arXiv subjects

Jiaming Gu

Publications and source records attributed to Jiaming Gu.

16 recordsLinked to original sources

arXiv2026

Giant bulk photovoltaic effect driven by interfacial symmetry breaking in MoS2/Ta2NiSe5 heterostructures

Van der Waals (vdW) heterostructures offer a versatile platform for engineering unconventional bulk photovoltaic (BPV) effect through interfacial symmetry breaking. However, the coexistence of multiple photophysical mechanisms, driven by structural complexity, spontaneous charge transfer, and strong interlayer coupling, often obscures the microscopic origin of the BPV response and hinders its rational optimization. Here, we demonstrate a pronounced BPV effect localized at the overlap region of a cross-bar MoS2/Ta2NiSe5 vdW heterostructure, where symmetry breaking induced by vertical stacking lifts the inversion center of MoS2. The orthogonal device geometry enables the independent probing of intralayer and interfacial photoresponse pathways, facilitating clear separation of competing mechanisms. Spontaneous interfacial charge transfer between MoS2 and Ta2NiSe5 further establishes a strong interlayer electronic coupling. By modulating the interlayer potential landscape through gate voltage and vertical electric fields, we achieve an optimized zero-bias photocurrent density of 247 A/cm2 and a BPV coefficient of 0.99 V-1. Supported by theoretical modelling, our results illustrate how minimalist device geometry can transform complex heterostructures into experimentally tractable platforms. This strategy paves the way for analyzing and optimizing interface-driven BPV effect, with implications for self-powered optoelectronics, broadband photodetection, and energy-harvesting nanodevices.

cond-mat.mes-hall↗

arXiv2026

Giant-exchange-driven Vectorial Control of a Minimal Topological Magnet in Eu3In2As4

The interplay between magnetism and band topology provides a route to controlling quantum states of matter, yet its realization in materials is often constrained by weak exchange coupling and complex electronic structures. Here, a giant exchange coupling is identified in the newly predicted topological magnet Eu3In2As4, giving rise to magnetization-dependent band shifts of up to 300 meV. Together with its intrinsically soft magnetic response, this strong cou-pling enables systematic tuning of topological phases by both the magnitude and orientation of applied magnetic fields. The magneto-topological phase diagram is mapped out in which an antiferromagnetic topological insulator ground state evolves, under modest fields, into a pro-posed intermediate 2/3-ferrimagnetic phase, and further into fully polarized ferromagnetic states predicted to host either Weyl or nodal-ring semimetals. Notably, the Weyl phase corresponds to a minimal model hosting a single pair of Weyl nodes. Quantum oscillations, anomalous Hall transport and magneto-infrared spectroscopy consistently reveal exchange-driven band recon-struction across these transitions. Rotation of the magnetization theoretically provides an effi-cient means to tune the momentum-space positions and separations of the Weyl nodes. These results establish Eu3In2As4 as a model system for exploring how strong exchange coupling can be used to control topological band structures with minimal complexity.

cond-mat.mtrl-sci↗

arXiv2026

ProtoAct: Turning Wet-Lab Protocols into Embodied Robotic Actions

Biological wet-lab protocols are written for trained researchers and often leave routine operations, state-dependent conditions, and contextual parameters implicit, making them difficult to translate into robot-executable actions. We present ProtoAct, a structured protocol-grounding framework that converts free-form biological procedures into state-aware, embodiment-ready action sequences. ProtoAct uses ProtoRAG to retrieve manually annotated examples for context-sensitive parsing, employs RefineChecker to detect and revise missing or inconsistent steps, and applies ActSchema to map the refined procedure into constrained JSON function sequences. We further introduce BioP2E, for which we manually annotate 22 cell-culture protocols into 258 monitoring conditions, 910 executable subtasks, and 962 grounded action calls. Evaluation across seven large language models demonstrates that ProtoAct can be effectively instantiated with different backbones. Ablations confirm that retrieval, posterior checking, and schema constraints make complementary contributions. The parsed subtasks further support demonstration collection and VLA model training, enabling successful execution in both simulation and real-robot settings. ProtoAct thus provides a practical interface between biological protocol understanding and embodied robotic execution.

