SearcharxivSearch

arXiv subjects

Ran Jing

Publications and source records attributed to Ran Jing.

At least 19 recordsLinked to original sources

Scalable, Simple, and Versatile Encapsulation of 2D Materials and Devices

Air-sensitive 2D materials present a fundamental challenge for device integration. Encapsulation is often required to preserve intrinsic properties, yet conventional protection strategies often fail for thicker layers and complicate fabrication. Here, we demonstrate that electron-beam (e-beam) evaporated aluminum oxide ($\mathrm{AlO}_x$) serves as both an effective encapsulation layer and a platform for direct device fabrication. Unlike transfer-based approaches, this scalable method is compatible with thicker flakes and full device or wafer coverage. It requires no stacking procedures and enables contacts without post-encapsulation etching. Using rare-earth tritellurides ($\mathrm{RTe}_3$, R = La, Er), semimetallic $\mathrm{WTe}_2$, and superconducting $\mathrm{FeTe}_x\mathrm{Se}_{1-x}$, we show that $\mathrm{AlO}_x$ suppresses oxidation and preserves intrinsic optical and electronic properties. We establish substrate-dependent optimization of encapsulation across a range of flake thicknesses, demonstrate that ultrathin $\mathrm{AlO}_x$ preserves $\mathrm{WTe}_2$'s plasmonic response and maintains superconducting performance in $\mathrm{FeTe}_x\mathrm{Se}_{1-x}$. Thus we overcome the longstanding tradeoff between encapsulation and straightforward device fabrication in fragile quantum materials.

cond-mat.mes-hall

Design Optimization for Large High-Force Soft Robot Manipulators Under Gravitational Loads

Designing large soft robots capable of generating high forces for physical human-robot interaction remains a significant challenge in soft robotics. Prior work in large soft robots has focused on proof-of-concept prototypes, and no systematic framework exists for determining the suitability of a design paradigm for a desired task. This manuscript introduces a method for optimizing the geometry of a soft robot limb, maximizing its blocking force subject to an anti-bucking constraint under its own gravitational loading. We demonstrate that an explicit solution exists to the proposed optimization problem under certain assumptions. Experiments with three geometries of a large, soft, pneumatically-actuated manipulator demonstrate that the method correctly predicts which designs meet constraints and which produces the largest end-effector forces. This method, with its closed-form solution, can allow designers to determine a-priori if an intended class of soft manipulators is an appropriate choice for physical interaction at large size scales.

cs.RO

Magnetic-field-tunable cyclotron hyperbolic polaritons

Hyperbolic polaritons are conventionally associated with structural anisotropy or phononic Reststrahlen bands. Here, we predict a new class of hyperbolic polaritons arising from magnetic-field-induced cyclotron motion of charge carriers. When a perpendicular magnetic field is applied to high-mobility semimetals, the cyclotron response drives the in-plane dielectric function from metallic- to insulating-like below the cyclotron resonance frequency, while the out-of-plane response remains metallic. This anisotropy creates a hyperbolic dielectric environment that supports field-tunable hyperbolic polaritons. We develop a comprehensive theoretical framework incorporating coupling to other collective excitations and show that these modes can be directly visualized in real space via terahertz near-field nanoscopy. Our work identifies cyclotron motion as a new route to hyperbolic polaritons and establishes a versatile platform for magnetically programmable nanophotonics.

cond-mat.mes-hall

Trajectory Generation for Underactuated Soft Robot Manipulators using Discrete Elastic Rod Dynamics

Soft robots are well suited for contact-rich tasks due to their compliance, yet this property makes accurate and tractable modeling challenging. Planning motions with dynamically-feasible trajectories requires models that capture arbitrary deformations, remain computationally efficient, and are compatible with underactuation. However, existing approaches balance these properties unevenly: continuum rod models provide physical accuracy but are computationally demanding, while reduced-order approximations improve efficiency at the cost of modeling fidelity. To address this, our work introduces a control-oriented reformulation of Discrete Elastic Rod (DER) dynamics for soft robots, and a method to generate trajectories with these dynamics. The proposed formulation yields a control-affine representation while preserving certain first-principles force-deformation relationships. As a result, the generated trajectories are both dynamically feasible and consistent with the underlying actuation assumptions. We present our trajectory generation framework and validate it experimentally on a pneumatic soft robotic limb. Hardware results demonstrate consistently improved trajectory tracking performance over a constant-curvature-based baseline, particularly under complex actuation conditions.

