11 Jun 2025

Chemo-Electrochemical Evolution of Cathode–Solid Electrolyte Interface in All-Solid-State Batteries

Abstract: The stability of the interface between the cathode and the solid electrolyte (SE) has been found to be a key determinant of solid-state battery (SSB) performance. While interfacial failure from electro-chemical cycling has been studied, temperature effects on the chemical and electrochemical evolution of interface properties are not well-understood. We utilize a dense additive-free LiCoO2 cathode, which provides controlled morphology and crystallography, and well-known high voltage halide SEs (Li₃InCl₆ and Li₃YCl₆) to eliminate the need for cathode coating to explore the nature of interface deterioration induced by operating at up to 100 °C. By promoting temperature-induced accelerated interfacial failure, we show that at elevated temperatures (>60 °C) and higher states of charge, a significant chemo-electrochemical contribution to interfacial resistance results in rapid cell performance degradation. Our findings show that beyond the well sought-after SE electrochemical voltage stability, the atomic-scale restructuring of the cathode surface interfaced with the SE must be considered when designing stable interfaces.

11 Jun 2025

A Bellwether for Microplastic in Wetland Catchments in the Great Lakes Region

Abstract: This study is intended as a bellwether for the occurrence of microplastics (MPs) in Great Lakes wetlands. In 2020, sediment, surface water, and atmospheric deposition samples were collected from wetland catchments in or near five National Wildlife Refuges (NWRs) in the Great Lakes region: Horicon-WI, Seney-MI, Shiawassee-MI, Ottawa- OH, and Montezuma-NY. Sediment and surface water samples were taken from river, stream, and canal inflows and outflows to and from wetland areas. Atmospheric deposition samples were collected in carboys placed near established rain gauges. These sample sites were chosen as indicators of MP deposition into and out of the region’s wetland systems. MPs were extracted from each sample, enumerated, and categorized by particle morphology and polymer type. Average MP particle abundances in the sediment and surface water samples ranged from 344 to 538 particles kg⁻¹ (dry weight) and 2–68 particles m⁻³, respectively. Atmospheric MP deposition ranged from 5.8 to 22.6 particles m⁻² d⁻¹. Fibers were the most abundant MP particle type found in each sample type (sediment, surface water, and atmospheric deposition), followed by fragments. These results suggest that input and retention of MPs are pervasive in the Great Lakes region and surrounding wetland areas.

11 Jun 2025

Prediction of Waterborne Freight Activity with Automatic Identification System Using Machine Learning

Abstract: This paper addresses latency issues related to publicly available port-level commodity tonnage reports. Predicting commodity tonnage at the port-level, near real time vessel tracking data is used with historical WCS with a machine learning model. Commodity throughput is derived from WCS data which is released publicly approximately two years after collection. This latency presents a challenge for short-term planning and other operational uses. This study leverages near real time vessel tracking data from the AIS data set. LSTM, TCN, and TFT machine learning models are developed using the features extracted from AIS and the historical WCS data. The output of the model is the prediction of the quarterly volume of commodities at port terminals for four quarters in the future. Uncategorized and Categorized models were developed. The uncategorized outperformed the categorized based on the Mean Absolute Percentage Error. The uncategorized LSTM model has the highest accuracy. Results show the model has higher accuracy for port terminals that handle a specific type of vessel, compared to the port terminals handling more than one vessel type. The application of the model enables port authorities and stakeholders to make short-term capacity expansion and infrastructure investment decisions based on commodity volume.

9 Jun 2025

Review of Remote-Sensing Methods for Mapping Riparian and Submerged Aquatic Vegetation: Support for Ecosystem Restoration Monitoring and Flood Risk Management

Abstract: Riparian vegetation, defined as multilayered herbaceous and woody plant communities along river margins or bank edges, and freshwater submerged aquatic vegetation (SAV), described as rooted aquatic plants in shallow rivers, lakes, and estuaries, are key factors influencing the connection between river and floodplain systems. These vegetation types are often used as indicators of riparian health. Current data on riparian vegetation and SAV are essential for addressing future water resource needs, particularly for restoration monitoring and flood risk management. The US Army Corps of Engineers (USACE), as the federal government’s largest water resources development and management agency, requires updated monitoring and assessment methods to support the development, utilization, and conservation of water and related resources. Assessing large riparian corridors involves characterizing baseline conditions, habitat extents, vegetation patterns, and health. Vegetation and habitat data are critical for evaluating the effects of project operations, resource management, and restoration outcomes downstream from USACE dams. However, obtaining such data across large, dynamic, and inaccessible river reaches is challenging. Integrating field-based techniques with remote-sensing technology offers opportunities to map larger areas comprehensively and adapt to future water resource needs. This report reviews re-mote sensing methods for mapping riparian and SAV habitats with emphasis on vegetation characteristics.

