arXiv subjects

Peng Bai

Publications and source records attributed to Peng Bai.

At least 19 recordsLinked to original sources

arXiv2026

Transport-defined photodetection through electrically selectable nonequilibrium carrier transport

Broadband optical fields can change in both intensity and spectral distribution, but a fixed-response detector maps this evolving information onto a single electrical signal. Here we demonstrate transport-defined photodetection, in which electrical bias selects how photoexcited carriers are redistributed, escape and are collected, creating complementary response functions within one shared active region. In a GaAs/AlGaAs semiconductor ratchet, light-driven ratchet transport defines the response at 0 V, whereas the spectral evolution at -2 V is consistent with field-assisted hot-carrier transport. The overlapping states span a measured spectral range of 0.4-94.5 {\mu}m at 5 K, provide state-dependent calculated detection floors and support joint infrared operation at 30 K. Direct optical beat notes at 7.432 GHz in the mid-infrared and 17.103 GHz in the terahertz demonstrate optical-to-electrical conversion. Their common-path outputs recover an imposed spatial temperature gradient and the transient field-of-view-integrated effective radiation temperature of laser-excited graphite. These results establish post-photoexcitation transport as a function-defining design variable for semiconductor photodetectors, complementing structure-defined, field-tuned and optically encoded approaches to reconfigurable photodetection.

physics.optics↗

arXiv2026

Assessment of the synthetic feasibility of hypothetical zeolite-like materials based on ZeoNet

A suite of classifiers was developed to distinguish experimentally synthesized zeolites from computationally predicted zeolite-like structures. Using convolutional neural networks applied to 3D volumetric grids, these classifiers achieve accuracies more than an order of magnitude higher than previous approaches based on geometric filters or other machine learning methods. The best-performing model differentiates among hypothetical zeolites and those that can be synthesized as silicates, as aluminophosphates, or as both. This four-class classifier attains a false negative rate of 3.4% and a false positive rate of 0.4%, misidentifying only 1,207 of over 330,000 hypothetical structures--even though the hypothetical structures exhibit similar formation energies as real zeolites and chemically reasonable bond lengths and angles. We hypothesize that the ZeoNet representation captures essential structural features correlated with synthetic feasibility. In the absence of comprehensive physics-based criteria for synthesizability, the small subset of misclassified hypothetical structures likely represents promising candidates for future experimental synthesis.

cond-mat.mtrl-sci↗

arXiv2025

Deep learning denoising unlocks quantitative insights in operando materials microscopy

Operando microscopy provides direct insight into the dynamic chemical and physical processes that govern functional materials, yet measurement noise limits the effective resolution and undermines quantitative analysis. Here, we present a general framework for integrating unsupervised deep learning-based denoising into quantitative microscopy workflows across modalities and length scales. Using simulated data, we demonstrate that deep denoising preserves physical fidelity, introduces minimal bias, and reduces uncertainty in model learning with partial differential equation (PDE)-constrained optimization. Applied to experiments, denoising reveals nanoscale chemical and structural heterogeneity in scanning transmission X-ray microscopy (STXM) of lithium iron phosphate (LFP), enables automated particle segmentation and phase classification in optical microscopy of graphite electrodes, and reduces noise-induced variability by nearly 80% in neutron radiography to resolve heterogeneous lithium transport. Collectively, these results establish deep denoising as a powerful, modality-agnostic enhancement that advances quantitative operando imaging and extends the reach of previously noise-limited techniques.

cs.CV↗

arXiv2025

Machine Learning Detection of Lithium Plating in Lithium-ion Cells: A Gaussian Process Approach