cs.RO

Plasmon dynamics in graphene

Plasmon are collective oscillations of mobile electrons with dynamics controlled by their charge stiffness("Drude weight"). Using terahertz spacetime metrology, we probe Plasmon dynamics of mono- and bi-layer graphene. In both systems, the experimentally measured Drude weight systematically exceeds the prediction based on non-interacting electronic system. The relative enhancement increases as the carrier density decreases. We attribute the observed deviation to the interplay of interactions and wave function structure of the Dirac fermions in multi-layer graphene. Our results establish that pseudospin structure of the single-particle electronic wave function can directly influence collective excitations, with implications that extend beyond graphene to a broad class of quantum materials.

cond-mat.mes-hall

CIP: A Plug-and-Play Causal Prompting Framework for Mitigating Hallucinations under Long-Context Noise

Large language models often hallucinate when processing long and noisy retrieval contexts because they rely on spurious correlations rather than genuine causal relationships. We propose CIP, a lightweight and plug-and-play causal prompting framework that mitigates hallucinations at the input stage. CIP constructs a causal relation sequence among entities, actions, and events and injects it into the prompt to guide reasoning toward causally relevant evidence. Through causal intervention and counterfactual reasoning, CIP suppresses non causal reasoning paths, improving factual grounding and interpretability. Experiments across seven mainstream language models, including GPT-4o, Gemini 2.0 Flash, and Llama 3.1, show that CIP consistently enhances reasoning quality and reliability, achieving 2.6 points improvement in Attributable Rate, 0.38 improvement in Causal Consistency Score, and a fourfold increase in effective information density. API level profiling further shows that CIP accelerates contextual understanding and reduces end to end response latency by up to 55.1 percent. These results suggest that causal reasoning may serve as a promising paradigm for improving the explainability, stability, and efficiency of large language models.

cs.CL

Lessons from $\alpha$-RuCl3 for pursuing quantum spin liquid physics in atomically thin materials

Quantum spin liquids can arise from Kitaev magnetic interactions, and exhibit fractionalized excitations with the potential for a topological form of quantum computation. This review surveys recent experimental and theoretical progress on the pursuit of phenomena related to Kitaev magnetism in layered and exfoliatable materials, which offer numerous opportunities to apply powerful techniques from the field of atomically thin materials. We primarily focus on the antiferromagnetic Mott insulator $\alpha$-RuCl3, which exhibits Kitaev couplings and is readily exfoliated to single- or few-layer sheets, and thus serves as a test bed for developing probes of Kitaev phenomena in atomically thin materials and devices. We introduce the Kitaev model and how it is realized in $\alpha$-RuCl3 and other material candidates; and cover $\alpha$-RuCl3 synthesis and fabrication into van der Waals heterostructure devices. A key discovery is a work-function-mediated charge transfer that heavily dopes both the $\alpha$-RuCl3 and proximate materials, and can enhance Kitaev interactions by up to 50%. We further discuss a wide range of recent results in electronic transport and optical and tunneling spectroscopies of $\alpha$-RuCl3 devices. The experimental techniques and theoretical insights developed for $\alpha$-RuCl3 establish a framework for discovering and engineering superior two-dimensional Kitaev materials that may ultimately realize elusive quantum spin liquid phases.

cond-mat.str-el

Koopman Control Factorization: Data-Driven Convex Controller Design for a Class of Nonlinear Systems

