My Customized Papers (Auto-Filtered)

[ScienceDirect Publication: Journal of Energy Storage] Hollow nanofiber ion conductor protective layer on Zn metal anode for long-term stable zinc battery

ScienceDirect Publication: Journal of Energy Storage — Wed, 31 Dec 2025 12:41:33 GMT

Publication date: 10 February 2026

Source: Journal of Energy Storage, Volume 146

Author(s): Mengfei Sun, Zumin Zhang, Yang Su, Wensheng Yu, Xiangting Dong, Dongtao Liu, Xinlu Wang, Gaopeng Li, Jinxian Wang

[ScienceDirect Publication: Journal of Energy Storage] Alkaline-compatible polyaniline/graphene negative electrode for ultrahigh-energy all-solid-state asymmetric supercapacitors

ScienceDirect Publication: Journal of Energy Storage — Wed, 31 Dec 2025 12:41:33 GMT

Publication date: 10 February 2026

Source: Journal of Energy Storage, Volume 146

Author(s): Aizhen Xu, Li Yin, Shaoqing Zhang, Zhiyi Zhao, Wenna Lv, Yuanyu Zhu, Yujun Qin

[Wiley: Angewandte Chemie International Edition: Table of Contents] Machine Learning–Guided Solvation Engineering of Chiral Viologens for Durable Neutral Aqueous Organic Flow Batteries

Wiley: Angewandte Chemie International Edition: Table of Contents — Wed, 31 Dec 2025 06:56:15 GMT

Angewandte Chemie International Edition, EarlyView.

[Nature Communications] Scalable photonic reservoir computing for parallel machine learning tasks

Nature Communications — Wed, 31 Dec 2025 00:00:00 GMT

Nature Communications, Published online: 31 December 2025; doi:10.1038/s41467-025-67983-z

Neuromorphic computing processes data faster and with less energy than electronics. Here, authors demonstrate a reconfigurable photonic reservoir computer that performs multiple machine learning tasks in parallel at ultrafast rates while using extremely low energy per operation.

[Nature Machine Intelligence] Harnessing the power of single-cell large language models with parameter-efficient fine-tuning using scPEFT

Nature Machine Intelligence — Wed, 31 Dec 2025 00:00:00 GMT

Nature Machine Intelligence, Published online: 31 December 2025; doi:10.1038/s42256-025-01170-z

He et al. present a parameter-efficient fine-tuning method for single-cell language models that improves performance on unseen diseases, treatments and cell types.

[Nature Machine Intelligence] Assessing the potential of deep learning for protein–ligand docking

Nature Machine Intelligence — Wed, 31 Dec 2025 00:00:00 GMT

Nature Machine Intelligence, Published online: 31 December 2025; doi:10.1038/s42256-025-01160-1

Morehead et al. introduce the benchmark PoseBench and evaluate the strengths and limitations of current AI-based protein–ligand docking and structure prediction methods.

[ChemRxiv] Sensing the Acidity of Hydrogen Bond Networks

ChemRxiv — Wed, 31 Dec 2025 00:00:00 GMT

The reactivity of hydrogen bond networks (HBNs) is critical to many chemical and biological scenarios. When the HBNs are under constraint, hydrogen bond strength and acidity are affected significantly. HBNs exhibit cooperativity, where connections formed in one part of the HBN influence its behavior elsewhere. We combined experimental and computational approaches to examine the growth of the HBNs of water and hexafluoroisopropanol (HFIP), constrained by an aprotic cosolvent. We independently employed vibrational frequency shift of an acetonitrile probe, 1H NMR chemical shift of an aniline probe, and molecular dynamics with machine learning interatomic potentials, to demonstrate the increase in the hydrogen bond strength with the growth of the HBNs. Finally, using vibrational spectroscopy of a titratable probe, we established that not only the hydrogen bond strength, but also the acidity of HFIP is affected by the changes in the network geometry. These results enable the engineering and measurement of HBNs in confined environments with tailored acidity.

