arXiv subjects

Yunlong Fan

Publications and source records attributed to Yunlong Fan.

4 recordsLinked to original sources

arXiv2026

An encoded asymmetric ligand for metal-selective topological assembly of two-dimensional metal-organic frameworks

Two-dimensional metal-organic frameworks (2D MOFs), with diverse topological architectures, provide a powerful platform for exploring unconventional electronic and lattice-dynamical responses. Yet their structural diversity remains fundamentally constrained by the fixed geometry of high-symmetry ligands. Here, we introduce an encoded asymmetric ligand, benzo[b]triphenylene-2,3,6,7,11,12-hexaol (BTH), for metal-selective topological assembly. By integrating multi-site coordination fields with sterically differentiated environments, BTH exhibits distinct topological programmability: different divalent metal ions direct divergent framework architectures. Specifically, coordination of BTH with divalent Cu(II) and Zn(II) ions assembles Cu-BTH-MOF with a dual-mode hexagonal pore topology and Zn-BTH-MOF with uniform hexagonal channels, respectively, as supported by PXRD Pawley refinement, structural simulations, and pore-size distribution analysis. Furthermore, this topological divergence is accompanied by a significant divergence in charge-transport properties, with Cu-BTH-MOF reaching an electrical conductivity of 1.186 x 10-3 S cm-1, more than six orders of magnitude higher than that of Zn-BTH-MOF (3.38 x 10-10 S cm-1). This work establishes ligand desymmetrization as a programmable strategy for metal-selective topological diversification in 2D MOFs.

cond-mat.mtrl-sci↗

arXiv2026

Atomic-scale origin of charge density wave-driven metal-semiconductor transition in an incommensurately modulated metal-organic framework

The intrinsic incommensurate charge density wave in metal-organic frameworks has remained elusive due to the lack of direct evidence linking atomic-scale structural modulation to macroscopic electronic properties. Using high-quality Pr3HHTP2 (HHTP = 2,3,6,7,10,11-hexahydroxytriphenylene) single crystals as a model system, we precisely resolve, for the first time, the incommensurately modulated structure of a conductive metal-organic framework at 100 K (modulation vector q = 0.39143(12) c*) via temperature-dependent single-crystal X-ray diffraction. The subsequent observation of a reversible metal-semiconductor transition around 350 K, which perfectly synchronizes with the disappearance of the structural modulation, provides convincing evidence for the electronic origin of the lattice distortion. Guest water molecules stabilize the modulated phase by synergistically regulating the relative rotation of the linkers and the interlayer spacing, thereby optimizing the inter-linker interactions. This work establishes a concrete experimental criterion for one-dimensional charge density wave in metal-organic frameworks and provides an ideal platform for probing coupled electronic-lattice modulations.

cond-mat.mtrl-sci↗

arXiv2024

NatLan: Native Language Prompting Facilitates Knowledge Elicitation Through Language Trigger Provision and Domain Trigger Retention

Multilingual large language models (MLLMs) do not perform as well when answering questions in non-dominant languages as they do in their dominant languages. Although existing translate-then-answer methods alleviate this issue, the mechanisms behind their effectiveness remain unclear. In this study, we analogize the dominant language of MLLMs to the native language of humans and use two human cognitive features: the Language Trigger (LT) and the Domain Trigger (DT), to interpret the mechanisms behind translate-then-answer methods. This reveals that while sufficient LTs are provided by these methods, there remains a deficiency in DT retention. To mitigate this issue, we propose Native Language Prompting (NatLan), employing a Multi-MLLM collaboration strategy and introducing an additional role-enhanced domain-specific MLLM with stronger multilingual understanding capabilities as the translator. Across five language QA benchmarks, NatLan achieves up to a 31.28% improvement in accuracy and, compared to existing state-of-the-art methods, provides comparable or greater retention of DTs in up to 87% of cases. Our code is available at https://github.com/AnonyNLP/NatLan.

cs.CL↗

arXiv2022

A Higher-Order Semantic Dependency Parser

Higher-order features bring significant accuracy gains in semantic dependency parsing. However, modeling higher-order features with exact inference is NP-hard. Graph neural networks (GNNs) have been demonstrated to be an effective tool for solving NP-hard problems with approximate inference in many graph learning tasks. Inspired by the success of GNNs, we investigate building a higher-order semantic dependency parser by applying GNNs. Instead of explicitly extracting higher-order features from intermediate parsing graphs, GNNs aggregate higher-order information concisely by stacking multiple GNN layers. Experimental results show that our model outperforms the previous state-of-the-art parser on the SemEval 2015 Task 18 English datasets.

cs.CL↗