Although Koopman operators provide a global linearization for autonomous dynamical systems, nonautonomous systems are not globally linear in the inputs. State (or output) feedback controller design therefore remains nonconvex in typical formulations, even with approximations via bilinear control-affine terms. We address this gap by introducing the Koopman Control Factorization, a novel parameterization of control-affine dynamical systems combined with a feedback controller defined as a linear combination of nonlinear measurements. With this choice, the Koopman operator of the closed-loop system is a bilinear combination of the coefficients in two matrices: one representing the system, and the other the controller. We propose a set of sufficient conditions such that the factorization holds. Then, we present an algorithm that calculates the feedback matrix via semi-definite programming, producing a Lyapunov-stable closed-loop system with convex optimization. We evaluate the proposed controllers on two canonical examples of control-affine nonlinear systems (inverted pendulums), and show that our factorization and controller successfully stabilize both under properly-chosen basis functions. This manuscript introduces a broadly generalizable control synthesis method for stabilization of nonlinear systems that is quick-to-compute, verifiably stable, data-driven, and does not rely on approximations.

eess.SY

Bolometric Superconducting Optical Nanoscopy (BOSON)

Superconducting transition-edge sensors are renowned for their extraordinary photon sensitivity and energy resolution, finding applications spanning quantum information, astronomy, and nanophotonics. Here, we report the development of BOlometric Superconducting Optical Nanoscopy (BOSON), a novel platform that integrates bolometric detection at the superconducting transition edges with near-field optical techniques. BOSON enables the mapping of photoinduced changes in superconductivity with unprecedented spatial resolution and photon sensitivity. By incorporating BOSON with low-dimensional materials, we achieved polariton imaging at nanowatt excitation levels--at least four orders of magnitude lower than the power typically required in prior near-field nanoscopy experiments. Our findings highlight the potential for BOSON to advance scanning probe based optical platforms to enable the detection of photons, polaritons, and Cooper pair dynamics at the nanoscale. This paves the way for quantum sensing applications using single-polariton detection and can offer deeper insights into quasiparticle dynamics.

quant-ph

Safe Autonomous Environmental Contact for Soft Robots using Control Barrier Functions

Robots built from soft materials will inherently apply lower environmental forces than their rigid counterparts, and therefore may be more suitable in sensitive settings with unintended contact. However, these robots' applied forces result from both their design and their control system in closed-loop, and therefore, ensuring bounds on these forces requires controller synthesis for safety as well. This article introduces the first feedback controller for a soft manipulator that formally meets a safety specification with respect to environmental contact. In our proof-of-concept setting, the robot's environment has known geometry and is deformable with a known elastic modulus. Our approach maps a bound on applied forces to a safe set of positions of the robot's tip via predicted deformations of the environment. Then, a quadratic program with Control Barrier Functions in its constraints is used to supervise a nominal feedback signal, verifiably maintaining the robot's tip within this safe set. Hardware experiments on a multi-segment soft pneumatic robot demonstrate that the proposed framework successfully maintains a positive safety margin. This framework represents a fundamental shift in perspective on control and safety for soft robots, implementing a formally verifiable logic specification on their pose and contact forces.

cs.RO

Photocurrent Nanoscopy of Quantum Hall Bulk

Understanding nanoscale electronic and thermal transport of two-dimensional (2D) electron systems in the quantum Hall regime, particularly in the bulk insulating state, poses considerable challenges. One of the primary difficulties arises from the presence of chiral edge channels, whose transport behavior obscures the investigation of the insulating bulk. Using near-field (NF) optical and photocurrent (PC) nanoscopy, we probe real-space variations of the optical and thermal dynamics of graphene in the quantum Hall regime without relying on complex sample or electrode geometries. Near the charge neutrality point (CNP), we detect strong optical and photothermal signals from resonant inter-Landau level (LL) magnetoexciton excitations between the 0th and +-1st LLs, which gradually weaken with increasing doping due to Pauli blocking. Interestingly, at higher doping levels and full integer LL fillings, photothermal signals reappear across the entire sample over a ~10-micrometer scale, indicating unexpectedly long cooling lengths and nonlocal photothermal heating through the insulating bulk. This observation suggests thermal conductivity persists for the localized states even as electronic transport is suppressed - a clear violation of the Wiedemann-Franz (WF) law. Our experiments provide novel insights into nanoscale thermal and electronic transport in incompressible 2D gases, highlighting the roles of magnetoexcitons and chiral edge states in the thermo-optoelectric dynamics of Dirac quantum Hall state.