[ChemRxiv] Thiol-bearing tertiary alkylammonium chloride for regulation of PbI2 excess in FAPbI3 perovskite solar cells

ChemRxiv — Wed, 31 Dec 2025 00:00:00 GMT

One of the key strategies for record photovoltaic efficiencies in metal halide perovskite solar cells is the addition of PbI2 excess in a stoichiometric perovskite solution which controls crystallization, passivates defects and induces a preferred orientation in the perovskite layer. However, residual PbI2, typically found in the perovskite layer after crystallization, generates non-radiative recombination centres and promotes ion migration under light and heating stress, thus accelerating performance loss. To mitigate the above issues, a common strategy is the post-deposition of organic ammonium salts which interact in situ with residual PbI2. Here, we adopt a multifunctional alkylammonium salt, 2-diethylaminoethanethiol hydrochloride (DEAET), in which both the thiol (–SH) and protonated tertiary amine groups can strongly bind to PbI₂. Upon deposition of DEAET on top of FAPbI3 film, we show that DEAT decreases the percentage of residual PbI2 by 40% and totally eliminates Pb0. These two effects lead to enhanced radiative recombination, proving a net passivation effect, while chemical analysis (FTIR and liquid-state NMR) explains that this is due to strong interactions between tertiary protonated ammonium (-NH+) and thiol (-SH) groups of DEAT with under-coordinated Pb2+. The stabilization of FAPbI3 black phase along with the establishment of a solid barrier to impede the infiltration of moisture into the perovskite layer over time lead to enhanced operational stability for the as-fabricated solar cells. The encouraging findings of this study lay the foundation for the utilization of tertiary ammonium thiol-based salts as efficient agents for interface engineering in perovskite solar cells.

