arXiv subjects

Zhen Jiang

Publications and source records attributed to Zhen Jiang.

At least 19 recordsLinked to original sources

arXiv2026

Rethinking EEG-Based Disease Diagnosis: Decoupling Instance Representation Learning from Subject-Level Supervision

EEG-based disease diagnosis requires one prediction per subject, yet common pipelines segment recordings into short instances, inherit the subject label for every instance, and train instance-level classifiers. This assumes that all instances provide equally reliable diagnostic evidence. Multiple instance learning (MIL) avoids inherited labels by treating each subject as a bag. However, EEG datasets contain far fewer subjects than instances, which can limit the quality of the representations learned by end-to-end MIL. We propose BridgeMIL, a two-stage framework that decouples instance representation learning from subject-level supervision. Stage 1 pretrains the encoder without inherited instance labels by aligning temporally nearby windows and independently sampled within-subject sub-bags. Variance and covariance regularization prevent collapse and reduce redundancy without negative pairs. Stage 2 transfers the encoder to an attention-based MIL aggregator, applies supervision only to subject predictions, and limits representation drift through feature retention. Across three EEG disease datasets and five representative backbones, BridgeMIL attains the highest mean accuracy in 14 of 15 dataset-backbone settings and an overall mean accuracy of 76.57%, 4.28 percentage points higher than the strongest baseline. Further analyses reveal substantial variation in inherited-label reliability across instances, greater performance sensitivity to subject scarcity than to instance scarcity, and a more structured representation space with distinct subject-wise clusters and improved separation between diagnostic classes. Together, these findings underscore the importance of aligning supervision with the subject-level prediction objective while learning from abundant EEG instances without assigning disease labels to individual instances.

cs.LG↗

arXiv2026

The FAST Hundred-Deg$^2$ HI Deep (HD$^2$) Survey: Early Results from the Pilot Survey

The Hundred-deg$^2$ HI Deep (HD$^2$) survey carried out with the Five-hundred-meter Aperture Spherical Telescope (FAST) is planned to map a contiguous region within the DESI DR1 footprint, achieving an effective integration time of 20 minutes for each pointing and a uniform detection sensitivity of 0.28 mJy beam$^{-1}$ at 4.8 km s$^{-1}$ resolution. We present early results from the pilot HD$^2$ survey: a 10 deg$^2$ field overlapping with HSC-SSP and the DESI EDR SV3, observed with an integration time of 7.3 minutes per beam and the rms of 0.45 mJy beam$^{-1}$ at 4.8 km s$^{-1}$ resolution. We identify 339 HI sources at $z<0.09$, corresponding to $\sim$34 detections per deg$^2$, nearly six times higher than the detection rate of the wide-field surveys. Optical counterparts are primarily identified using DESI redshifts, yielding a matching rate and correctness exceeding 90% for galaxies with $r<19.5$ mag, a substantial improvement over SDSS. Under the constraint of $r < 17.8$ mag and $0.01 < z < 0.05$, nearly 50% of galaxies in the DESI BGS samples have HI detections in this pilot survey. The optical properties of these HI-detected galaxies span nearly the entire parameter range of the DESI sample. The gas fraction scaling relations versus stellar mass, stellar mass surface density, NUV-r, and specific star formation rate are consistent with previous surveys, e.g., ALFALFA, DINGO, and xGASS. These results justify the feasibility of the full HD$^2$ survey, which will build a high-completeness HI census over a contiguous area to probe the cold gas scaling relations of galaxies over different scales.

astro-ph.GA↗

arXiv2026

PlexRL: Cluster-Level Orchestration of Serviceized LLM Execution for RLVR

Reinforcement learning with verifiable rewards (RLVR) has recently unlocked strong reasoning capabilities in large language models (LLMs), triggering rapid exploration of new algorithms and data. However, RLVR training is notoriously inefficient: long-tailed rollouts, tool-induced stalls, and asymmetric resource requirements between rollout and training introduce substantial idle time that cannot be eliminated by job-local optimizations such as synchronous pipelining, asynchronous rollout, or colocated execution. We argue that this inefficiency is structural. While idle gaps are unavoidable within individual RLVR jobs, they are largely anti-correlated across jobs and therefore exploitable at the cluster level. Leveraging this observation, we present PlexRL, a cluster-level runtime for multiplexing unified LLM services across RLVR jobs. By centrally managing model placement, state transitions, and function-level scheduling under strict affinity constraints, PlexRL time-slices LLM execution across jobs to fill otherwise idle periods without expensive model migration. Our implementation and evaluations demonstrate that PlexRL significantly improves effective cluster capacity and reduces user GPU hour cost by maximum 37.58% while preserving algorithmic flexibility and introducing minimal per-job overhead.