cs.RO↗

arXiv2026

BioVLN: A Simulation Platform for Visual Language Navigation in Biomedical Laboratories

Biomedical laboratory robots must navigate to instruments before performing experimental procedures. Existing embodied navigation platforms are designed for household environments and treat a target as an object center or an arbitrary nearby position. This representation is inadequate for laboratory instruments, which must be approached from their operating side while maintaining safe clearance from surrounding equipment. We introduce BioVLN, a simulation platform for developing and evaluating visual-language navigation agents in biomedical laboratories. BioVLN represents each instrument with three regions: its physical body, a surrounding clearance region, and an operation area in front of the usable side. This model is applied consistently to scene generation, target placement, navigation evaluation, and safety analysis, so success depends on reaching a position from which the instrument can be accessed. BioVLN supports procedural scene generation and manually designed environments, producing 47 scenes and 1667 episodes. Standardized navigation and reinforcement-learning interfaces enable trajectory collection and policy training. Experiments show that geometric exploration reaches 74.4--87.5% success, while sampling multiple valid positions in the operation area improves success to 83.3--92.5% and reduces unsafe proximity.

cs.RO↗

arXiv2026

Pipette: An Embodied Simulation Platform, Benchmark, and Data-Efficient Augmentation Framework for Wet-Lab Robotics

Wet-lab robots can improve the reproducibility, throughput, and safety of biomedical experiments, but scaling their learning requires customizable simulators for safe and reproducible task generation, open editable laboratory assets, and efficient pipelines that turn limited demonstrations into usable training data. We present Pipette, an embodied simulation platform, benchmark, and data-efficient augmentation framework for wet-lab robot learning. Pipette provides over 100 open-source and re-editable wet-lab assets through an extensible asset-building pipeline with built-in Tencent Hunyuan support for text- and image-conditioned 3D asset generation, and supports three robotic-arm embodiments through a unified simulation interface for task construction, data collection, augmentation, and evaluation. A key component of Pipette is its simulation-based data augmentation pipeline, which replays human demonstrations in simulation, applies lighting, camera, speed, and action perturbations, and filters generated episodes with automatic task success checks, rapidly expanding usable training data from limited manual demonstrations. We further introduce a 12-task wet-lab embodied benchmark covering sample handling, culture-ware manipulation, device operation, and precision placement. With only 30 demonstrations per task, ACT achieves 60.3% average success rate, while simulation augmentation improves SmolVLA from 40.4% to 71.8% and pi0 from 37.3% to 44.1%, validating the effectiveness of Pipette for data-efficient VLA training and evaluation. Pipette also supports natural-language-driven scene construction and task registration, lowering the barrier for non-expert users to define new wet-lab robotic tasks

cs.RO↗

arXiv2026

BioProVLA-Agent: An Affordable, Protocol-Driven, Vision-Enhanced VLA-Enabled Embodied Multi-Agent System with Closed-Loop-Capable Reasoning for Biological Laboratory Manipulation

Biological laboratory automation can reduce repetitive manual work and improve reproducibility, but reliable embodied execution in wet-lab environments remains challenging. Protocols are often unstructured, labware is frequently transparent or reflective, and multi-step procedures require state-aware execution beyond one-shot instruction following. Existing robotic systems often rely on costly hardware, fixed workflows, dedicated instruments, or robotics-oriented interfaces. Here, we introduce BioProVLA-Agent, an affordable, protocol-driven, vision-enhanced embodied multi-agent system enabled by Vision-Language-Action (VLA) models for biological manipulation. The system uses protocols as the task interface and integrates protocol parsing, visual state verification, and embodied execution in a closed-loop workflow. A Tailored LLM Protocol Agent converts protocols into verifiable subtasks; a VLM-RAG Verification Agent assesses readiness and completion using observations, robot states, retrieved knowledge, and success/failure examples; and a VLA Embodied Agent executes verified subtasks through a lightweight policy. To improve robustness under wet-lab visual perturbations, we develop AugSmolVLA, an online augmentation strategy targeting transparent labware, reflections, illumination shifts, and overexposure. We evaluate the system on a hierarchical benchmark covering 15 atomic tasks, 6 composite workflows, and 3 bimanual tasks, including tube loading, sorting, waste disposal, cap twisting, and liquid pouring. Across normal and high-exposure settings, AugSmolVLA improves execution stability over ACT, X-VLA, and the original SmolVLA, especially for precise placement, transparent-object manipulation, composite workflows, and visually degraded scenes. These results suggest a practical route toward accessible, protocol-centered, and verification-capable embodied AI for biological manipulation.

