ALBATROSS: A robotised system for high-throughput electrolyte screening via automated electrolyte formulation, coin-cell fabrication, and electrochemical evaluation

• URL: http://arxiv.org/abs/2512.13198v1
• Date: Mon, 15 Dec 2025 11:13:30 GMT
• Title: ALBATROSS: A robotised system for high-throughput electrolyte screening via automated electrolyte formulation, coin-cell fabrication, and electrochemical evaluation
• Abstract summary: ALBATROSS is an automated system capable of electrolyte formulation, coin-cell assembly, and electrochemical evaluation.<n>It enables fully automated assembly and testing of up to 48 cells without researcher intervention.
• Score: 8.37678754163832
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As battery technologies advance toward higher stability and energy density, the need for extensive cell-level testing across various component configurations becomes critical. To evaluate performance and understand the operating principles of batteries in laboratory scale, fabrication and evaluation of coin cells are essential processes. However, the conventional coin-cell assembly and testing processes require significant time and labor from researchers, posing challenges to high-throughput screening research. In this study, we introduce an Automated Li-ion BAttery Testing RObot SyStem (ALBATROSS), an automated system capable of electrolyte formulation, coin-cell assembly, and electrochemical evaluation. The system, integrated within a argon-filled glovebox, enables fully automated assembly and testing of up to 48 cells without researcher intervention. By incorporating custom-designed robot gripper and 3D-printed structures optimized for precise cell handling, ALBATROSS achieved high assembly reliability, yielding a relative standard deviation (RSD) of less than 1.2% in discharge capacity and a standard deviation of less than 3 Ω in EIS measurements for NCM811||Li half cells. Owing to its high reliability and automation capability, ALBATROSS allows for the acquisition of high-quality coin-cell datasets, which are expected to accelerate the development of next-generation electrolytes.

