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Research . 2026
License: CC BY
Data sources: Datacite
ZENODO
Research . 2026
License: CC BY
Data sources: Datacite
ZENODO
Research . 2026
License: CC BY
Data sources: Datacite
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Theoretical Framework for Sensorless pH Monitoring in Hydrometallurgical Systems: A Voltammetric Overpotential Approach for Edge Computing

Authors: Vivas Zamora, Daniel Isaias; Sanchez Diaz, Jose Alfredo; Brito Guerrero, Yndira Patricia;

Theoretical Framework for Sensorless pH Monitoring in Hydrometallurgical Systems: A Voltammetric Overpotential Approach for Edge Computing

Abstract

This paper introduces the “Hydro-Logics” protocol, a formal theoretical framework for indirect pH estimation in complex electrochemical systems. Traditional potentiometric sensors face severe degradation in both industrial and clinical environments due to chemical poisoning, mechanical abrasion, and biofouling. This framework proposes a “sensorless” alternative by analyzing the Hydrogen Evolution Reaction (HER) through linear sweep voltammetry. By utilizing an ESP32-S3 platform for Edge Computing, the protocol identifies the onset potential (Vonset) and applies a Nernstian model corrected for cathodic overpotential (η) and activity coefficients. This allows for real-time monitoring of critical variables such as gastric acidity and metal electrodeposition (including Rare Earth Elements), providing a resilient baseline for global technological applications. Key Innovations: Virtual Sensing for Harsh Environments: Enables resilient monitoring in dense mineral slurries (Rare Earth Elements) and biological fluids where traditional sensors fail. Edge Intelligence: Implementation of the HydroLogics_V1 algorithm for real-time derivative analysis (dI/dV) at the hardware level. Cross-Disciplinary Application: Validated frameworks for both Advanced Metallurgy (Hydrometallurgy Mode) and Clinical Diagnostics (gastric acidity).

Este artículo presenta el protocolo “Hydro-Logics”, un marco teórico formal para la estimación indirecta del pH en sistemas electroquímicos complejos. Los sensores potenciométricos tradicionales enfrentan degradación severa tanto en entornos industriales como clínicos debido al envenenamiento químico, la abrasión mecánica y el bioensuciamiento. Este marco propone una alternativa “sin sensores” mediante el análisis de la Reacción de Evolución de Hidrógeno (HER) a través de voltamperometría de barrido lineal. Utilizando una plataforma ESP32-S3 para computación en el borde, el protocolo identifica el potencial de inicio (Vonset) y aplica un modelo de Nernst corregido por el sobrevoltaje catódico (η) y coeficientes de actividad. Esto permite el monitoreo en tiempo real de variables críticas como la acidez gástrica y la electrodeposición de metales (incluyendo elementos de tierras raras), proporcionando una base resiliente para aplicaciones tecnológicas globales

Related Organizations
Keywords

Hydrogen Evolution Reaction (HER), Cyber-Physical Systems, ESP32-S3, Rare Earth Elements (REE), Linear sweep voltammetry, Cathodic overpotential, Sweat electrolytes, Virtual sensing, Sensorless electrochemical sensing, Hydrometallurgy, Edge Computing, Indirect pH estimation, Hydro-Logics protocol, Nernstian model

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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