Hard Potato: A Python Library to Control Commercial Potentiostats and to Automate Electrochemical Experiments

Abstract

Here, we develop and show the use of an opensource Python library to control commercial potentiostats. It standardizes the commands for different potentiostat models, opening the possibility to perform automated experiments independently of the instrument used. At the time of this writing, we have included potentiostats from CH Instruments (models 1205B, 1242B, 601E, and 760E) and PalmSens (model Emstat Pico), although the open-source nature of the library allows for more to be included in the future. To showcase the general workflow and implementation of a real experiment, we have automated the Randles−S ̌evcǐ ́k methodology to determine the diffusion coefficient of a redox-active species in solution using cyclic voltammetry. This was accomplished by writing a Python script that includes data acquisition, data analysis, and simulation. The total run time was 1 min and 40 s, well below the time it would take even an experienced electrochemist to apply the methodology in a traditional manner. Our library has potential applications that expand beyond the automation of simple repetitive tasks; for example, it can interface with peripheral hardware and well-established third-party Python libraries as part of a more complex and intelligent setup that relies on laboratory automation, advanced optimization, and machine learning.

Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation

“…We constructed an autonomous electrochemical platform consisting of five key modules (Supplementary Notes 1 and 2 ): (1) flow chemistry that enables automated electrolyte formulation and disposal (Fig. 1b, c ), (2) automated electrochemical testing, including automatic iR compensation during CV measurements, via a modified Hard Potato 40 Python library controlling a commercial potentiostat, (3) DL-based automated CV analysis that yields numerical propensity distributions of probable mechanisms that can be readily evaluated (Fig. 1e ) 34 , (4) adaptive exploration of a large parameter space using a Dragonfly 41 , 42 Bayesian optimization package that suggests new experimental conditions toward a user-defined objective in a closed-loop manner (Fig.…”

Key Requirements for Advancing Machine Learning Approaches in Single Entity Electrochemistry

“…[42,43] Advancements in this field could be propelled by either constructing highly autonomous robotic systems or automating specific critical points in current workflows. [42][43][44][45] In the first scenario, the literature demonstrates a marked trend toward completely eliminating human involvement by constructing an autonomous robotic system. [45,46] These systems are designed to search for and analyze literature from online resources independently, plan experiments, execute them using a robotic setup, and process and analyze data using ML.…”

Python for Electrochemistry: A Free and All-In-One Toolset

“…Hardware control and automatization.-A few examples include the "hardpotato" package (Python API to control programmable potentiostats, Fig. 3A) 8 and the "LABS" package (Laboratory Automation and Batch Scheduling). 9 The former is developed to standardize commands across different potentiostat models, enabling automated experiments on any instrument, from data acquisition to analysis and simulation.…”