[ChemRxiv] LAMMPS-ANI: Large Scale Molecular Dynamics Simulations +My Customized Papers (Auto-Filtered)</h1> <p>ACS Energy Letters: Latest Articles (ACS Publications) — Wed, 31 Dec 2025 12:54:38 GMT</p> <p></p><div><cite>ACS Energy Letters</cite></div><div>DOI: 10.1021/acsenergylett.5c03368</div> </article> <article> <h1>[ScienceDirect Publication: Journal of Energy Storage] Hollow nanofiber ion conductor protective layer on Zn metal anode for long-term stable zinc battery</h1> <p>ScienceDirect Publication: Journal of Energy Storage — Wed, 31 Dec 2025 12:41:33 GMT</p> <p>Publication date: 10 February 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 146</p><p>Author(s): Mengfei Sun, Zumin Zhang, Yang Su, Wensheng Yu, Xiangting Dong, Dongtao Liu, Xinlu Wang, Gaopeng Li, Jinxian Wang</p> </article> <article> <h1>[ScienceDirect Publication: Journal of Energy Storage] Alkaline-compatible polyaniline/graphene negative electrode for ultrahigh-energy all-solid-state asymmetric supercapacitors</h1> <p>ScienceDirect Publication: Journal of Energy Storage — Wed, 31 Dec 2025 12:41:33 GMT</p> <p>Publication date: 10 February 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 146</p><p>Author(s): Aizhen Xu, Li Yin, Shaoqing Zhang, Zhiyi Zhao, Wenna Lv, Yuanyu Zhu, Yujun Qin</p> </article> <article> <h1>[Wiley: Angewandte Chemie International Edition: Table of Contents] Machine Learning–Guided Solvation Engineering of Chiral Viologens for Durable Neutral Aqueous Organic Flow Batteries</h1> <p>Wiley: Angewandte Chemie International Edition: Table of Contents — Wed, 31 Dec 2025 06:56:15 GMT</p> Angewandte Chemie International Edition, EarlyView. </article> <article> <h1>[Nature Communications] Scalable photonic reservoir computing for parallel machine learning tasks</h1> <p>Nature Communications — Wed, 31 Dec 2025 00:00:00 GMT</p> <p>Nature Communications, Published online: 31 December 2025; <a href="https://www.nature.com/articles/s41467-025-67983-z">doi:10.1038/s41467-025-67983-z</a></p>Neuromorphic computing processes data faster and with less energy than electronics. Here, authors demonstrate a reconfigurable photonic reservoir computer that performs multiple machine learning tasks in parallel at ultrafast rates while using extremely low energy per operation. </article> <article> <h1>[Nature Machine Intelligence] Harnessing the power of single-cell large language models with parameter-efficient fine-tuning using scPEFT</h1> <p>Nature Machine Intelligence — Wed, 31 Dec 2025 00:00:00 GMT</p> <p>Nature Machine Intelligence, Published online: 31 December 2025; <a href="https://www.nature.com/articles/s42256-025-01170-z">doi:10.1038/s42256-025-01170-z</a></p>He et al. present a parameter-efficient fine-tuning method for single-cell language models that improves performance on unseen diseases, treatments and cell types. </article> <article> <h1>[Nature Machine Intelligence] Assessing the potential of deep learning for protein–ligand docking</h1> <p>Nature Machine Intelligence — Wed, 31 Dec 2025 00:00:00 GMT</p> <p>Nature Machine Intelligence, Published online: 31 December 2025; <a href="https://www.nature.com/articles/s42256-025-01160-1">doi:10.1038/s42256-025-01160-1</a></p>Morehead et al. introduce the benchmark PoseBench and evaluate the strengths and limitations of current AI-based protein–ligand docking and structure prediction methods. </article> <article> <h1>[ChemRxiv] Sensing the Acidity of Hydrogen Bond Networks</h1> <p>ChemRxiv — Wed, 31 Dec 2025 00:00:00 GMT</p> The reactivity of hydrogen bond networks (HBNs) is critical to many chemical and biological scenarios. When the HBNs are under constraint, hydrogen bond strength and acidity are affected significantly. HBNs exhibit cooperativity, where connections formed in one part of the HBN influence its behavior elsewhere. We combined experimental and computational approaches to examine the growth of the HBNs of water and hexafluoroisopropanol (HFIP), constrained by an aprotic cosolvent. We independently employed vibrational frequency shift of an acetonitrile probe, 1H NMR chemical shift of an aniline probe, and molecular dynamics with machine learning interatomic potentials, to demonstrate the increase in the hydrogen bond strength with the growth of the HBNs. Finally, using vibrational spectroscopy of a titratable probe, we established that not only