Deriving effective electrode-ion interactions from free-energy profiles at electrochemical interfaces

Authors: Fabrice Roncoroni, Abrar Faiyad, Yichen Li, Tao Ye, Ashlie Martini, David Prendergast

Published: July 30, 2025

Understanding ion adsorption at electrified metal-electrolyte interfaces is
essential for accurate modeling of electrochemical systems. Here, we
systematically investigate the free energy profiles of Na$^+$, Cl$^-$, and
F$^-$ ions at the Au(111)-water interface using enhanced sampling molecular
dynamics with both classical force fields and machine-learned interatomic
potentials (MLIPs). Our classical metadynamics results reveal a strong
dependence of predicted ion adsorption on the Lennard-Jones parameters,
highlighting that --without due care-- standard mixing rules can lead to
qualitatively incorrect descriptions of ion-metal interactions. We present a
systematic methodology for tuning the cross-term LJ parameters to control
adsorption energetics in agreement with more accurate models. As a surrogate
for an ab initio model, we employed the recently released Universal Models for
Atoms (UMA) MLIP, which validates classical trends and displays strong specific
adsorption for chloride, weak adsorption for fluoride, and no specific
adsorption for sodium, in agreement with experimental and theoretical
expectations. By integrating molecular-level adsorption free energies into
continuum models of the electric double layer, we show that specific ion
adsorption substantially alters the interfacial ion population, the potential
of zero charge, and the differential capacitance of the system. Our results
underscore the critical importance of force field parameterization and advanced
interatomic potentials for the predictive modeling of ion-specific effects at
electrified interfaces and provide a robust framework for bridging molecular
simulations and continuum electrochemical models.

Revealing Nanoscale Ni-Oxidation State Variations in Single-Crystal NMC811 via 2D and 3D Spectro-Ptychography

Authors: Ralf F. Ziesche, Michael J. Johnson, Ingo Manke, Joshua H. Cruddos, Alice V. Llewellyn, Chun Tan, Rhodri Jervis, Paul R. Shearing, Christoph Rau, Alexander J. E. Rettie, Silvia Cipiccia, Darren Batey

Published: July 30, 2025

Enabling lithium (Li)-ion batteries with higher energy densities, longer
cycle life, and lower costs will underpin the widespread electrification of the
transportation and large-scale energy storage industries. Nickel (Ni)-rich
layered oxide cathodes, such as LiNi$_{x}$Mn$_{y}$Co$_{z}$O$_{2}$ (NMC, x >
0.8), have gained popularity due to their high specific capacities and lower
cobalt content. However, the standard polycrystalline morphology suffers from
accelerated degradation at voltages above 4.2 V versus graphite, due to its
increased mechanical and chemical instability. Single-crystal NMC (SC-NMC) has
emerged as a promising morphology for suppressing the mechanical instability by
preventing intergranular cracking; however, robust methods of understanding its
chemical degradation pathways are required. We demonstrate how a
high-throughput data collection strategy unlocks the ability to perform 2D and
3D ptychography in minutes, where it currently requires hours, and combine this
with X-ray absorption near edge spectroscopy (XANES) to visualise the local Ni
oxidation state behaviour in SC-NMC811, with nanometre-scale spatial
resolution, and use this as a proxy for state-of-charge. By employing this
technique at various stages during lifetime cycling to high voltages (>4.2 V),
direct mapping of chemical degradation along the Li channels, identification of
nucleation sites, and observation of Ni oxidation state heterogeneities across
both electrode and particle scales can be achieved. We further correlate these
heterogeneities with the formation and growth of the rocksalt phase and
oxygen-induced planar gliding. This methodology will advance the fundamental
understanding of how high-Ni layered oxide materials chemically degrade during
high-voltage operation, guiding the design of more durable battery materials.