Lithium plating during fast charging is a critical degradation mechanism that accelerates capacity fade and can trigger catastrophic safety failures. Recent work has shown that plating onset can manifest in incremental-capacity analysis as an additional high-voltage feature above 4.0 V, often appearing as a secondary peak or shoulder distinct from the main intercalation peak complex; however, conventional methods for computing dQ/dV rely on finite differencing with filtering, which amplifies sensor noise and introduces bias in feature location. In this paper, we propose a Gaussian Process (GP) framework for lithium plating detection by directly modeling the charge-voltage relationship Q(V) as a stochastic process with calibrated uncertainty. Leveraging the property that derivatives of GPs remain GPs, we infer dQ/dV analytically and probabilistically from the posterior, enabling robust detection without ad hoc smoothing. The framework provides three key benefits: (i) noise-aware inference with hyperparameters learned from data, (ii) closed-form derivatives with credible intervals for uncertainty quantification, and (iii) scalability to online variants suitable for embedded BMS. Experimental validation on Li-ion coin cells across a range of C-rates (0.2C-1C) and temperatures (0-40$^\circ$C) demonstrates that the GP-based method reliably resolves distinct high-voltage secondary peak features under low-temperature, high-rate charging, while correctly reporting no features in non-plating cases. The concurrence of GP-identified differential features, reduced charge throughput, capacity fade measured via reference performance tests, and post-mortem microscopy confirmation supports the interpretation of these signatures as plating-related, establishing a practical pathway for real-time lithium plating detection.

cs.LG↗

arXiv2025

PerformSinger: Multimodal Singing Voice Synthesis Leveraging Synchronized Lip Cues from Singing Performance Videos

Existing singing voice synthesis (SVS) models largely rely on fine-grained, phoneme-level durations, which limits their practical application. These methods overlook the complementary role of visual information in duration prediction.To address these issues, we propose PerformSinger, a pioneering multimodal SVS framework, which incorporates lip cues from video as a visual modality, enabling high-quality "duration-free" singing voice synthesis. PerformSinger comprises parallel multi-branch multimodal encoders, a feature fusion module, a duration and variational prediction network, a mel-spectrogram decoder and a vocoder. The fusion module, composed of adapter and fusion blocks, employs a progressive fusion strategy within an aligned semantic space to produce high-quality multimodal feature representations, thereby enabling accurate duration prediction and high-fidelity audio synthesis. To facilitate the research, we design, collect and annotate a novel SVS dataset involving synchronized video streams and precise phoneme-level manual annotations. Extensive experiments demonstrate the state-of-the-art performance of our proposal in both subjective and objective evaluations. The code and dataset will be publicly available.

eess.AS↗

arXiv2024

High-Performance Green and Blue Light-Emitting Diodes Enabled by CdZnSe/ZnS Core/Shell Colloidal Quantum Wells

The unique anisotropic properties of colloidal quantum wells (CQWs) make them highly promising as components in nanocrystal-based devices. However, the limited performance of green and blue light-emitting diodes (LEDs) based on CQWs has impeded their practical applications. In this study, we tailored alloy CdZnSe core CQWs with precise compositions via direct cation exchange (CE) from CdSe CQWs with specific size, shape, and crystal structure and utilized hot-injection shell (HIS) growth to synthesize CdZnSe/ZnS core/shell CQWs exhibiting exceptional optoelectronic characteristics. This approach enabled us to successfully fabricate green and blue LEDs manifesting superior performance compared to previously reported solution-processed CQW-LEDs. Our devices demonstrated a remarkable peak external quantum efficiency (20.4% for green and 10.6% for blue), accompanied by a maximum brightness 347,683 cd m-2 for green and 38,063 cd m-2 for blue. The high-performance represents a significant advancement for nanocrystal-based light-emitting diodes (Nc-LEDs) incorporating anisotropic nanocrystals. This work provides a comprehensive synthesis strategy for enhancing the efficiency of Nc-LEDs utilizing anisotropic nanocrystals.

physics.optics↗

arXiv2024

Intrinsic Electrochemical Limits Preceding Dendrite Penetration in Ceramic Electrolytes

Solid-state electrolytes have the potential to stabilize lithium metal anodes, which hold the promise to increase the energy density of lithium-ion batteries. However, lithium metal dendrites that occur locally at the solid-solid interface plague the solid-state lithium metal cells during charging, limiting the cycle life, and rendering high safety risks in practical applications. While multiple explanations have been proposed, understandings of the dynamics preceding and causing the metal penetration in solid electrolytes are still not conclusive. Here, by testing many highly consistent Ta-doped Li7La3Zr2O12 (LLZTO) samples and utilizing an improved operando technique on symmetrical cells, we observed a consistent and statistically significant trend of current-dependent dendrite initiation time, which coincides with the Sand's time scaling in liquid electrolytes. This new understanding offers an electrochemical origin of dendritic initiation but also provides future insight into optimal all-solid-state battery design.