the hydrogen bond strength, but also the acidity of HFIP is affected by the changes in the network geometry. These results enable the engineering and measurement of HBNs in confined environments with tailored acidity. </article> <article> <h1>[ChemRxiv] Thiol-bearing tertiary alkylammonium chloride for regulation of PbI2 excess in FAPbI3 perovskite solar cells</h1> <p>ChemRxiv — Wed, 31 Dec 2025 00:00:00 GMT</p> One of the key strategies for record photovoltaic efficiencies in metal halide perovskite solar cells is the addition of PbI2 excess in a stoichiometric perovskite solution which controls crystallization, passivates defects and induces a preferred orientation in the perovskite layer. However, residual PbI2, typically found in the perovskite layer after crystallization, generates non-radiative recombination centres and promotes ion migration under light and heating stress, thus accelerating performance loss. To mitigate the above issues, a common strategy is the post-deposition of organic ammonium salts which interact in situ with residual PbI2. Here, we adopt a multifunctional alkylammonium salt, 2-diethylaminoethanethiol hydrochloride (DEAET), in which both the thiol (–SH) and protonated tertiary amine groups can strongly bind to PbI₂. Upon deposition of DEAET on top of FAPbI3 film, we show that DEAT decreases the percentage of residual PbI2 by 40% and totally eliminates Pb0. These two effects lead to enhanced radiative recombination, proving a net passivation effect, while chemical analysis (FTIR and liquid-state NMR) explains that this is due to strong interactions between tertiary protonated ammonium (-NH+) and thiol (-SH) groups of DEAT with under-coordinated Pb2+. The stabilization of FAPbI3 black phase along with the establishment of a solid barrier to impede the infiltration of moisture into the perovskite layer over time lead to enhanced operational stability for the as-fabricated solar cells. The encouraging findings of this study lay the foundation for the utilization of tertiary ammonium thiol-based salts as efficient agents for interface engineering in perovskite solar cells. </article> <article> <h1>[ChemRxiv] LAMMPS-ANI: Large Scale Molecular Dynamics Simulations with ANI Neural Network Potential</h1> <p>ChemRxiv — Wed, 31 Dec 2025 00:00:00 GMT</p> Machine Learning Interatomic Potentials (MLIPs), trained with Quantum Mechanics data, can model potential energy surfaces for molecular systems with very high accuracy and extreme speedups compared to reference quantum calculations, offering a powerful tool for studying complex chemical and biological @@ -12,7 +12,7 @@ efficiency. We highlight our work in large-scale molecular dynamics using ANI po benchmark results for water boxes (up to 100 million atoms) and a solvated HIV capsid (44 million atoms). We also present results for accurately simulating complex reaction processes at unprecedented scales, such as methane combustion (300 thousand atoms) and early Earth chemistry experiment (228 -thousand atoms) demonstrating the spontaneous formation of glycine. </article> <article> <h1>[ScienceDirect Publication: Acta Materialia] Inverse Design of High-Performance Glasses Through an Encoder-Decoder Machine Learning Approach Toward Materials Discovery: Application to Oxynitride Glasses</h1> <p>ScienceDirect Publication: Acta Materialia — Tue, 30 Dec 2025 18:31:09 GMT</p> <p>Publication date: Available online 29 December 2025</p><p><b>Source:</b> Acta Materialia</p><p>Author(s): Alexis Duval, Eric Robin, Patrick Houizot, Tanguy Rouxel</p> </article> <article> <h1>[ScienceDirect Publication: Journal of Energy Storage] Machine learning-aided prediction and optimization of specific capacitance in functionalized GO-based Mn/Fe co-doped Bi<sub>2</sub>O<sub>3</sub> nanocomposites</h1> <p>ScienceDirect Publication: Journal of Energy Storage — Tue, 30 Dec 2025 12:42:28 GMT</p> <p>Publication date: 10 February 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 146</p><p>Author(s): Vijay A. Mane, Kartik M. Chavan, Sushant S. Munde, Dnyaneshwar V. Dake, Nita D. Raskar, Ramprasad B. Sonpir, Pravin V. Dhole, Ketan P. Gattu, Sandeep B. Somvanshi, Pavan R. Kayande, Jagruti S. Pawar, Babasaheb N. Dole</p> </article> <article> <h1>[ScienceDirect Publication: Journal of Energy Storage] Time-resolved impedance spectroscopy analysis of stable lithium