cs.DC↗

arXiv2026

Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding

Source-free domain adaptation (SFDA) provides a practical solution to cross-subject EEG decoding by adapting source-pretrained models to unlabeled target domains without accessing source data. However, existing SFDA methods rely solely on the limited internal knowledge of source-pretrained models, leading to inferior cross-domain generalization and unreliable pseudo-labels. Although EEG Foundation Models (FMs) pretrained on large-scale data exhibit strong generalizability, their potential in SFDA remains largely unexplored. To this end, we propose FUSED, a Foundation-guided Source-free EEG Decoding framework that integrates a large-scale FM with a compact Specialist Model (SM) via dual-branch co-adaptation. Specifically, we introduce a Co-adaptation mechanism equipping both branches with linear and prototype views, enabling cross-branch pseudo-label generation. Additionally, we design a Consensus Filtering Mechanism that exploits the FM's inherent stability to identify high-quality samples, along with a Two-Stage Pseudo-Label Refinement scheme to suppress error accumulation through cross-branch arbitration. Finally, we calibrate the FM's decision boundaries via mutual information maximization with the SM, followed by knowledge distillation from FM to SM, forming a principled calibrate-then-distill pipeline. To our knowledge, FUSED is the first work to leverage EEG FMs within the SFDA framework for cross-subject EEG decoding. Extensive experiments across three EEG paradigms, including motor imagery, emotion recognition, and SSVEP, demonstrate consistent state-of-the-art performance, validating the effectiveness of foundation-guided synergy for robust and privacy-preserving EEG decoding.

eess.SP↗

arXiv2026

Random batch sum-of-Gaussians method for molecular dynamics simulation of particle systems in the NPT ensemble

In this work, we develop a random batch sum-of-Gaussians (RBSOG) method for molecular dynamics simulations of charged systems in the isothermal-isobaric (NPT) ensemble. We introduce an SOG splitting of the pressure-related $1/r^3$ kernel, yielding a smooth short-/long-range decomposition for instantaneous pressure evaluation. The long-range part is treated in Fourier space by random-batch importance sampling. Because the radial and non-radial pressure components favor different proposals, direct sampling either increases structure-factor evaluations and communication or leads to substantial variance inflation. To address this tradeoff, we introduce a measure-recalibration strategy that reuses Fourier modes drawn from the radial proposal and corrects them for the non-radial target, producing an unbiased pressure estimator with significantly reduced variance and negligible extra cost. The resulting method mitigates pressure artifacts caused by cutoff discontinuities in traditional Ewald-based treatments while preserving near-optimal $O(N)$ complexity. We provide theoretical evidence on pressure decomposition error, consistency of stochastic approximation, and convergence of RBSOG-based MD. Numerical experiments on bulk water, LiTFSI ionic liquids, and DPPC membranes show that RBSOG accurately reproduces key structural and dynamical observables with small batch sizes ($P\sim 100$). In large-scale benchmarks up to $10^7$ atoms on $2048$ CPU cores, RBSOG achieves about an order-of-magnitude speedup over particle-particle particle-mesh in electrostatic calculations for NPT simulations, together with a consistent $4\times$ variance reduction relative to random batch Ewald and excellent weak/strong scalability. Overall, RBSOG provides a practical and scalable route to reduce time-to-solution and communication cost in large-scale NPT simulations.

physics.comp-ph↗

arXiv2025

Fast and Robust T1 Mapping Based on a 3D Dual-Echo UTE Sequence (PETALUTE) for SPION Biodistribution Assessment