Universal dynamics and microwave control of programmable cavity electro-optic frequency combs

Authors: Yunxiang Song, Tianqi Lei, Yanyun Xue, Andrea Cordaro, Michael Haas, Guanhao Huang, Xudong Li, Shengyuan Lu, Leticia Magalhaes, Jiayu Yang, Matthew Yeh, Xinrui Zhu, Neil Sinclair, Qihuang Gong, Yaowen Hu, Marko Loncar

Published: July 29, 2025

Electro-optic (EO) frequency combs are foundational for metrology and
spectroscopy. Specifically, microresonator-based cavity EO combs are
distinguished by efficient sideband generation, precisely controlled by
microwave signals, enabling high-performance integrated frequency references
and pulse sources. However, the apparent simplicity of these devices, often
described by the EO modulation-induced coupling of nearest-neighbor cavity
modes, has limited investigations of their fundamental physics, thereby
restricting their full potential. Here, we uncover the universal dynamics and
complete frequency lattice connectivity underpinning cavity EO microcombs, as
well as characterize the full space of nonlinear optical states, controlled by
modulation depth and optical detuning, using the thin-film lithium niobate
photonic platform. Leveraging this understanding, we design complex long-range
couplings between cavity modes to realize programmable spectro-temporal shaping
of the generated combs and pulses. We achieve three technological advances,
including repetition-rate flexibility, substantial comb bandwidth extension
beyond traditional scaling laws, and resonantly-enhanced flat-top spectrum. Our
results provide physical insights for synchronously driven cavity-based EO
systems, broadly defined, paving the way for electrically controlled and
electrically enhanced comb generators for next-generation photonic
applications.

High-efficiency and broadband coherent optical comb generation in integrated X-cut lithium niobate microresonators

Authors: Yunxiang Song, Zongda Li, Xinrui Zhu, Norman Lippok, Miro Erkintalo, Marko Loncar

Published: July 28, 2025

The ability to generate efficient and coherent frequency combs using photonic
integrated circuits offers tremendous potential for a range of applications. In
particular, "microcombs" based on chip-integrated resonators are poised to
revolutionize optical communication, computation, and sensing systems,
especially when paired with fast electro-optic (EO) devices. X-cut thin-film
lithium niobate (TFLN) is a promising platform for developing the
next-generation of microcomb-driven integrated photonic systems, providing a
diversity of functionalities through combined $\chi^{(3)}$ and EO
nonlinearities. In this context, normal-dispersion Kerr microcombs are
critically needed because of their standout advantages, yet this dispersion
regime remains unexplored for comb generation on X-cut TFLN. Here, we leverage
ultralow-loss photonic waveguides, as well as strategic resonator designs that
allow us to tailor Raman effects and engineer desired spatial mode
interactions, for the robust generation of normal-dispersion Kerr microcombs.
Specifically, we show microcombs that substantially surpass state-of-the-art
bright cavity soliton and EO microcombs on X-cut TFLN in key performance
metrics. Additionally, we demonstrate a novel microcomb whose existence is
underpinned by both normal-dispersion Kerr dynamics and stimulated Raman
scattering, in a single spatial mode of a microresonator. This microcomb
manifests itself as two interleaved frequency combs centered about the pump and
Stokes frequencies, resulting in extended spectral spans. Our work will unlock
high-speed and low energy consumption photonic circuits for communications,
frequency synthesis, and signal processing enabled by a monolithic microcomb
technology, while also stimulating further investigations of new nonlinear
states that may synergize the strong hybrid nonlinearities unique to X-cut
TFLN.

Monolithically Integrated Optical Convolutional Processors on Thin Film Lithium Niobate

Authors: Ruixue Liu, Rongbo Wu, Yong Zheng, Yuan Ren, Boyang Nan, Min Wang, Yunpeng Song, Ya Cheng