iron phosphate cathode with enhanced electronic/ionic conductivity and ion diffusion characteristics</h1> <p>ScienceDirect Publication: Journal of Energy Storage — Tue, 30 Dec 2025 12:42:28 GMT</p> <p>Publication date: 10 February 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 146</p><p>Author(s): Jiguo Tu, Yan Li, Libo Chen, Dongbai Sun</p> </article> <article> <h1>[Wiley: Small Methods: Table of Contents] Standardization and Machine Learning Prediction of Tafel Slope of Pt‐Based Nanocatalysts for High‐Performance HER Catalyst Development</h1> <p>Wiley: Small Methods: Table of Contents — Tue, 30 Dec 2025 12:06:41 GMT</p> Small Methods, EarlyView. </article> <article> <h1>[cond-mat updates on arXiv.org] Thermodynamic Phase Stability, Structural, Mechanical, Optoelectronic, and Thermoelectric Properties of the III-V Semiconductor AlSb for Energy Conversion Applications</h1> <p>ChemRxiv — Wed, 31 Dec 2025 00:00:00 GMT</p> arXiv:2512.22277v1 Announce Type: new +thousand atoms) demonstrating the spontaneous formation of glycine. </article> <article> <h1>[Nature Communications] Domain oriented universal machine learning potential enables fast exploration of chemical space of battery electrolytes</h1> <p>Nature Communications — Wed, 31 Dec 2025 00:00:00 GMT</p> <p>Nature Communications, Published online: 31 December 2025; <a href="https://www.nature.com/articles/s41467-025-67982-0">doi:10.1038/s41467-025-67982-0</a></p>Efficient modeling of battery electrolytes is limited by the accuracy-cost trade-off. Here, authors develop a universal machine learning potential to accurately calculate transport and solvation properties across a broad chemical space. </article> <article> <h1>[ChemRxiv] Revealing amyloid-β peptide isoforms, including post-translationally modified species, using electrochemical profiling with a dual-electrode set-up</h1> <p>ChemRxiv — Wed, 31 Dec 2025 00:00:00 GMT</p> The amyloid-β (Aβ) peptides are crucial biomarkers for the diagnosis of Alzheimer's disease (AD), the most common neurodegenerative disease. The high diversity of the Aβ family provides a significant challenge for recognizing various Aβ forms, which may differ by a single amino acid or a post-translational modification. Such variation at the N-terminus of Aβ peptides leads to changes in their properties associated with typical AD biomolecular mechanisms, such as aggregation or generation of reactive oxygen species (ROS). In this work, a novel method for discriminating Aβ peptides with physiologically occurring truncations and modifications at their N-termini, based on the electrochemical profiling of their Cu(II) complexes, is presented. A dual-electrode set-up incorporating both glassy carbon and gold electrodes, together with Differential Pulse Voltammetry (DPV), was employed to generate unique electrochemical profiles, which were subsequently analyzed using chemometric techniques, including Principal Component Analysis (PCA) for data exploration, and Partial Least Squares Discriminant Analysis (PLS-DA) for classification. By combining electrochemical measurements with machine learning algorithms for pattern recognition, we successfully differentiated the studied Aβ forms, Aβ1-16, Aβ3-16, Aβpyr3-16, Aβ4-16, Aβ5-16, Aβ11-16, and Aβpyr11-16. The integration of machine learning not only enhances detection accuracy but also identifies subtle patterns that could support early-stage diagnostics. These findings support the ongoing development of analytical strategies that seek to improve the detection range and accuracy of Aβ peptides identification in Alzheimer’s disease research. </article> <article> <h1>[Cell Reports Physical Science] Hierarchical optimization of perovskite solar cell fabrication via step-by-step machine learning</h1> <p>Cell Reports Physical Science — Wed, 31 Dec 2025 00:00:00 GMT</p> Pu et al. report a hierarchical multi-target Bayesian optimization (MTBO) framework that optimizes the electrospray deposition process for perovskite solar cells. By integrating adaptive constraints and prioritizing thin-film quality across multiple fabrication stages, MTBO efficiently identifies feasible, high-performance conditions, enabling 1.63 eV FA0.82Cs0.18Pb(I0.86Br0.11Cl0.03)3 devices with a champion efficiency of 21.95%. </article> <article> <h1>[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Machine