cs.RO↗

arXiv2025

Defect Engineering for Stabilizing Magnetic and Topological Properties in Mn(Bi1-xSbx)2Te4

MnBi2Te4 is a versatile platform for exploring diverse topological quantum states, yet its potential is hampered by intrinsic antisite defects. While Sb substitution has been employed to tune the Fermi level towards the charge neutral point, it exacerbates the formation of Mn-Sb antisite defects. Here, we address this challenge by combining first-principles calculations with strategic synthesis to systematically investigate and control antisite defects in Mn(Bi1-xSbx)2Te4. Our calculations reveal that increasing antisite defect density progressively destroys the field-forced magnetic Weyl state, eventually driving the system into a trivial magnetic insulator. Motivated by these findings, we develop an optimized chemical vapor transport method, yielding high-quality Mn(Bi1-xSbx)2Te4 crystals with significantly reduced antisite defect density. The emergence of strong Shubnikov-de Haas oscillations in the forced ferromagnetic state and a pronounced anomalous Hall effect near charge neutrality, with opposite signs for n- and p-type samples, confirms the type-II Weyl semimetal nature. These findings underscore the critical role of antisite defects in determining the magnetic and topological properties of Mn(Bi1-xSbx)2Te4 and establish defect engineering via optimized synthesis as a crucial strategy for realizing its exotic magnetic topological states.

cond-mat.mtrl-sci↗

arXiv2025

VoteSplat: Hough Voting Gaussian Splatting for 3D Scene Understanding

3D Gaussian Splatting (3DGS) has become horsepower in high-quality, real-time rendering for novel view synthesis of 3D scenes. However, existing methods focus primarily on geometric and appearance modeling, lacking deeper scene understanding while also incurring high training costs that complicate the originally streamlined differentiable rendering pipeline. To this end, we propose VoteSplat, a novel 3D scene understanding framework that integrates Hough voting with 3DGS. Specifically, Segment Anything Model (SAM) is utilized for instance segmentation, extracting objects, and generating 2D vote maps. We then embed spatial offset vectors into Gaussian primitives. These offsets construct 3D spatial votes by associating them with 2D image votes, while depth distortion constraints refine localization along the depth axis. For open-vocabulary object localization, VoteSplat maps 2D image semantics to 3D point clouds via voting points, reducing training costs associated with high-dimensional CLIP features while preserving semantic unambiguity. Extensive experiments demonstrate effectiveness of VoteSplat in open-vocabulary 3D instance localization, 3D point cloud understanding, click-based 3D object localization, hierarchical segmentation, and ablation studies. Our code is available at https://sy-ja.github.io/votesplat/

cs.GR↗

arXiv2025

A Survey of mmWave Backscatter: Applications, Platforms, and Technologies

As a key enabling technology of the Internet of Things (IoT) and 5G communication networks, millimeter wave (mmWave) backscatter has undergone noteworthy advancements and brought significant improvement to prevailing sensing and communication systems. Past few years have witnessed growing efforts in innovating mmWave backscatter transmitters (e.g., tags and metasurfaces) and the corresponding techniques, which provide efficient information embedding and fine-grained signal manipulation for mmWave backscatter technologies. These efforts have greatly enabled a variety of appealing applications, such as long-range localization, roadside-to-vehicle communication, coverage optimization and large-scale identification. In this paper, we carry out a comprehensive survey to systematically summarize the works related to the topic of mmWave backscatter. Firstly, we introduce the scope of this survey and provide a taxonomy to distinguish two categories of mmWave backscatter research based on the operating principle of the backscatter transmitter: modulation-based and relay-based. Furthermore, existing works in each category are grouped and introduced in detail, with their common applications, platforms and technologies, respectively. Finally, we elaborate on potential directions and discuss related surveys in this area.