Superparamagnetic iron oxide nanoparticles (SPIONs) such as ferumoxytol are promising theranostic agents detectable with MRI. Relaxation time mapping offers reproducible, quantitative biomarkers of SPION distribution, but conventional methods suffer from susceptibility artifacts, long echo times, and extended scan durations, limiting accurate quantification. This study developed a fast, B1-corrected T1-mapping protocol using PETALUTE, a 3D dual-echo ultrashort-echo MRI sequence with a rosette k-space trajectory and variable flip-angle acquisition for quantitative ferumoxytol imaging. Agarose phantoms containing 0-5000 ppm ferumoxytol were scanned at 7T with PETALUTE and vendor-supplied RARE-VTR. PETALUTE T1 maps were derived from two flip angles (4 deg and 20 deg), and mean R1 values were correlated with ferumoxytol concentration. For in vivo feasibility, mice bearing 4T1 mammary and flank tumors were scanned 24 h post-injection (ferumoxytol: n=2, 40 mg/kg; control: n=1). Regions of interest in muscle and tumors were analyzed to compare T1 and R1 values obtained with both methods. PETALUTE produced positive contrast for all phantom concentrations except 5000 ppm, whereas RARE-VTR did not. PETALUTE demonstrated a significant linear correlation between R1 and ferumoxytol concentration (R=0.975, p<0.01), in contrast to RARE-VTR (R=0.672, p=0.144). In vivo, PETALUTE enabled high-resolution, whole-abdominal imaging in 4 min 19 s. Ferumoxytol-injected mice showed T1 shortening in flank tumors, consistent with iron uptake, and PETALUTE revealed elevated T1 value with preserved T2*-weighted signal in one mammary tumor. PETALUTE-based T1 mapping provides fast, quantitative, positive-contrast ferumoxytol imaging with greater spatial coverage and a wider usable concentration range than conventional RARE-VTR.

physics.med-ph↗

arXiv2025

Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction

The evolution of Large Language Models (LLMs) from passive responders to autonomous agents necessitates a fundamental shift in learning paradigms -- from static imitation to incentive-driven decision making. However, this transition is significantly impeded by the lack of scalable infrastructure capable of constructing high-quality interaction signals for effective policy learning. To address this, we introduce a comprehensive method designed to systematically scale the diversity and complexity of interactive environments. Our method realizes this scaling by addressing three orthogonal dimensions: (1) Complexity: NexAU, a flexible agent framework that supports building complex agent hierarchies via simple configurations; (2) Diversity: NexA4A automatically generates diverse agent hierarchies from natural language to cover infinite domains; and (3) Fidelity: NexGAP bridges the simulation-reality gap by integrating dynamic real-world environment for grounded trajectories synthesis. We train Nex-N1 upon the diverse and complex interactive environments established by our infrastructure. Empirical results on benchmarks such as SWE-bench and tau2 demonstrate that Nex-N1 consistently outperforms SOTA open-source models and achieves competitive performance against frontier proprietary models on complex agentic tasks. We open-source the Nex ecosystem and model weights to facilitate further research.

cs.CL↗

arXiv2025

Expert-as-a-Service: Towards Efficient, Scalable, and Robust Large-scale MoE Serving

Mixture-of-Experts (MoE) models challenge serving infrastructures with dynamic, sparse expert utilization, causing instability on conventional systems designed for dense architectures. We propose EaaS, a novel serving system to enable efficient, scalable, and robust MoE deployment. Our system disaggregates MoE modules into independent, stateless services. This design enables fine-grained resource scaling and provides inherent fault tolerance by decoupling compute units. The architecture is powered by a high-performance, CPU-free peer-to-peer communication library that ensures minimal overhead and high throughput. Experiments confirm EaaS's scalability and efficiency, achieving performance comparable to monolithic systems while providing robust fault tolerance and strong scalability. EaaS incurs less than a 2% throughput reduction under simulated hardware failures that would otherwise halt monolithic architectures. It further saves up to 37.5% of computing resources through dynamic fine-grained adaptation to serving traffic, demonstrating strong resilience for large-scale MoE deployment in production.

cs.DC↗

arXiv2025

First Light And Reionization Epoch Simulations (FLARES) -- XIX: Supermassive black hole mergers in the early Universe and their environmental dependence

