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Plasma-Assisted Surface Engineering of Binary Metal Chalcogenides: A Path Toward High Energy Efficiency, Electrocatalysts for Water Splitting, and Urea Oxidation with Stability Prediction via Machine Learning

  • Swapnil R. Patil
  • , Rakesh Kulkarni
  • , Sourabh B. Ghode
  • , Jungmin Kim
  • , Qazi Muhammad Saqib
  • , Muhammad Noman
  • , Chandrashekhar S. Patil
  • , Youngbin Ko
  • , Seo Yeong Bae
  • , Yoon Young Chang
  • , Janardhan Reddy Koduru
  • , Kolleboyina Jayaramulu
  • , Nilesh R. Chodankar
  • , Jinho Bae
    • Jeju National University
    • IIT Jammu
    • Kwangwoon University
    • University of Wisconsin-Madison

    Research output: Contribution to journalArticlepeer-review

    15 Scopus citations

    Abstract

    This study introduces an advanced Cu2MnS2 ctenophore-like nanostructured electrocatalyst, synthesized through a hydrothermal process and enhanced via argon (Ar) plasma activation (Cu2MnS2-Ar) to improve its performance in overall water splitting (OWS) and urea oxidation reactions (UORs). Plasma activation generates reactive species that modify the material’s surface, increasing its conductivity, electroactive sites, and surface energy, all contributing to enhanced catalytic activity. The Cu2MnS2-Ar catalyst exhibits impressive performance in hydrogen evolution (HER) and oxygen evolution (OER) reactions, with overpotentials of 0.012 and 0.026 V at 10 and 300 mA/cm2, respectively, much lower than the untreated Cu2MnS2 catalyst, which shows 0.308 and 0.309 V. More importantly, the developed cell with the Cu2MnS2-Ar electrocatalyst demonstrates an exceptional overpotential of 1.47 and 1.37 V at 50 mA/cm2 for the OWS and UOR and, notably, which is much smaller than the noble metal-based catalyst. Conversely, our developed cell exhibits outstanding performance by achieving cell voltages of 1.59 V even under demanding industrial conditions (60 °C). The stability of the Cu2MnS2-Ar catalyst was further evaluated using time series analysis (TSA) and long short-term memory (LSTM) modeling, which accurately predicts the electrocatalytic behavior, confirming the effectiveness of the modeling technique in understanding the catalyst’s performance.

    Original languageBritish English
    Pages (from-to)2346-2359
    Number of pages14
    JournalACS Applied Energy Materials
    Volume8
    Issue number4
    DOIs
    StatePublished - 24 Feb 2025

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 7 - Affordable and Clean Energy
      SDG 7 Affordable and Clean Energy
    2. SDG 9 - Industry, Innovation, and Infrastructure
      SDG 9 Industry, Innovation, and Infrastructure

    Keywords

    • binary metal chalcogenide
    • machine learning
    • overall water splitting
    • surface engineering
    • urea oxidation

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