cond-mat.mes-hall

Real-Time Trajectory Generation for Soft Robot Manipulators Using Differential Flatness

Soft robots have the potential to interact with sensitive environments and perform complex tasks effectively. However, motion plans and trajectories for soft manipulators are challenging to calculate due to their deformable nature and nonlinear dynamics. This article introduces a fast real-time trajectory generation approach for soft robot manipulators, which creates dynamically-feasible motions for arbitrary kinematically-feasible paths of the robot's end effector. Our insight is that piecewise constant curvature (PCC) dynamics models of soft robots can be differentially flat, therefore control inputs can be calculated algebraically rather than through a nonlinear differential equation. We prove this flatness under certain conditions, with the curvatures of the robot as the flat outputs. Our two-step trajectory generation approach uses an inverse kinematics procedure to calculate a motion plan of robot curvatures per end-effector position, then, our flatness diffeomorphism generates corresponding control inputs that respect velocity. We validate our approach through simulations of our representative soft robot manipulator along three different trajectories, demonstrating a margin of 23x faster than real-time at a frequency of 100 Hz. This approach could allow fast verifiable replanning of soft robots' motions in safety-critical physical environments, crucial for deployment in the real world.

cs.RO

Self-Sensing for Proprioception and Contact Detection in Soft Robots Using Shape Memory Alloy Artificial Muscles

Estimating a soft robot's pose and applied forces, also called proprioception, is crucial for safe interaction of the robot with its environment. However, most solutions for soft robot proprioception use dedicated sensors, particularly for external forces, which introduce design trade-offs, rigidity, and risk of failure. This work presents an approach for pose estimation and contact detection for soft robots actuated by shape memory alloy (SMA) artificial muscles, using no dedicated force sensors. Our framework uses the unique material properties of SMAs to self-sense their internal stress, via offboard measurements of their electrical resistance and in-situ temperature readings, in an existing fully-soft limb design. We demonstrate that a simple polynomial regression model on these measurements is sufficient to predict the robot's pose, under no-contact conditions. Then, we show that if an additional measurement of the true pose is available (e.g. from an already-in-place bending sensor), it is possible to predict a binary contact/no-contact using multiple combinations of self-sensing signals. Our hardware tests verify our hypothesis via a contact detection test with a human operator. This proof-of-concept validates that self-sensing signals in soft SMA-actuated soft robots can be used for proprioception and contact detection, and suggests a direction for integrating proprioception into soft robots without design compromises. Future work could employ machine learning for enhanced accuracy.

cs.RO

Plasmonic polarization sensing of electrostatic superlattice potentials

Plasmon polaritons are formed by coupling light with delocalized electrons. The half-light and half-matter nature of plasmon polaritons endows them with unparalleled tunability via a range of parameters, such as dielectric environments and carrier density. Therefore, plasmon polaritons are expected to be tuned when in proximity to polar materials since the carrier density is tuned by an electrostatic potential; conversely, the plasmon polariton response might enable the sensing of polarization. Here, we use infrared nano-imaging and nano-photocurrent measurements to investigate heterostructures composed of graphene and twisted hexagonal boron nitride (t-BN), with alternating polarization in a triangular network of moir\'e stacking domains. We observe that the carrier density and the corresponding plasmonic response of graphene are modulated by polar domains in t-BN. In addition, we demonstrate that the nanometer-wide domain walls of graphene moir\'es superlattices, created by the polar domains of t-BN, provide momenta to assist the plasmonic excitations. Furthermore, our studies establish that the plasmon of graphene could function as a delicate sensor for polarization textures. The evolution of polarization textures in t-BN under uniform electric fields is tomographically examined via plasmonic imaging. Strikingly, no noticeable polarization switching is observed under applied electric fields up to 0.23 V/nm, at variance with transport reports. Our nano-images unambiguously reveal that t-BN with triangular domains acts like a ferrielectric, rather than ferroelectric claimed by many previous studies.