The upcoming space-based gravitational wave (GW) observatory, LISA, is expected to detect GW signals from supermassive black hole (SMBH) mergers occurring at high redshifts. However, understanding the origin and growth of SMBHs in the early Universe remains an open problem in astrophysics. In this work, we utilize the First Light And Reionization Epoch Simulations (FLARES), a suite of cosmological hydrodynamical zoom-in simulations, to study SMBH mergers at $5 \lesssim z \lesssim 10$ across a wide range of environments. Most mergers in FLARES involve secondary SMBHs near the seed mass ($m_{\rm seed} \approx 1.5 \times 10^{5}~{\rm M}_{\odot}$) while primary SMBHs span up to $10^{9}~{\rm M}_{\odot}$, resulting in mass ratios from $q \sim 10^{-4}$ to $1$, with a peak at $q \sim 1$. The number of mergers increases rapidly towards lower redshifts, and the comoving total number density scales with overdensity as $n_{\rm merger} = 10^{-3.81} (1 + \delta)^{4.78}$. Denser regions host more massive mergers, with higher merger redshifts and lower mass ratios. Within the FLARES redshift range, LISA is expected to detect mergers with $10^{5} \lesssim M_{\rm tot}/{\rm M}_{\odot} \lesssim 10^{8}$ and $q \gtrsim 10^{-2}$, corresponding to a detection rate of $0.030~{\rm yr}^{-1}$ for events with signal-to-noise ratio ${\rm SNR} \geq 10$. Our study demonstrates the sensitivity of GW predictions at high redshifts to SMBH seed models and merger time delays, highlighting the need for improved modeling in future cosmological simulations to maximize LISA's scientific return.

astro-ph.GA↗

arXiv2025

Distributionally chaotic $C_0$-semigroups on complex sectors

We explore distributional chaos for $C_0$-semigroups of linear operators on Banach spaces whose index set is a sector in the complex plane. We establish the relationship between distributional sensitivity and distributional chaos by characterizing them in terms of distributionally (semi-)irregular vectors. Additionally, we provide conditions under which a $C_0$-semigroup admits a linear manifold of distributionally irregular vectors. Furthermore, we delve into the study of distributional chaos for the translation $C_0$-semigroup on weighted $L_p$-spaces with a complex sector as the index set. We obtain a sufficient condition for dense distributional chaos, expressed in terms of the weight. In particular, we construct an example of a translation $C_0$-semigroup with a complex sector index set that is Devaney chaotic but not distributionally chaotic.

math.FA↗

arXiv2025

Two categories of UV-upturn galaxies revealed by semi-analytic models

UV-upturn galaxies are characterized by unusually excessive flux in the far-ultraviolet (FUV) band, observed in some elliptical galaxies and the bulges of disk galaxies. We examine UV-upturn galaxies within the semi-analytic model GABE, which embeds the formation of extreme horizontal branch (EHB) stars -- proposed as key candidates responsible for the UV-upturn phenomenon. We have analyzed all related physical processes, including stellar evolution, initial mass functions (IMFs), dust attenuation, galaxy age, metallicity, and binary fractions, in an effort to determine which processes play significant roles. Our findings reveal two categories of UV-upturn galaxies in the semi-analytic model, each with distinct formation channels: old metal-rich quenched elliptical galaxies, which are intrinsic UV-upturn galaxies induced by EHB stars within their old stellar populations, and dusty star-forming galaxies, which are relatively young and may also be photometrically identified as UV-upturn galaxies when accounting for dust attenuation. Dust attenuation contributes to 20% - 60% of the UV-upturn galaxies, depending on the specific dust attenuation models adopted. With the binary star formation model of EHB stars, both of these formation channels exhibit strong preferences for high stellar metallicity. The high-mass end slope of the IMFs is found to have a marginal effect, indicating that a universal IMF is adequate for studying the UV-upturn phenomenon.

astro-ph.GA↗

arXiv2024

Exploring the anisotropic gravitational wave background from all-sky mock gravitational wave event catalogues