cond-mat.mtrl-sci↗

arXiv2024

Increased resistance to photooxidation in Dion-Jacobson lead halide perovskites -- implication for perovskite device stability

2D metal halide perovskites have enabled significant stability improvements in perovskite devices, particularly in resistance to moisture. However, some 2D perovskites are even more susceptible to photooxidation compared to 3D perovskites. This is particularly true for more commonly investigated Ruddlesden-Popper (RP) perovskites that exhibit increased susceptibility to photoinduced degradation compared to Dion-Jacobson (DJ) perovskites. Comparisons between different RP and DJ perovskites reveal that this phenomenon cannot be explained by commonly proposed differences in superoxide ion generation, interlayer distance and lattice structural rigidity differences. Instead, the resistance to photooxidation of DJ perovskites can be attributed to decreased likelihood of double deprotonation events (compared to single deprotonation events in RP perovskites) required for the loss of organic cations and the perovskite decomposition. Consequently, DJ perovskites are less susceptible to oxidative degradation (both photo- and electrochemically induced), which leads to improved operational stability of solar cells based on these materials.

cond-mat.mtrl-sci↗

arXiv2024

FMamba: Mamba based on Fast-attention for Multivariate Time-series Forecasting

In multivariate time-series forecasting (MTSF), extracting the temporal correlations of the input sequences is crucial. While popular Transformer-based predictive models can perform well, their quadratic computational complexity results in inefficiency and high overhead. The recently emerged Mamba, a selective state space model, has shown promising results in many fields due to its strong temporal feature extraction capabilities and linear computational complexity. However, due to the unilateral nature of Mamba, channel-independent predictive models based on Mamba cannot attend to the relationships among all variables in the manner of Transformer-based models. To address this issue, we combine fast-attention with Mamba to introduce a novel framework named FMamba for MTSF. Technically, we first extract the temporal features of the input variables through an embedding layer, then compute the dependencies among input variables via the fast-attention module. Subsequently, we use Mamba to selectively deal with the input features and further extract the temporal dependencies of the variables through the multi-layer perceptron block (MLP-block). Finally, FMamba obtains the predictive results through the projector, a linear layer. Experimental results on eight public datasets demonstrate that FMamba can achieve state-of-the-art performance while maintaining low computational overhead.

cs.LG↗

arXiv2022

Lateral quantum confinement regulates charge carrier transfer and biexciton interaction in CdSe/CdSeS core/crown nanoplatelets

Charge carrier dynamics essentially determine the performance of various optoelectronic applications of colloidal semiconductor nanocrystals. Among them, two-dimensional nanoplatelets provide new adjustment freedom for their unique core/crown heterostructure. Herein, we demonstrate that by fine-tuning the core size and the lateral quantum confinement, the charge carrier transfer rate from the crown to the core can be varied by one order of magnitude in CdSe/CdSeS core/alloy-crown nanoplatelets. In addition, the transfer can be affected by a carrier blocking mechanism, i.e., the filled carriers hinder further possible transfer. Furthermore, we found that the biexciton interaction is oppositely affected by quantum confinement and electron delocalization, resulting in a non-monotonic variation of the biexciton binding energy with the emission wavelength. This work provides new observations and insights into the charge carrier transfer dynamics and exciton interactions in colloidal nanoplatelets and will promote their further applications in lasing, display, sensing, etc.

cond-mat.mes-hall↗

arXiv2021

Interplay between Phase Transformation Instabilities and Spatiotemporal Reaction Heterogeneities in Particulate Intercalation Electrodes