cond-mat.mes-hall

Maximizing Consistent Force Output for Shape Memory Alloy Artificial Muscles in Soft Robots

Soft robots have immense potential given their inherent safety and adaptability, but challenges in soft actuator forces and design constraints have limited scaling up soft robots to larger sizes. Electrothermal shape memory alloy (SMA) artificial muscles have the potential to create these large forces and high displacements, but consistently using these muscles under a well-defined model, in-situ in a soft robot, remains an open challenge. This article provides a system for maintaining the highest-possible consistent SMA forces, over long lifetimes, by combining a fatigue testing protocol with a supervisory control system for the muscles' internal temperature state. We propose a design of a soft limb with swap-able SMA muscles, and deploy the limb in a blocked-force test to quantify the relationship between the measured maximum force at different temperatures over different lifetimes. Then, by applying an invariance-based control system to maintain temperatures under our long-life limit, we demonstrate consistent high forces in a practical task over hundreds of cycles. The method we developed allows for practical implementation of SMAs in soft robots through characterizing and controlling their behavior in-situ, and provides a method to impose limits that maximize their consistent, repeatable behavior.

cs.RO

Electronic structure, magnetic and transport properties of antiferromagnetic Weyl semimetal GdAlSi

We report the topological electronic structure, magnetic, and magnetotransport properties of a noncentrosymmetric compound GdAlSi. Magnetic susceptibility shows an antiferromagnetic transition at $T_\mathrm{N}$ = 32 K. In-plane isothermal magnetization exhibits an unusual hysteresis behavior at higher magnetic field, rather than near zero field. Moreover, the hysteresis behavior is asymmetric under positive and negative magnetic fields. First-principles calculations were performed on various magnetic configurations, revealing that the antiferromagnetic state is the ground state, and the spiral antiferromagnetic state is a close competing state. The calculations also reveal that GdAlSi hosts multiple Weyl points near the Fermi energy. The band structure measured by angle-resolved photoemission spectroscopy (ARPES) shows relatively good agreement with the theory, with the possibility of Weyl nodes slightly above the Fermi energy. Within the magnetic ordered state, we observe an exceptionally large anomalous Hall conductivity (AHC) of ~ 1310 $\Omega^{-1}$cm$^{-1}$ at 2 K. Interestingly, the anomalous Hall effect persists up to room temperature with a significant value of AHC (~ 155 $\Omega^{-1}$cm$^{-1}$). Our analysis indicates that the large AHC originates from the Berry curvature associated with the multiple pairs of Weyl points near Fermi energy.

cond-mat.str-el

Nano-Imaging of Landau-Phonon Polaritons in Dirac Heterostructures

Polaritons are light-matter quasiparticles that govern the optical response of quantum materials and enable their nanophotonic applications. We have studied a new type of polaritons arising in magnetized graphene encapsulated in hexagonal boron nitride (hBN). These polaritons stem from hybridization of Dirac magnetoexciton modes of graphene with waveguide phonon modes of hBN crystals. We refer to these quasiparticles as the Landau-phonon polaritons (LPPs). Using infrared magneto nanoscopy, we imaged LPPs and controlled their real-space propagation by varying the magnetic field. These LLPs have large in-plane momenta and are not bound by the conventional optical selection rules, granting us access to the "forbidden" inter-Landau level transitions (ILTs). We observed avoided crossings in the LPP dispersion - a hallmark of the strong coupling regime - occurring when the magnetoexciton and hBN phonon frequencies matched. Our LPP-based nanoscopy also enabled us to resolve two fundamental many-body effects: the graphene Fermi velocity renormalization and ILT-dependent magnetoexciton binding energies. These results indicate that magnetic-field-tuned Dirac heterostructures are promising platforms for precise nanoscale control and sensing of light-matter interaction.

physics.optics