Anisotropic stochastic gravitational wave background (SGWB) serves as a potential probe of the large-scale structure (LSS) of the universe. In this work, we explore the anisotropic SGWB from local ($z < \sim 0.085$) merging stellar mass compact binaries, specifically focusing on merging stellar binary black holes, merging neutron-star-black-hole binaries, and merging binary neutron stars. The analysis employs seven all-sky mock lightcone gravitational wave event catalogues, which are derived from the Millennium simulation combined with a semi-analytic model of galaxy formation and a binary population synthesis model. We calculate the angular power spectra $\mathrm{C}_\ell$ at multipole moments $\ell$, expressed as $\text{log}_{10} [\ell(\ell+1)\mathrm{C}_\ell/(2\pi)]$, based on the skymaps of the overdensity $\delta_\mathrm{GW}$ in the anisotropic SGWB. The spectra for all three source types exhibit an approximately linear increase with $\text{log}_{10} \ell$ at higher $\ell$ (e.g., $\ell > \sim 30 - 300$) in seven catalogues, with a characteristic slope of $\sim 2$. The spectra of seven catalogues exhibit considerable variations, arising from fluctuations in spatial distribution, primarily in the radial distribution, of nearby sources (e.g., $< 50$ Mpc/h). After subtracting these nearby sources, the variations become much smaller and the spectra for the three source types become closely aligned (within discrepancies of a factor of $\sim 2$ across $\ell = 1 - 1000$ for all catalogues). We also find that including further sources results in a rapid decrease in the anisotropy.

astro-ph.HE↗

arXiv2024

On the Cosmic Variance of the Merger Rate Density of Binary Neutron Stars

The cosmic variance on the star formation history may lead to bias to the merger rate density estimation of binary neutron star (BNS) mergers by the compact binary population synthesis. In this paper, we take the advantage of the large boxsize of the Millennium Simulation combined with the semi-analytic galaxy formation model GABE, and the parameterized population binary star evolution (BSE) model to examine how much effect will the cosmic variance introduce on the estimation of merger rate density of BNS mergers. We find that for sub-box size of $100\rm Mpc$ and $200\rm Mpc$, the variance of merger rate density $σ_{\rm R}/\rm R$ at different redshift is about $23\%-35\%$ and $13\%-20\%$ respectively. On one hand, as for the variance of the detection rate on BNS mergers with current LIGO-Virgo-KAGRA (LVK) detector network, this value is very small $\lesssim 10\%$, which indicates ignoring the cosmic variance is reasonable for estimating the merger rate density from current LVK observation. On the other hand, with next-generation gravitational wave detectors, it is possible to localize BNS mergers within sub-boxes possessing length of $\rm 40 Mpc$ for source redshift $z_{s}<0.2$. In such a small box, the cosmic variance of the merger rate density is significant, i.e., the value of $σ_{\rm R}/\rm R$ is about $\sim 55\%$. This hints that estimating the merger rate density of BNS in different sky areas may provide useful information on the cosmic variance.

astro-ph.HE↗

arXiv2024

Manipulating multiple optical parametric processes in photonic topological insulators

Topological quantum optics, an emerging area of study, holds the potential to bring about substantial enhancements for integrated quantum devices. Here we propose integrated topological quantum devices performing various functions including optical parametric amplification, frequency division, and frequency entangled biphoton generation. We show two distinct edge modes corresponding to different frequency ranges in both sandwich kagome and honeycomb topological designs that emulate the quantum valley Hall effect. These two topological edge modes enable two types of optical parametric processes through four-wave mixing, specifically inter-band and intra-band cases. The devices emulating photonic valley-Hall insulators allow the frequency division of two transverse modes, and furthermore, enable the separation of two quantum functionalities - optical parametric amplification and frequency entangled biphoton state generation. More importantly, the parametric processes are inborn topological protected, showing robustness against sharp bends and disorders. Our proposal significantly widens the possibilities for robust, multifunctional topological quantum devices on-chip, which may find applications in quantum information processing.

physics.optics↗

arXiv2023

Merging history of massive galaxies at 3<z<6

The observational data of high redshift galaxies become increasingly abundant, especially since the operation of the James Webb Space Telescope (JWST), which allows us to verify and optimize the galaxy formation model at high redshifts. In this work, we investigate the merging history of massive galaxies at $3 < z < 6$ using a well-developed semi-analytic galaxy formation catalogue. We find that the major merger rate increases with redshift up to 3 and then flattens. The fraction of wet mergers, during which the sum of the cold gas mass is higher than the sum of the stellar mass in two merging galaxies, also increases from $\sim$ 34\% at $z = 0$ to 96\% at $z = 3$. Interestingly, almost all major mergers are wet at $z > 3$ . This can be attributed to the high fraction ($> 50\%$) of cold gas at $z > 3$. In addition, we study some special systems of massive merging galaxies at $3 < z < 6$, including the massive gas-rich major merging systems and extreme dense proto-clusters, and investigate the supermassive black hole-dark matter halo mass relation and dual AGNs. We find that the galaxy formation model reproduces the incidence of those observed massive galaxies, but fails to reproduce the relation between the supermassive black hole mass and the dark matter halo mass at $z \sim 6$. The latter requires more careful estimates of the supermassive black hole masses observationally. Otherwise, it could suggest modifications of the modeling of the supermassive black hole growth at high redshifts.