cs.ET↗

arXiv2025

Unexpected large electrostatic gating by pyroelectric charge accumulation

Pyroelectricity refers to the accumulation of charges due to changes in the spontaneous polarization of ferroelectric materials when subjected to temperature variations. Typically, these pyroelectric charges are considered unstable and dissipate quickly through interactions with the external environment. Consequently, the pyroelectric effect has been largely overlooked in ferroelectric field-effect transistors. In this work, we leverage the van der Waals interface of hBN to achieve a substantial and long-term electrostatic gating effect in graphene devices via the pyroelectric properties of a ferroelectric LiNbO3 substrate. Upon cooling, the polarization change in LiNbO3 induces high doping concentrations up to 1013 cm-2 in the adjacent graphene. Through a combination of transport measurements and non-contact techniques, we demonstrate that the pyroelectric charge accumulation, as well as its enhancement in electric fields, are responsible for this unexpectedly high doping level. Our findings introduce a novel mechanism for voltage-free electrostatic gating control with long retention.

cond-mat.mtrl-sci↗

arXiv2025

Broadband photoresponse enhancement by band engineering in Sb-doped MnBi2Te4

Topological materials have attracted considerable attention for their potential in broadband and fast photoresponse, particularly in the infrared regime. However, the high carrier concentration in these systems often leads to rapid recombination of photogenerated carriers, limiting the photoresponsivity. Here, we demonstrate that Sb doping in MnBi2Te4 effectively reduces carrier concentration and suppresses electron-hole recombination, thereby significantly improving the optoelectronic performance across the visible to mid-infrared spectra. The optimally doped Mn(Bi0.82Sb0.18)2Te4 photodetector achieves a responsivity of 3.02 mA W-1 with a response time of 18.5 {\mu}s at 1550 nm, and 0.795 mA W-1 with a response time of 9.0 {\mu}s at 4 {\mu}m. These values represent nearly two orders of magnitude improvement compared to undoped MnBi2Te4. Our results highlight band engineering as an effective strategy to enhance the infrared performance of topological material-based photodetectors, opening new avenues for high-sensitivity infrared detection.

cond-mat.mtrl-sci↗

arXiv2024

Memristive switching in the surface of a charge-density-wave topological semimetal

Owing to the outstanding properties provided by nontrivial band topology, topological phases of matter are considered as a promising platform towards low-dissipation electronics, efficient spin-charge conversion, and topological quantum computation. Achieving ferroelectricity in topological materials enables the non-volatile control of the quantum states, which could greatly facilitate topological electronic research. However, ferroelectricity is generally incompatible with systems featuring metallicity due to the screening effect of free carriers. In this study, we report the observation of memristive switching based on the ferroelectric surface state of a topological semimetal (TaSe4)2I. We find that the surface state of (TaSe4)2I presents out-of-plane ferroelectric polarization due to surface reconstruction. With the combination of ferroelectric surface and charge-density-wave-gapped bulk states, an electric switchable barrier height can be achieved in (TaSe4)2I-metal contact. By employing a multi-terminal grounding design, we manage to construct a prototype ferroelectric memristor based on (TaSe4)2I with on/off ratio up to 10^3, endurance over 10^3 cycles, and good retention characteristics. The origin of the ferroelectric surface state is further investigated by first-principles calculations, which reveals an interplay between ferroelectricity and band topology. The emergence of ferroelectricity in (TaSe4)2I not only demonstrates it as a rare but essential case of ferroelectric topological materials, but also opens new routes towards the implementation of topological materials in functional electronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Gate-tunable quantum acoustoelectric transport in graphene

