Peining Chen | Searcharxiv

arXiv2026

New energy conversion system based on charge-exchange and inner-shell electron transitions

The rapidly growing demand for compact, high-energy power sources has outpaced the capabilities of conventional electrochemical systems that rely on outer-shell redox reactions. In this work, we present a new energy platform that utilizes inner-shell electron transitions that are previously inaccessible due to their high energy thresholds. By leveraging charge exchange processes between bare argon ions (Ar^18+) and neutral helium atoms, we provide clear evidence for the emission of soft X-ray and extreme-ultraviolet photons across a broad spectra range, resulting from inner-shell electron capture and cascade de-excitation. This strategy overcomes the limitations of radiative recombination by enhancing photon energy utilization through broader emission profiles more compatible with practical energy converters. Our design of a helium-filled chamber design enables precise control of output via pressure tuning, achieving a remarkable radiation power density of 6.29*10^8 W L^-1 and an unprecedented energy density of 2.64*10^6 Wh kg^-1. These results may provide a new and effective paradigm for energy conversion systems with ultra-high power and energy densities based on inner-shell electrons.

physics.atom-ph↗

arXiv2025

Semantic-VAE: Semantic-Alignment Latent Representation for Better Speech Synthesis

Mel-spectrograms have been widely used in zero-shot text-to-speech (TTS); their inherent redundancy leads to inefficiency in text-speech alignment. Compact VAE-based latent representations have emerged as a stronger alternative but exhibit an optimization dilemma: higher-dimensional latents improve reconstruction quality and speaker similarity but degrade intelligibility, while lower-dimensional latents improve intelligibility at the cost of reconstruction fidelity. To overcome this dilemma, we propose Semantic-VAE, which uses semantic alignment regularization in the latent space. This design alleviates the reconstruction-generation trade-off by capturing semantic structure in high-dimensional latent representations. When integrated into F5-TTS, our method achieves 2.10% WER and 0.64 speaker similarity on LibriSpeech-PC, outperforming mel-based systems and vanilla acoustic VAE baselines with improved training efficiency. Demo and codes: https://zhikangniu.github.io/semantic-vae/

eess.AS↗