astro-ph.GA↗

arXiv2023

Exploring the multiband gravitational wave background with a semi-analytic galaxy formation model

An enormous number of compact binary systems, spanning from stellar to supermassive levels, emit substantial gravitational waves during their final evolutionary stages, thereby creating a stochastic gravitational wave background (SGWB). We calculate the merger rates of stellar compact binaries and massive black hole binaries using a semi-analytic galaxy formation model -- Galaxy Assembly with Binary Evolution (GABE) in a unified and self-consistent approach, followed by an estimation of the multi-band SGWB contributed by those systems. We find that the amplitudes of the principal peaks of the SGWB energy density are within one order of magnitude $Ω_{GW} \sim 10^{-9}- 10^{-8}$. This SGWB could easily be detected by the Square Kilometre Array (SKA), as well as planned interferometric detectors, such as the Einstein Telescope (ET) and the Laser Interferometer Space Antenna (LISA). The energy density of this background varies as $Ω_{GW} \propto f^{2/3}$ in SKA band. The shape of the SGWB spectrum in the frequency range $\sim[10^{-4}$,$1]$Hz could allow the LISA to distinguish the black hole seed models. The amplitude of the SGWB from merging stellar binary black holes (BBHs) at $\sim 100$ Hz is approximately 10 and 100 times greater than those from merging binary neutron stars (BNSs) and neutron-star-black-hole (NSBH) mergers, respectively. Note that, since the cosmic star formation rate density predicted by GABE is somewhat lower than observational results by $\sim 0.2$ dex at z < $\sim 2$, the amplitude of the SGWB in the frequency range $\sim[1$, $10^{4}]$ Hz may be underestimated by a similar factor at most.

gr-qc↗

arXiv2023

On-chip topological transport of optical frequency combs in silicon-based valley photonic crystals

The generation and control of optical frequency combs in integrated photonic systems enables complex, high-controllable, and large-scale devices. In parallel, harnessing topological physics in multipartite systems has allowed them with compelling features such as robustness against fabrication imperfections. Here we experimentally demonstrate on-chip topological transport for optical frequency combs at telecommunication wavelengths, both in classical and nonclassical domains. We access both the quantum frequency combs and dissipative Kerr soliton combs with a micro-resonator. The quantum frequency comb, that is, a coherent superposition of multiple frequency modes, is proven to be a frequency-entangled qudit state. We also show that dissipative Kerr soliton combs are highly coherent and mode-locked due to the collective coherence or self-organization of solitons. Moreover, the valley kink states allow both quantum frequency combs and dissipative Kerr soliton combs with robustness against sharp bends. Our topologically protected optical frequency combs could enable the inherent robustness in integrated complex photonic systems.

physics.optics↗

arXiv2023

Li iontronics in single-crystalline T-Nb2O5 thin films with vertical ionic transport channels

The niobium oxide polymorph T-Nb2O5 has been extensively investigated in its bulk form especially for applications in fast-charging batteries and electrochemical (pseudo)capacitors. Its crystal structure that has two-dimensional (2D) layers with very low steric hindrance allows for fast Li-ion migration. However, since its discovery in 1941, the growth of single-crystalline thin films and its electronic applications have not yet been realized, likely due to its large orthorhombic unit cell along with the existence of many polymorphs. Here we demonstrate the epitaxial growth of single-crystalline T-Nb2O5 thin films, critically with the ionic transport channels oriented perpendicular to the film's surface. These vertical 2D channels enable fast Li-ion migration which we show gives rise to a colossal insulator-metal transition where the resistivity drops by eleven orders of magnitude due to the population of the initially empty Nb 4d0 states by electrons. Moreover, we reveal multiple unexplored phase transitions with distinct crystal and electronic structures over a wide range of Li-ion concentrations by comprehensive in situ experiments and theoretical calculations, that allow for the reversible and repeatable manipulation of these phases and their distinct electronic properties. This work paves the way to the exploration of novel thin films with ionic channels and their potential applications.

cond-mat.mtrl-sci↗