Published: July 28, 2025

Photonic neural networks (PNNs) of sufficiently large physical dimensions and
high operation accuracies are envisaged as an ideal candidate for breaking the
major bottlenecks in the current artificial intelligence architectures in terms
of latency, energy efficiency and computational power. To achieve this vision,
it is of vital importance to scale up the PNNs and in the meantime reduce the
high demand on the dimensions required by the PNNs. The underlying cause of
this strategy is the enormous gap between the scales of photonic and electronic
integrated circuits. Here, we demonstrate monolithically integrated optical
convolutional processors on thin film lithium niobate (TFLN) to enable
large-scale programmable convolution kernels and in turn greatly reduce the
dimensions required by the subsequent fully connected layers. Experimental
validation achieves high classification accuracies of 96%/86% on the
MNIST/Fashion-MNIST datasets and 84.6% on the AG News dataset, while
dramatically reducing the required subsequent fully connected layer dimensions
to 196x10 (from 784x10) and 175x4 (from 800x4), respectively. Furthermore, our
devices can be driven by commercial field-programmable gate array (FPGA)
systems, a unique advantage in addition to their scalable channel number and
kernel size, our architecture provides a solution to build practical machine
learning photonic devices.

Lithium in V505 Per

Authors: G. Koenigsberger, W. Schmutz, C. Pilachowski, A. R. Mejia-Nava, D. Sikorski, M. Cordero

Published: July 26, 2025

We determine the surface lithium abundance of the eclipsing binary components
in V 505 Per (HIP 10961), A(Li)=2.65+/-0.07 and 2.35+/-0.1, which supports the
rather unexpected conclusion that their surface Li abundances differ. We find
effective temperatures 6600 K + 6550 K (~150 K higher than previously
reported), which place the stars at the hot limit of the Lithium Dip, thus
aleviating the previously suggested discrepancy with cluster stars of similar
ages and temperatures. These temperatures are also more consistent with the
system's Gaia spectral energy distribution. Our iron abundances,
[Fe/H]=-0.15+\-0.07 and -0.25+\-0.1, agree with predictions of the higher
temperatures deduced from our spectra and from evolutionary tracks. The surface
rotation rate implied by our line profiles, 12.5+\-1 km/s, is smaller than the
synchronous rotation rate, a curious result given the circular orbit and the
age of the system.

Human-AI Synergy in Adaptive Active Learning for Continuous Lithium Carbonate Crystallization Optimization

Authors: Shayan S. Mousavi Masouleh, Corey A. Sanz, Ryan P. Jansonius, Cara Cronin, Jason E. Hein, Jason Hattrick-Simpers

Published: July 25, 2025

As demand for high-purity lithium surges with the growth of the electric
vehicle (EV) industry, cost-effective extraction from lower-grade North
American sources like the Smackover Formation is critical. These resources,
unlike high-purity South American brines, require innovative purification
techniques to be economically viable. Continuous crystallization is a promising
method for producing battery-grade lithium carbonate, but its optimization is
challenged by a complex parameter space and limited data. This study introduces
a Human-in-the-Loop (HITL) assisted active learning framework to optimize the
continuous crystallization of lithium carbonate. By integrating human expertise
with data-driven insights, our approach accelerates the optimization of lithium
extraction from challenging sources. Our results demonstrate the framework's
ability to rapidly adapt to new data, significantly improving the process's
tolerance to critical impurities like magnesium from the industry standard of a
few hundred ppm to as high as 6000 ppm. This breakthrough makes the
exploitation of low-grade, impurity-rich lithium resources feasible,
potentially reducing the need for extensive pre-refinement processes. By
leveraging artificial intelligence, we have refined operational parameters and
demonstrated that lower-grade materials can be used without sacrificing product
quality. This advancement is a significant step towards economically harnessing
North America's vast lithium reserves, such as those in the Smackover
Formation, and enhancing the sustainability of the global lithium supply chain.

Deep learning-enabled large-scale analysis of particle geometry-lithiation correlations in battery cathode materials

Authors: Binbin Lin, Luis J. Carrillo, Xiang-Long Peng, Wan-Xin Chen, David A. Santosb, Sarbajit Banerjeeb, Bai-Xiang Xu

Published: July 24, 2025

A deep learning model is employed to address the challenging problem of V2O5
nanoparticle segmentation and the correlation between the chemical composition
and the geometrical features of lithiated V2O5 nanoparticles as an exemplar of
a phase-transforming battery cathode material. First, the deep learning-enabled
segmentation model is integrated with the singular value decomposition
technique and a spectral database to generate accurate composition and phase
maps capturing lithiation heterogeneities as imaged using scanning transmission
X-ray microscopy. These phase maps act as the output properties for correlation
analysis. Subsequently, the quantitative influences of the geometrical features
of nanoparticles such as the particle size (i.e., projected perimeter and
area), the aspect ratio, circularity, convexity, and orientation on the
lithiation phase maps are revealed. These findings inform strategies to improve
lithiation uniformity and reduce stress in phase-transforming lithium battery
materials via optimized particle geometry.