Lithium-ion batteries rely on particulate porous electrodes to realize high performance, especially the fast-charging capability. To minimize the particle-wise reaction heterogeneities that may lead to local hot spots, deeper understandings of these electrodes at the mesoscale, i.e. hundreds of particles, have become an urgent need. This study reveals that the seemingly random reaction heterogeneities are actually controlled by the interplay between the non-equilibrium material thermodynamics and the external electrochemical driving force. Our operando experiments confirm the true working current density around a single particle that is much higher than the globally averaged current density, can change the behavior of phase transformation. The combined theoretical and experimental analyses reveal that unlike other phase-transforming porous electrodes, not all phase separation processes in graphite can be suppressed at high currents, due to the characteristics of the concentration-dependent exchange current density. The insights highlight the necessity to incorporate materials thermodynamics into electrochemical models to ensure self-consistent understandings of practical porous electrodes toward precision design and management.

cond-mat.mtrl-sci↗

arXiv2021

Transient Polarization and Dendrite Initiation Dynamics in Ceramic Electrolytes

Solid-state electrolytes, by enabling lithium metal anodes, may significantly increase the energy density of current lithium-ion batteries. However, similar to their liquid counterparts, these hard and stiff electrolytes can still be penetrated by soft Li metal, above a critical current density (CCD). The prevailing method to determine the CCD employs step-wise galvanostatic cycling, which suffers from inconsistent active interfacial areas due to void formations after repeated stripping and plating, leaving large variance in the reported data that preclude precision understandings. Here, we combine a one-way polarization technique with electrochemical impedance spectroscopy to uncover, for the first time, the existence of significant polarization dynamics in ceramic electrolytes. In contrast to the diverging transient current due to metal penetration, the current peaks we observed suggest a diffusion-limited mechanism that follows the classic Randles-Sevcik equation for analyzing the diffusion-limited processes in liquid electrolytes. Our results allow a rigorous self-consistent analysis to reveal that the CCD is a diffusion-limited current density, while the system-specific limiting current density for ceramic electrolytes is still lower than CCD, which suggests that the ion transport mechanism preceding the dendrite penetration in ceramic electrolytes is unifiable with that in liquid electrolytes.

physics.chem-ph↗

arXiv2021

1D Stochastic Inversion of Airborne Time-domain Electromag-netic Data with Realistic Prior and Accounting for the Forward Modeling Error

Airborne electromagnetic surveys may consist of hundreds of thousands of soundings. In most cases, this makes 3D inversions unfeasible even when the subsurface is characterized by a high level of heterogeneity. Instead, approaches based on 1D forwards are routinely used because of their computational efficiency. However, it is relatively easy to fit 3D responses with 1D forward modelling and retrieve apparently well-resolved conductivity models. However, those detailed features may simply be caused by fitting the modelling error connected to the approximate forward. In addition, it is, in practice, difficult to identify this kind of artifacts as the modeling error is correlated. The present study demonstrates how to assess the modelling error introduced by the 1D approximation and how to include this additional piece of information into a probabilistic inversion. Not surprisingly, it turns out that this simple modification provides not only much better reconstructions of the targets but, maybe, more importantly, guarantees a correct estimation of the corresponding reliability.

physics.geo-ph↗

arXiv2021

Fast Charging Limits of Ideally Stable Metal Anodes in Liquid Electrolytes

Next-generation high-energy-density batteries require ideally stable metal anodes, for which smooth metal deposits during battery recharging are considered a sign of interfacial stability that can ensure high efficiency and long cycle life. With the recent successes, whether the absolute morphological stability guarantees absolute electrochemical stability and safety emerges as a critical question to be investigated in systematic experiments under practical conditions. Here, we use the ideally stable ingot-type sodium metal anode as a model system to identify the fast-charging limits, i.e. highest safe current density, of metal anodes. Our results show that metal penetration can still occur at relatively low current densities, but the overpotentials at the penetration depend on the pore sizes of the separators and surprisingly follow a simple mathematical model we developed as the Young-Laplace overpotential. Our study suggests that the success of stable metal batteries with even the ideally smooth metal anode requires the holistic design of the electrolyte, separator, and metal anodes to ensure the penetration-free operation.

cond-mat.mtrl-sci↗

arXiv2021

Ultrabroadband THz/IR upconversion and photovoltaic response in semi-conductor ratchet based upconverter

