graphene, and graphene-BN heterostructure are consistent with that in single-layer graphene and h-BN. The fundamental mechanism lies in that the response to strain of the orbital coupling are consistent under the effect of interlayer interactions. The deep understanding of the NPR with the effect of interlayer interactions as achieved in this study is beneficial for the future design and development of micro-/nanoscale electromechanical devices with novel functions based on nanostructures.

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preprint2022arXiv

The record low thermal conductivity of monolayer Cuprous Iodide (CuI) with direct wide bandgap

Two-dimensional materials have attracted lots of research interests due to the fantastic properties that are unique to the bulk counterparts. In this paper, from the state-of-the-art first-principles, we predicted the stable structure of monolayer counterpart of the γ-CuI (Cuprous Iodide), which is a p-type wide bandgap semiconductor. The monolayer CuI presents multifunctional superiority in terms of electronic, optical, and thermal transport properties. Specifically, the ultralow thermal conductivity of 0.116 Wm-1K-1 is predicted for monolayer CuI, which is much lower than γ-CuI (0.997 Wm-1K-1) and other typical semiconductors. Moreover, an ultrawide direct bandgap of 3.57 eV is found in monolayer CuI, which is larger than γ-CuI (2.95-3.1 eV), promoting the applications in nano-/optoelectronics with better optical performance. The ultralow thermal conductivity and direct wide bandgap of monolayer CuI as reported in this study would promise its potential applications in transparent and wearable electronics.

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