Ultrabroadband Integrated Photonics Empowering Full-Spectrum Adaptive Wireless Communications

Authors: Zihan Tao, Haoyu Wang, Hanke Feng, Yijun Guo, Bitao Shen, Dan Sun, Yuansheng Tao, Changhao Han, Yandong He, John Bowers, Haowen Shu, Cheng Wang, Xingjun Wang

Published: July 24, 2025

The forthcoming sixth-generation (6G) and beyond (XG) wireless networks are
poised to operate across an expansive frequency range from microwave,
millimeter-wave to terahertz bands to support ubiquitous connectivity in
diverse application scenarios. This necessitates a one-size-fits-all hardware
solution that can be adaptively reconfigured within this wide spectrum to
support full-band coverage and dynamic spectrum management. However, existing
electrical or photonic-assisted wireless communication solutions see
significant challenges in meeting this demand due to the limited bandwidths of
individual devices and the intrinsically rigid nature of their system
architectures. Here, we demonstrate adaptive wireless communications over an
unprecedented frequency range spanning over 100 GHz, driven by a universal
thin-film lithium niobate (TFLN) photonic wireless engine. Leveraging the
strong Pockels effect and excellent scalability of the TFLN platform, we
achieve monolithic integration of essential functional elements, including
baseband modulation, broadband wireless-photonic conversion, and reconfigurable
carrier/local signal generation. Powered by broadband tunable optoelectronic
oscillators, our signal sources operate across a record-wide frequency range
from 0.5 GHz to 115 GHz with high frequency stability and consistent coherence.
Based on the broadband and reconfigurable integrated photonic solution, we
realize, for the first time, full-link wireless communication across 9
consecutive bands, achieving record lane speeds of up to 100 Gbps. The
real-time reconfigurability further enables adaptive frequency allocation, a
crucial capability to ensure enhanced reliability in complex spectrum
environments. Our proposed system marks a significant step towards future
full-spectrum and omni-scenario wireless networks.

Microstructure of Silicon Anodes in Solid-State Batteries -- From Crystalline to Amorphous

Authors: Shamail Ahmed, Federico Rossi, Hanyu Huo, Johannes Haust, Franziska Hueppe, Juergen Belz, Andreas Beyer, Juergen Janek, Kerstin Volz

Published: July 22, 2025

Silicon offers great promise as a potential anode active material and the
optimum alternative to lithium metal in all-solid-state lithium-ion batteries.
However, its practical application is limited by severe volume expansion
(~300%) during lithiation, leading to cracking upon delithiation. In this
study, we investigated the microstructural evolution of microcrystalline
silicon electrodes in a solid-electrolyte-free environment using cryogenic
scanning transmission electron microscopy (STEM) during electrochemical
cycling. A controlled workflow prevents ambient exposure, and cryo-TEM ensures
structural integrity. After the first lithiation, the electrode shows a
heterogeneous mix of crystalline Li15Si4, various amorphous LixSi phases, and
residual crystalline silicon. After delithiation, the structure becomes
predominantly amorphous with thread-like features and minimal remaining
crystallinity. By the 10th delithiation, the microstructure is more uniform,
with thread-like regions mainly at grain boundaries. Our results reveal that
starting from a crystalline phase, a stationary microstructure emerges in bulk
silicon only after several cycles. Thus, to have a more controlled behavior of
the electrode and minimize cracking, the starting material should be carefully
chosen along with an optimized electrode architecture to help stabilize the
microstructure throughout cycling.