Learning Applications in Predicting Friction Properties of Bearing Steel: A Review</h1> <p>ACS Materials Letters: Latest Articles (ACS Publications) — Tue, 30 Dec 2025 19:59:57 GMT</p> <p></p><div><cite>ACS Materials Letters</cite></div><div>DOI: 10.1021/acsmaterialslett.5c01047</div> </article> <article> <h1>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Machine Learning-Guided Discovery of Sterically Protected High Triplet Exciplex Hosts for Ultra-Bright Green OLEDs</h1> <p>Journal of the American Chemical Society: Latest Articles (ACS Publications) — Tue, 30 Dec 2025 19:03:11 GMT</p> <p></p><div><cite>Journal of the American Chemical Society</cite></div><div>DOI: 10.1021/jacs.5c16369</div> </article> <article> <h1>[ScienceDirect Publication: Acta Materialia] Inverse Design of High-Performance Glasses Through an Encoder-Decoder Machine Learning Approach Toward Materials Discovery: Application to Oxynitride Glasses</h1> <p>ScienceDirect Publication: Acta Materialia — Tue, 30 Dec 2025 18:31:09 GMT</p> <p>Publication date: Available online 29 December 2025</p><p><b>Source:</b> Acta Materialia</p><p>Author(s): Alexis Duval, Eric Robin, Patrick Houizot, Tanguy Rouxel</p> </article> <article> <h1>[ScienceDirect Publication: Journal of Energy Storage] Machine learning-aided prediction and optimization of specific capacitance in functionalized GO-based Mn/Fe co-doped Bi<sub>2</sub>O<sub>3</sub> nanocomposites</h1> <p>ScienceDirect Publication: Journal of Energy Storage — Tue, 30 Dec 2025 12:42:28 GMT</p> <p>Publication date: 10 February 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 146</p><p>Author(s): Vijay A. Mane, Kartik M. Chavan, Sushant S. Munde, Dnyaneshwar V. Dake, Nita D. Raskar, Ramprasad B. Sonpir, Pravin V. Dhole, Ketan P. Gattu, Sandeep B. Somvanshi, Pavan R. Kayande, Jagruti S. Pawar, Babasaheb N. Dole</p> </article> <article> <h1>[ScienceDirect Publication: Journal of Energy Storage] Time-resolved impedance spectroscopy analysis of stable lithium iron phosphate cathode with enhanced electronic/ionic conductivity and ion diffusion characteristics</h1> <p>ScienceDirect Publication: Journal of Energy Storage — Tue, 30 Dec 2025 12:42:28 GMT</p> <p>Publication date: 10 February 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 146</p><p>Author(s): Jiguo Tu, Yan Li, Libo Chen, Dongbai Sun</p> </article> <article> <h1>[Wiley: Small Methods: Table of Contents] Standardization and Machine Learning Prediction of Tafel Slope of Pt‐Based Nanocatalysts for High‐Performance HER Catalyst Development</h1> <p>Wiley: Small Methods: Table of Contents — Tue, 30 Dec 2025 12:06:41 GMT</p> Small Methods, EarlyView. </article> <article> <h1>[cond-mat updates on arXiv.org] Thermodynamic Phase Stability, Structural, Mechanical, Optoelectronic, and Thermoelectric Properties of the III-V Semiconductor AlSb for Energy Conversion Applications</h1> <p>cond-mat updates on arXiv.org — Tue, 30 Dec 2025 05:00:00 GMT</p> arXiv:2512.22277v1 Announce Type: new Abstract: This study presents a first principles investigation of the structural, thermodynamic, electronic, optical and thermoelectric properties of aluminum antimonide (AlSb) in its cubic (F-43m) and hexagonal (P63mc) phases. Both structures are dynamically and mechanically stable, as confirmed by phonon calculations and the Born Huang criteria. The lattice constants obtained using the SCAN and PBEsol functionals show good agreement with experimental data. The cubic phase exhibits a direct band gap of 1.66 to 1.78 eV, while the hexagonal phase shows a band gap of 1.48 to 1.59 eV, as confirmed by mBJ and HSE06 calculations. Under external pressure, the band gap decreases in the cubic phase and increases in the hexagonal phase due to different s p orbital hybridization mechanisms. The optical absorption coefficient reaches 1e6 cm-1, which is comparable to or higher than values reported for other III V semiconductors. The Seebeck coefficient exceeds 1500 microV per K under intrinsic conditions, and the thermoelectric performance improves above 600 K due to enhanced phonon scattering and lattice anharmonicity. The calculated formation energies (-1.316 eV for F-43m and -1.258 eV for P63mc) confirm that the cubic phase is thermodynamically more stable. The hexagonal phase exhibits higher anisotropy and lower lattice stiffness, which is favorable for