Transport probes the motion of quasiparticles in response to external excitations. Apart from the well-known electric and thermoelectric transport, acoustoelectric transport induced by traveling acoustic waves has been rarely explored. Here, by adopting a hybrid nanodevices integrated with piezoelectric substrates, we establish a simple design of acoustoelectric transport with gate tunability. We fabricate dual-gated acoustoelectric devices based on BN-encapsuled graphene on LiNbO3. Longitudinal and transverse acoustoelectric voltages are generated by launching pulsed surface acoustic wave. The gate dependence of zero-field longitudinal acoustoelectric signal presents strikingly similar profiles as that of Hall resistivity, providing a valid approach for extracting carrier density without magnetic field. In magnetic fields, acoustoelectric quantum oscillations appear due to Landau quantization, which are more robust and pronounced than Shubnikov-de Haas oscillations. Our work demonstrates a feasible acoustoelectric setup with gate tunability, which can be extended to the broad scope of various Van der Waals materials.

cond-mat.mes-hall↗

arXiv2023

Acousto-drag photovoltaic effect by piezoelectric integration of two-dimensional semiconductors

Light-to-electricity conversion is crucial for energy harvesting and photodetection, requesting efficient electron-hole pair separation to prevent recombination. Traditional junction-based mechanisms using built-in electric fields fail in non-barrier regions. Homogeneous material harvesting under photovoltaic effect is appealing but only realized in non-centrosymmetric systems via bulk photovoltaic effect. Here we report the realization of photovoltaic effect by employing surface acoustic waves (SAW) to generate zero-bias photocurrent in a conventional layered semiconductor MoSe2. SAW induces periodic modulation to electronic bands and drags the photoexcited pairs toward the travelling direction. The photocurrent is extracted by a local barrier. The separation of generation and extraction processes suppresses recombination and yields large nonlocal photoresponse. We distinguish acousto-electric drag and electron-hole pair separation effect by fabricating devices of different configurations. The acousto-drag photovoltaic effect, enabled by piezoelectric integration, offers an efficient light-to-electricity conversion method, independent of semiconductor crystal symmetry.

cond-mat.mes-hall↗

arXiv2023

Absence of metallicity and bias-dependent resistivity in low-carrier-density EuCd2As2

EuCd2As2 was theoretically predicted to be a minimal model of Weyl semimetals with a single pair of Weyl points in the ferromagnet state. However, the heavily p-doped EuCd2As2 crystals in previous experiments prevent direct identification of the semimetal hypothesis. Here we present a comprehensive magneto-transport study of high-quality EuCd2As2 crystals with ultralow bulk carrier density (10^13 cm-3). In contrast to the general expectation of a Weyl semimetal phase, EuCd2As2 shows insulating behavior in both antiferromagnetic and ferromagnetic states as well as surface-dominated conduction from band bending. Moreover, the application of a dc bias current can dramatically modulate the resistance by over one order of magnitude, and induce a periodic resistance oscillation due to the geometric resonance. Such nonlinear transport results from the highly nonequilibrium state induced by electrical field near the band edge. Our results suggest an insulating phase in EuCd2As2 and put a strong constraint on the underlying mechanism of anomalous transport properties in this system.

cond-mat.mes-hall↗

arXiv2023

UE4-NeRF:Neural Radiance Field for Real-Time Rendering of Large-Scale Scene

Neural Radiance Fields (NeRF) is a novel implicit 3D reconstruction method that shows immense potential and has been gaining increasing attention. It enables the reconstruction of 3D scenes solely from a set of photographs. However, its real-time rendering capability, especially for interactive real-time rendering of large-scale scenes, still has significant limitations. To address these challenges, in this paper, we propose a novel neural rendering system called UE4-NeRF, specifically designed for real-time rendering of large-scale scenes. We partitioned each large scene into different sub-NeRFs. In order to represent the partitioned independent scene, we initialize polygonal meshes by constructing multiple regular octahedra within the scene and the vertices of the polygonal faces are continuously optimized during the training process. Drawing inspiration from Level of Detail (LOD) techniques, we trained meshes of varying levels of detail for different observation levels. Our approach combines with the rasterization pipeline in Unreal Engine 4 (UE4), achieving real-time rendering of large-scale scenes at 4K resolution with a frame rate of up to 43 FPS. Rendering within UE4 also facilitates scene editing in subsequent stages. Furthermore, through experiments, we have demonstrated that our method achieves rendering quality comparable to state-of-the-art approaches. Project page: https://jamchaos.github.io/UE4-NeRF/.

cs.CV↗