An ultrabroadband upconversion device is demonstrated by direct tandem integration of a p-type GaAs/AlxGa1-xAs ratchet photodetector (RP) with a GaAs double heterojunction LED (DH-LED) using the molecular beam epitaxy (MBE). An ultrabroadband photoresponse from terahertz (THz) to near infrared (NIR) region (4-200 THz) was realized that covers a much wider frequency range com-pared with the existing upconversion devices. Broadband IR/THz radiation from 1000 K blackbody is successfully upconverted into NIR photons which can be detected by commercial Si-based device. The normal incidence absorption of the RP simplifies the structure of the RP-LED device and make it more compact compared with the inter-subband transition based upconverters. In addition to the up-conversion function, the proposed upconverter is also tested as photovoltaic detectors in the infrared region (15-200 THz) without an applied bias voltage due to the ratchet effect.

physics.app-ph↗

arXiv2021

Realization of ultrabroadband THz/IR photoresponse in a bias-tunable ratchet photodetector

High performance Terahertz (THz) photodetector has drawn wide attention and got great improvement due to its significant application in biomedical, astrophysics, nondestructive inspection, 6th generation communication system as well as national security application. Here we demonstrate a novel broadband photon-type THz/infrared (IR) photodetector based on the GaAs/AlxGa1-xAs ratchet structure. This kind of photodetector realizes a THz photon-response based on the electrically pumped hot hole injection and overcomes the internal workfunction related spectral response limit. An ultrabroadband photoresponse from 4 THz to 300 THz and a peak responsivity of 50.3 mA/W are realized at negative bias voltage of -1 V. The photodetector also presents a bias-tunable photon-response characteristic due to the asymmetric structure. The ratchet structure also induces an evident photocurrent even at zero bias voltage, which indicates the detector can be regard as a broadband photovoltaic-like detector. The rectification characteristic and high temperature operation possibility of the photodetector are also discussed. This work not only demonstrates a novel ultrabroadband THz/IR photodetector, but also provides a new method to study the light-responsive ratchet.

physics.app-ph↗

arXiv2020

(Quasi-)Real-Time Inversion of Airborne Time-Domain Electromagnetic Data via Artificial Neural Network

The possibility to have results very quickly after, or even during, the collection of electromagnetic data would be important, not only for quality check purposes, but also for adjusting the location of the proposed flight lines during an airborne time-domain acquisition. This kind of readiness could have a large impact in terms of optimization of the Value of Information of the measurements to be acquired. In addition, the importance of having fast tools for retrieving resistivity models from airborne time-domain data is demonstrated by the fact that Conductivity-Depth Imaging methodologies are still the standard in mineral exploration. In fact, they are extremely computationally efficient, and, at the same time, they preserve a very high lateral resolution. For these reasons, they are often preferred to inversion strategies even if the latter approaches are generally more accurate in terms of proper reconstruction of the depth of the targets and of reliable retrieval of true resistivity values of the subsurface. In this research, we discuss a novel approach, based on neural network techniques, capable of retrieving resistivity models with a quality comparable with the inversion strategy, but in a fraction of the time. We demonstrate the advantages of the proposed novel approach on synthetic and field datasets.

physics.geo-ph↗

arXiv2020

Operando Electrochemical Kinetics in Particulate Porous Electrodes by Quantifying the Mesoscale Spatiotemporal Heterogeneities

Electrochemical energy systems rely on particulate porous electrodes to store or convert energies. While the three-dimensional porous structures were introduced to maximize the interfacial area for better overall performance of the system, spatiotemporal heterogeneities arose from materials thermodynamics localize the charge transfer processes onto a limited portion of the available interfaces. Here, we demonstrate a simple but precision method that can directly track and analyze the operando (i.e. local and reacting) interfaces at the mesoscale in a practical graphite porous electrode to obtain the true local current density, which turned out to be two orders of magnitude higher than the globally averaged current density adopted by existing studies. Our results resolve the long-standing discrepancies between kinetics parameters derived from electroanalytical measurements and from first principles predictions. Contradictory to prevailing beliefs, the electrochemical dynamics is not controlled by the solid-state diffusion process once the spatiotemporal reaction heterogeneities emerge in porous electrodes.

physics.app-ph↗