Related papers

• AI-Driven Surrogate Models for Predicting Electrode-Scale Discharge Behavior in Lithium-Ion Batteries [47.27974914502511]
  We introduce a deep learning surrogate pipeline based on the Swin3D Transformer to predict discharge dynamics directly from data.<n>The proposed method reduces the computational overhead by orders of magnitude, providing a scalable and efficient framework for high- throughput battery design and optimization.
  arXiv  Detail & Related papers  (2026-07-22T09:10:48Z)
• Bridging battery design and health assessment through virtual sensing and physics-informed learning [2.8974566456589095]
  Supercharging of lithium-ion batteries (LiBs) requires robust health monitoring to ensure durability, safety, and user confidence.<n>Here we propose a physics-informed learning framework with virtual sensing that infers hard-to-measure design parameters.
  arXiv  Detail & Related papers  (2026-07-18T15:59:29Z)
• Closed-Loop LLM Co-Pilots for Digital Agriculture [0.0]
  This study evaluates the application of Large Language Models (LLMs) in complex biological systems.<n>The framework is driven by data from a 49-channel phytosensor network.<n>The system provides real-time natural-language interpretation for both specialists and non-experts.
  arXiv  Detail & Related papers  (2026-07-09T11:05:42Z)
• A Robotic Testing Platform for Pipelined Discovery of Resilient Soft Actuators [63.07587517846448]
  We propose an optimization pipeline enabled by a novel testing robot capable of scanning DEA lifetime.<n>The robot integrates electro-mechanical property measurement, programmable voltage input, and multi-channel testing capacity.<n>Using it, we scanned the lifetime of Elastosil-based linear actuators across parameters including input voltage magnitude, frequency, electrode material concentration, and electrical connection filler.
  arXiv  Detail & Related papers  (2026-02-24T14:41:28Z)
• A Lightweight Transfer Learning-Based State-of-Health Monitoring with Application to Lithium-ion Batteries in Autonomous Air Vehicles [53.158733310637295]
  Transfer learning is a promising technique for leveraging knowledge from data-rich source working conditions.<n>Traditional TL-based state-of-health (SOH) monitoring is infeasible when applied in portable mobile devices.<n>This paper proposes a lightweight TL-based SOH monitoring approach with constructive transfer incremental learning (CITL)
  arXiv  Detail & Related papers  (2025-12-09T11:54:09Z)
• AI-Driven Discovery of High Performance Polymer Electrodes for Next-Generation Batteries [0.0]
  The use of transition group metals in electric batteries requires extensive usage of critical elements like lithium, cobalt and nickel, which poses significant environmental challenges.<n> replacing these metals with redox-active organic materials offers a promising alternative, thereby reducing the carbon footprint of batteries by one order of magnitude.<n>A machine learning driven battery informatics framework is developed and implemented to overcome the limitations for lower voltage and specific capacity.
  arXiv  Detail & Related papers  (2025-02-19T17:32:17Z)
• Adapting Amidst Degradation: Cross Domain Li-ion Battery Health Estimation via Physics-Guided Test-Time Training [19.606703130917325]
  Health modeling of lithium-ion batteries (LIBs) is crucial for safe and efficient energy management and carries significant socio-economic implications. We introduce a practical Test-Time Training framework, BatteryTTT, which adapts the model continually using each target data (UTD) collected amidst degradation. To fully utilize each UTD, BatteryTTT integrates the inherent physical laws of modern LIBs into self-supervised learning, termed Physcics-Guided Test-Time Training.
  arXiv  Detail & Related papers  (2024-01-30T14:47:15Z)
• Toward High-Performance Energy and Power Battery Cells with Machine Learning-based Optimization of Electrode Manufacturing [61.27691515336054]
  In this study, we tackle the issue of high-performance electrodes for desired battery application conditions. We propose a powerful data-driven approach supported by a deterministic machine learning (ML)-assisted pipeline for bi-objective optimization of the electrochemical performance. Our results suggested a high amount of active material, combined with intermediate values of solid content in the slurry and calendering degree, to achieve the optimal electrodes.
  arXiv  Detail & Related papers  (2023-07-07T13:48:50Z)
• CellDefectNet: A Machine-designed Attention Condenser Network for Electroluminescence-based Photovoltaic Cell Defect Inspection [67.99623869339919]
  A big challenge faced by industry in photovoltaic cell visual inspection is the fact that it is currently done manually by human inspectors. In this work, we introduce CellDefectNet, a highly efficient attention condenser network designed via machine-driven design exploration. We demonstrate the efficacy of CellDefectNet on a benchmark dataset comprising of a diversity of photovoltaic cells captured using electroluminescence imagery.
  arXiv  Detail & Related papers  (2022-04-25T16:35:19Z)
• Machine learning pipeline for battery state of health estimation [3.0238880199349834]
  We design and evaluate a machine learning pipeline for estimation of battery capacity fade. The pipeline estimates battery SOH with an associated confidence interval by using two parametric and two non-parametric algorithms. When deployed on cells operated under the fast-charging protocol, the best model achieves a root mean squared percent error of 0.45%.
  arXiv  Detail & Related papers  (2021-02-01T13:50:56Z)
• Automatic LiDAR Extrinsic Calibration System using Photodetector and Planar Board for Large-scale Applications [110.32028864986918]
  This study proposes a new concept of a target board with embedded photodetector arrays, named the PD-target system, to find the precise position of the correspondence laser beams on the target surface. The experimental evaluation of the proposed system on low-resolution LiDAR showed that the LiDAR offset pose can be estimated within 0.1 degree and 3 mm levels of precision.
  arXiv  Detail & Related papers  (2020-08-24T16:28:40Z)
• Universal Battery Performance and Degradation Model for Electric Aircraft [52.77024349608834]
  Design, analysis, and operation of electric vertical takeoff and landing aircraft (eVTOLs) requires fast and accurate prediction of Li-ion battery performance. We generate a battery performance and thermal behavior dataset specific to eVTOL duty cycles. We use this dataset to develop a battery performance and degradation model (Cellfit) which employs physics-informed machine learning.
  arXiv  Detail & Related papers  (2020-07-06T16:10:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.