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Volcanic earthquake catalogs are an essential data product used to interpret subsurface volcanic activity and forecast eruptions. Advances in detection techniques (e.g., matched-filtering, machine learning) and relative relocation tools have improved catalog completeness and refined event locations. However, most volcano observatories have yet to incorporate these techniques into their catalog-building workflows. This is due in part to complexities in operationalizing, automating, and calibrating these techniques in a satisfactory way for disparate volcano networks and their varied seismicity. In an effort to streamline the integration of catalog-enhancing tools at the Alaska Volcano Observatory (AVO), we have integrated four popular open-source tools: REDPy, EQcorrscan, HypoDD, and GrowClust. The combination of these tools offers the capability of adding seismic event detections and relocating events in a single workflow. The workflow relies on a combination of standard triggering and cross-correlation clustering (REDPy) to consolidate representative templates used in matched-filtering (EQcorrscan). The templates and their detections are then relocated using the differential time methods provided by HypoDD and/or GrowClust. Our workflow also provides codes to incorporate campaign data at appropriate junctures, and calculate magnitude and frequency index for valid events. We apply this workflow to three datasets: the 2012–2013 seismic swarm sequence at Mammoth Mountain (California), the 2009 eruption of Redoubt Volcano (Alaska), and the 2006 eruption of Augustine Volcano (Alaska); and compare our results with previous studies at each volcano. In general, our workflow provides a significant increase in the number of events and improved locations, and we relate the event clusters and temporal progressions to relevant volcanic activity. We also discuss workflow implementation best practices, particularly in applying these tools to sparse volcano seismic networks. We envision that our workflow and the datasets presented here will be useful for detailed volcano analyses in monitoring and research efforts. 

Abstract Volcanic tremor is a semi‐continuous seismic and/or acoustic signal that occurs at time scales ranging from seconds to years, with variable amplitudes and spectral features. Tremor sources have often been related to fluid movement and degassing processes, and are recognized as a potential geophysical precursor and co‐eruptive geophysical signal. Eruption forecasting and monitoring efforts need a fast, robust method to automatically detect, characterize, and catalog volcanic tremor. Here we develop VOlcano Infrasound and Seismic Spectrogram Network (VOISS‐Net), a pair of convolutional neural networks (one for seismic, one for acoustic) that can detect tremor in near real‐time and classify it according to its spectral signature. Specifically, we construct an extensive data set of labeled seismic and low‐frequency acoustic (infrasound) spectrograms from the 2021–2022 eruption of Pavlof Volcano, Alaska, and use it to train VOISS‐Net to differentiate between different tremor types, explosions, earthquakes and noise. We use VOISS‐Net to classify continuous data from past Pavlof Volcano eruptions (2007, 2013, 2014, 2016, and 2021–2022). VOISS‐Net achieves an 81.2% and 90.0% accuracy on the seismic and infrasound test sets respectively, and successfully characterizes tremor sequences for each eruption. By comparing the derived seismoacoustic timelines of each eruption with the corresponding eruption chronologies compiled by the Alaska Volcano Observatory, our model identifies changes in tremor regimes that coincide with observed volcanic activity. VOISS‐Net can aid tremor‐related monitoring and research by making consistent tremor catalogs more accessible. 

All-solid-state batteries (ASSBs) are viewed as promising next-generation energy storage devices, due to their enhanced safety by replacing organic liquid electrolytes with non-flammable solid-state electrolytes (SSEs). The high ionic conductivity and low Young's modulus of sulfide SSEs make them suitable candidates for commercial ASSBs. Nevertheless, sulfide SSEs are generally reported to be unstable in ambient air. Moreover, instead of gloveboxes used for laboratory scale studies, large scale production of batteries is usually conducted in dry rooms. Thus, this study aims to elucidate the chemical evolution of a sulfide electrolyte, Li 6 PS 5 Cl (LPSCl), during air exposure and to evaluate its dry room compatibility. When LPSCl is exposed to ambient air, hydrolysis, hydration, and carbonate formation can occur. Moreover, hydrolysis can lead to irreversible sulfur loss and therefore LPSCl cannot be fully recovered in the subsequent heat treatment. During heat treatment, exposed LPSCl undergoes dehydration, decomposition of carbonate species, and reformation of the LPSCl phase. Finally, LPSCl was found to exhibit good stability in a dry room environment and was subject to only minor conductivity loss due to carbonate formation. The dry room exposed LPSCl sample was tested in a LiNi 0.8 Co 0.1 Mn 0.1 O 2 |LiIn half-cell, exhibiting no significant loss of electrochemical performance compared with the pristine LPSCl, proving it to be compatible with dry room manufacturing processes. 

Abstract All‐solid‐state sodium ion batteries (AS³iBs) are highly sought after for stationary energy storage systems due to their suitable safety and stability over a wide temperature range. Hard carbon (HC), which is low cost, exhibits a low redox potential, and a high capacity, is integral to achieve a practical large‐scale sodium‐ion battery. However, the energy density of the battery utilizing this anode material is hampered by its low initial Coulombic efficiency (ICE). Herein, two strategies, namely i) additional pyrolysis and ii) presodiation by thermal decomposition of NaBH₄, are explored to improve the ICE of pristine HC. Raman spectroscopy, X‐ray photoelectron spectroscopy, and electrochemical characterizations elucidate that the thermal treatment increases the C_sp2content in the HC structure, while the presodiation supplies the sodium to occupy the intrinsic irreversible sites. Consequently, presodiated HC exhibits an outstanding ICE (>99%) compared to the thermally treated (90%) or pristine HC (83%) in half‐cell configurations. More importantly, AS³iB using presodiated HC and NaCrO₂as the anode and cathode, respectively, exhibits a high ICE of 92% and an initial discharge energy density of . 

Abstract In April 2022, a seismic swarm near Mt. Edgecumbe in southeast Alaska suggested renewed activity at this transform fault volcano, which was last active ≈800 years ago. Previously, thin rhyolitic tephras were deposited 5 and 4 ka. Satellite radar data from 2014 to 2022 resolves line‐of‐sight rapid inflation up to 7.1 cm/yr beginning in August 2018. Bayesian modeling suggests a transcrustal system of a deflating (−0.528 km³) dipping sill at 20 km depth recharging a magma chamber at 10 km (0.222 km³). A near‐vertical conduit could capture the volume difference without noticeable surface deformation. Reanalyzed seismicity, recorded 25 km away, shows increases since July 2019. Magma ascent through ductile material and brittle strain release in a stressed overburden could explain the time delay. Cloud‐native open data and workflows enabled discovery and analysis of this signal within days after going unnoticed for >3 years.