thermoelectric applications. These results demonstrate the strong interplay between crystal symmetry, phonon behavior and charge transport, and provide useful guidance for the design of AlSb based materials for optoelectronic and energy conversion technologies. </article> <article> <h1>[cond-mat updates on arXiv.org] The Role of THz Phonons in the Ionic Conduction Mechanism of $Li_7La_3Zr_2O_{12}$ Polymorphs</h1> <p>cond-mat updates on arXiv.org — Tue, 30 Dec 2025 05:00:00 GMT</p> arXiv:2512.22427v1 Announce Type: new Abstract: Superionic conduction in solid-state materials is governed not only by static factors, such as structure and composition, but also by dynamic interactions between the mobile ion and the crystal lattice. Specifically, the dynamics of lattice vibrations, or phonons, have attracted interest because of their hypothesized ability to facilitate superionic conduction. However, direct experimental measurement of the role of phonons in ionic conduction is challenging due to the fast intrinsic timescales of ion hopping and the difficulty of driving relevant phonon modes, which often lie in the low-energy THz regime. To overcome these limitations, we use laser-driven ultrafast impedance spectroscopy (LUIS). LUIS resonantly excites phonons using a THz field and probes ion hopping with picosecond time resolution. We apply LUIS to understand the dynamical role of phonons in $Li_7La_3Zr_2O_{12}$ (LLZO). When in its cubic phase (c-LLZO), this garnet-type solid electrolyte has an ionic conductivity two orders of magnitude greater than its tetragonal phase (t-LLZO). T-LLZO is characterized by an ordered and filled $Li^+$ sublattice necessitating synchronous ion hopping. In contrast, c-LLZO is characterized by a disordered and vacancy-rich $Li^+$ sublattice, and has a conduction mechanism dominated by single hops. We find that, upon excitation of phonons in the 0.5-7.5 THz range, the impedance of t-LLZO experiences a longer ion hopping decay signal in comparison to c-LLZO. The results suggest that phonon-mediated ionic conduction by THz modes may lead to larger ion displacements in ordered and fully occupied mobile ion sublattices as opposed to those that are disordered and vacancy-rich. Overall, this work highlights the interplay between static and dynamic factors that enables improved ionic conductivity in otherwise poorly conducting inorganic solids. </article> <article> <h1>[cond-mat updates on arXiv.org] Thermally Activated Non-Affine Rearrangements in Amorphous Glass: Emergence of Intrinsic Length Scales</h1> <p>cond-mat updates on arXiv.org — Tue, 30 Dec 2025 05:00:00 GMT</p> arXiv:2512.22530v1 Announce Type: new Abstract: We present a systematic study of temperature-driven nonaffine rearrangements in a model amorphous solid across the full thermodynamic range, from a high-temperature liquid, through supercooled and sub-glass regimes, into deep glassy states. The central result is a quantitative characterisation of the componentwise nonaffine residual displacements, obtained by subtracting local affine maps from particle displacements. For each state point the tails of the probability distributions of these nonaffine components display clear exponential decay; linear fits to the logarithm of the tail region yield characteristic nonaffine length scales {\xi}NA,x and {\xi}NA,y , which quantify the spatial extent of purely nonaffine, local rearrangements. To compare with other length scales, we compute van Hove distributions Gx(ux), Gy (uy ) which capture the full particle displacement field (coherent affine-like motion plus residuals). A robust, key finding is that the van Hove length scale consistently exceeds the filtered nonaffine length scale, i.e. {\xi}VH > {\xi}NA, across all temperatures, state points, and densities we studied. The nonaffine length {\xi}NA quantifies the distance over which complex deformation occurs, specifically nonlinear and anharmonic responses, irreversible (plastic) rearrangements, topological non-recoverable particle rearrangements, and other residual motions that cannot be represented by a local affine map. Moreover, near equality of {\xi}NA,x and {\xi}NA,y in all conditions provides further evidence that nonaffine rearrangements propagate isotropically under thermally driven deformation in contrast to externally driven shear. </article> </main></body></html>