Chronoamperometry with Room-Temperature Ionic Liquids: Sub-Second Inference Techniques

• URL: http://arxiv.org/abs/2506.04540v1
• Date: Thu, 05 Jun 2025 01:21:24 GMT
• Title: Chronoamperometry with Room-Temperature Ionic Liquids: Sub-Second Inference Techniques
• Authors: Kordel K. France,
• Abstract summary: This paper presents a novel mathematical regression approach that reduces chronoamperometric windows to under 1 second.<n>By applying an inference algorithm to the initial transient current response, this method accurately predicts steady-state electrochemical parameters.<n>The implications of this technique are explored in analytical chemistry, sensor technology, and battery science.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chronoamperometry (CA) is a fundamental electrochemical technique used for quantifying redox-active species. However, in room-temperature ionic liquids (RTILs), the high viscosity and slow mass transport often lead to extended measurement durations. This paper presents a novel mathematical regression approach that reduces CA measurement windows to under 1 second, significantly faster than previously reported methods, which typically require 1-4 seconds or longer. By applying an inference algorithm to the initial transient current response, this method accurately predicts steady-state electrochemical parameters without requiring additional hardware modifications. The approach is validated through comparison with standard chronoamperometric techniques and is demonstrated to maintain reasonable accuracy while dramatically reducing data acquisition time. The implications of this technique are explored in analytical chemistry, sensor technology, and battery science, where rapid electrochemical quantification is critical. Our technique is focused on enabling faster multiplexing of chronoamperometric measurements for rapid olfactory and electrochemical analysis.

Related papers

• Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems [52.19558333652367]
  We present finite-range embeddings (FiRE) for accurate large-scale ab-initio electronic structure calculations.<n>FiRE reduces the complexity of neural-network variational Monte Carlo (NN-VMC) by $sim ntextel$, the number of electrons.<n>We validate our method's accuracy on various challenging systems, including biochemical compounds and organometallic compounds.
  arXiv  Detail & Related papers  (2025-04-08T14:28:54Z)
• OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytes [44.16223940507546]
  Solid-state electrolyte batteries are expected to replace liquid electrolyte lithium-ion batteries in the near future.<n>Finding highly ion-conductive materials is time-consuming and resource-intensive.<n>OBELiX is a database of synthesized solid electrolyte materials and experimentally measured room temperature ionic conductivities.
  arXiv  Detail & Related papers  (2025-02-20T03:59:35Z)
• Predicting ionic conductivity in solids from the machine-learned potential energy landscape [68.25662704255433]
  We propose an approach for the quick and reliable screening of ionic conductors through the analysis of a universal interatomic potential.<n>Eight out of the ten highest-ranked materials are confirmed to be superionic at room temperature in first-principles calculations.<n>Our method achieves a speed-up factor of approximately 50 compared to molecular dynamics driven by a machine-learning potential, and is at least 3,000 times faster compared to first-principles molecular dynamics.
  arXiv  Detail & Related papers  (2024-11-11T09:01:36Z)
• Thermometry of Trapped Ions Based on Bichromatic Driving [10.452541695685712]
  A thermometry method based on bichromatic driving was theoretically proposed by Ivan Vybornyi et al. We provide a detailed statistical analysis of this method and prove its robustness to several imperfect experimental conditions. Our theoretical analysis and experimental verification demonstrate that the scheme can accurately and efficiently measure the temperature in ion crystals.
  arXiv  Detail & Related papers  (2024-07-21T14:46:57Z)
• Tree-Based Learning on Amperometric Time Series Data Demonstrates High Accuracy for Classification [0.0]
  We present a universal method for the classification with respect to diverse amperometric datasets using data-driven approaches in computational science. We demonstrate a very high prediction accuracy (greater than or equal to 95%) This is one of the first studies that propose a scheme for machine learning, and in particular, supervised learning on full amperometry time series data.
  arXiv  Detail & Related papers  (2023-02-06T09:44:53Z)
• PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design [102.9593507372373]
  Catalyst materials play a crucial role in the electrochemical reactions involved in industrial processes. Machine learning holds the potential to efficiently model materials properties from large amounts of data. We propose task-specific innovations applicable to most architectures, enhancing both computational efficiency and accuracy.
  arXiv  Detail & Related papers  (2022-11-22T05:24:30Z)
• Probing quantum devices with radio-frequency reflectometry [68.48453061559003]
  Radio-frequency reflectometry can measure changes in impedance even when their duration is extremely short, down to a microsecond or less. Examples of reflectometry experiments include projective measurements of qubits and Majorana devices for quantum computing. This book aims to introduce the readers to the technique, to review the advances to date and to motivate new experiments in fast quantum device dynamics.
  arXiv  Detail & Related papers  (2022-02-21T20:14:21Z)
• A Priori Calibration of Transient Kinetics Data via Machine Learning [0.8208704543835964]
  The temporal analysis of products reactor provides a vast amount of transient kinetic information. Herein we use machine learning techniques combined with physical constraints to convert the raw instrument signal to chemical information.
  arXiv  Detail & Related papers  (2021-09-27T20:15:28Z)
• Benchmarking Quantum Chemistry Computations with Variational, Imaginary Time Evolution, and Krylov Space Solver Algorithms [0.2107969466194361]
  State of the art methods to reach chemical accuracy in hybrid quantum-classical electronic structure calculations of alkali hydride molecules on IBM quantum computers. Results show that electronic structure calculations have advanced rapidly to routine chemical accuracy for simple molecules, from their inception on quantum computers a few short years ago.
  arXiv  Detail & Related papers  (2021-02-10T15:54:55Z)
• Benchmarking adaptive variational quantum eigensolvers [63.277656713454284]
  We benchmark the accuracy of VQE and ADAPT-VQE to calculate the electronic ground states and potential energy curves. We find both methods provide good estimates of the energy and ground state. gradient-based optimization is more economical and delivers superior performance than analogous simulations carried out with gradient-frees.
  arXiv  Detail & Related papers  (2020-11-02T19:52:04Z)
• Attosecond Dynamics in Liquids [0.0]
  We review the first steps in developing attosecond time-resolved measurements in liquids. In the first part, we discuss attosecond photoelectron spectroscopy with cylindrical microjets. In the second part, we introduce HHS of liquids based on flat microjets.
  arXiv  Detail & Related papers  (2020-09-06T18:40:28Z)
• Ultrafast viscosity measurement with ballistic optical tweezers [55.41644538483948]
  Noninvasive viscosity measurements require integration times of seconds. We demonstrate a four orders-of-magnitude improvement in speed, down to twenty microseconds. We achieve this using the instantaneous velocity of a trapped particle in an optical tweezer.
  arXiv  Detail & Related papers  (2020-06-29T00:09:40Z)

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.