9 Jun 2025

KANICE: Kolmogorov-Arnold Networks with Interactive Convolutional Elements

Abstract: We introduce KANICE, a novel neural architecture that com-bines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Convolutional Blocks (ICBs) and KAN linear layers into a CNN framework. This leverages KANs’ universal approximation capabilities and ICBs’ adaptive feature learning. KANICE captures complex, non-linear data relationships while enabling dynamic, context-dependent feature extraction based on the Kolmogorov-Arnold representation theorem. We evaluated KANICE on four datasets: MNIST, Fashion-MNIST, EMNIST, and SVHN, comparing it against standard CNNs, CNN-KAN hybrids, and ICB variants. KANICE consistently outperformed baseline models, achieving 99.35% accuracy on MNIST and 90.05% on the SVHN dataset. Furthermore, we introduce KANICE-mini, a compact variant designed for efficiency. A comprehensive ablation study demonstrates that KANICE-mini achieves comparable performance to KANICE with significantly fewer parameters. KANICE-mini reached 90.00% accuracy on SVHN with 2,337,828 parameters, compared to KAN-ICE’s 25,432,000. This study highlights the potential of KAN-based architectures in balancing performance and computational efficiency in image classification tasks. Our work contributes to research in adaptive neural networks, integrates mathematical theorems into deep learning architectures, and explores the trade-offs between model complexity and performance, advancing computer vision and pattern recognition. The source code for this paper is publicly accessible through our GitHub repository (https://github.com/m-ferdaus/kanice).

5 Jun 2025

Incorporating Natural and Nature-Based Features in an Urban California Creek Through Application of Engineering With Nature® Principles

Purpose: Since its launch in 2021, the Engineering With Nature® (EWN®) program has funded research focused in a variety of environments, particularly along marine and freshwater coasts and fluvial (riverine) systems. Until recently, there has been less focus on applying EWN principles in urban landscapes and watersheds to help manage flood risk, a main civil works mission of the US Army Corps of Engineers (USACE). Natural hazard challenges, including intense rainfall events, are contributing to flooding and prompting the need for more sustainable infrastructure to reduce flood risks in urban areas. This is especially relevant when such nature-based solutions (NBS) are desired by stakeholders who stand to benefit from the project. This technical note documents a USACE Chicago District (LRC) project that supports USACE Los Angeles District (SPL) to incorporate EWN principles in an urban ephemeral creek to reduce flood risk while providing other environmental, social, and economic benefits.

4 Jun 2025

Floridan Aquifer System (FAS) Aquifer Material Collection and Screening: Investigating Arsenic Fate and Transport Under Lab-Simulated Aquifer Storage and Recovery (ASR) Conditions in the FAS—Task A Report

Abstract: The US Army Engineer Research Development Center is leading a laboratory study to quantify arsenic release that could occur during large-scale aquifer storage and recovery (ASR) operations in the anoxic Floridan Aquifer System (FAS). FAS materials containing arsenic must be collected and preserved under anoxic conditions to complete the laboratory study. This report describes collection, preservation, and initial characterization results of FAS material collected. Analysis of water surrounding the FAS material during storage detected some arsenic, suggesting arsenic presence in the solids. In-depth characterization of a single sample confirmed storage conditions were anoxic; no arsenic was detected in surface scrapings collected from the sample solids. Initial characterization results suggested FAS materials collected were suitable for use in the planned laboratory study and that storage methods were suitable for preserving collected materials.

4 Jun 2025

Comprehensive Marsh Model Demonstration—Seven Mile Island Innovation Laboratory: Integrating Hydrodynamic, Morphodynamic, and Vegetation Modeling Components Using the Landlab Toolkit

Abstract: Marshes are highly dynamic landscapes that are shaped through feedbacks between hydrodynamic, morphodynamic, and ecological processes. Future marsh resilience is therefore dependent on the interaction between these different drivers rather than any individual piece. Marshes face a variety of threats, both natural and anthropogenic, resulting in a need for restoration actions that increase survivability. Because many of these threats are unprecedented or acting at unprecedented rates, statistical models do not adequately represent future conditions and require process-based models to better capture the complex interactions between both physical and ecological processes. This report demonstrates how to develop a comprehensive marsh model that integrates tidal flow, morphodynamics, and vegetation growth using the Python based Landlab toolkit. The model was applied to a site within the Seven Mile Island Innovation Laboratory complex in coastal New Jersey.

4 Jun 2025

Developing an Ecosystem Goods and Services Assessment Framework: Products and Resources

Purpose: The Environmental Research Area Review Group has long recognized a need to understand the role of Ecosystem Goods and Services (EGS) in US Army Corps of Engineers (USACE) civil works planning. An EGS Work Unit, funded by the Ecosystem Management and Restoration Research Program (EMRRP), has collaborated for more than a decade to develop many products and resources useful to USACE planners and policy makers. This technical note reviews the body of work produced by this large, diverse, and dedicated team.

3 Jun 2025

Development and Validation of NOAA’s 20-Year Global Wave Ensemble Reforecast

Abstract: A 20-yr wave reforecast was generated based on the NOAA Global Ensemble Forecast System, version 12. It was produced using the same setup as the NCEP’s operational GEFSv12 wave component. The reforecast comprises five members with 1 cycle per day and a forecast range of 16 days. Once a week, it expands to 35 days and 11 members. This paper describes the development of the wave ensemble reforecast, focusing on validation against buoys and altimeters. The statistical analyses demonstrated very good performance in the short range for significant wave height, with correlation coefficients of 0.95–0.96 on day 1 and between 0.86 and 0.88 within week 1, along with bias close to zero. After day 10, correlation coefficients fall below 0.70. The degradation of predictability and the increase in scatter errors predominantly occur in the forecast lead time between days 4 and 10, in terms of the ensemble mean and individual members, including the control. For week 2 and beyond, a probabilistic spatiotemporal analysis of the ensemble space provides useful forecast guidance. Our results provide a framework for expanding the usefulness of wave ensemble data in operational forecasting applications.