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<p>Emerging sensing technologies require a precise and robust process of material and device optimization for reaching the desirable performance. We report a rapid and non-destructive strategy to classify polyaniline/vanadium pentoxide (PANI/V<sub>2</sub>O<sub>5</sub>) composite thin films using visible reflectance spectroscopy sensor data combined with Principal Component Analysis (PCA). PANI/V<sub>2</sub>O<sub>5</sub> thin films were galvanostatically electrodeposited from deposition solutions with V<sub>2</sub>O<sub>5</sub> molarity from 1 to 4% of PANI on FTO substrates and characterized by chronopotentiometry, scanning electron microscopy and reflectance spectrophotometry (400 to 700&#xa0;nm) including CIE <i>L*a*b*</i> color scale parameters. No characterization distinction was observed between samples with varying V<sub>2</sub>O<sub>5</sub> content. The samples were tested as sensing stage in potentiometric extended-gate field-effect transistor (EGFET) and optical pH sensors. EGFET measurements showed a sensitivity (approximately 71 mV/pH) invariable across samples while the optical response was suppressed due to the oxidized state of all the films, regardless of the V<sub>2</sub>O<sub>5</sub> concentration. On the other hand, PCA biplots, score and loading plots, from reflectance data consistently produced distinct, non-overlapping, reproducible clusters that separated the samples according to oxide content for all sets of variables analyzed. PC1 accounted for up to 99.5% of spectral variance, in one subset, driven mainly by <i>L*</i> and integrated reflectance. The methodology was able to highlight subtle differences in the composition of the materials without sample processing and powerful analytical characterization techniques. It is a simple and cost-effective fingerprint tool for quality control and optimization of material and devices in the development of sensor platforms.</p>

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Fingerprinting PANI/V₂O₅ composite thin films: PCA-based classification beyond conventional sensor performance

  • 期刊论文

  • 发表日期：

    2025年10月14日

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  • Hugo José Nogueira Pedroza Dias Mello,
  • Ricardo Estéfani França Rocha,
  • Marcelo Mulato

摘要

Emerging sensing technologies require a precise and robust process of material and device optimization for reaching the desirable performance. We report a rapid and non-destructive strategy to classify polyaniline/vanadium pentoxide (PANI/V2O5) composite thin films using visible reflectance spectroscopy sensor data combined with Principal Component Analysis (PCA). PANI/V2O5 thin films were galvanostatically electrodeposited from deposition solutions with V2O5 molarity from 1 to 4% of PANI on FTO substrates and characterized by chronopotentiometry, scanning electron microscopy and reflectance spectrophotometry (400 to 700 nm) including CIE L*a*b* color scale parameters. No characterization distinction was observed between samples with varying V2O5 content. The samples were tested as sensing stage in potentiometric extended-gate field-effect transistor (EGFET) and optical pH sensors. EGFET measurements showed a sensitivity (approximately 71 mV/pH) invariable across samples while the optical response was suppressed due to the oxidized state of all the films, regardless of the V2O5 concentration. On the other hand, PCA biplots, score and loading plots, from reflectance data consistently produced distinct, non-overlapping, reproducible clusters that separated the samples according to oxide content for all sets of variables analyzed. PC1 accounted for up to 99.5% of spectral variance, in one subset, driven mainly by L* and integrated reflectance. The methodology was able to highlight subtle differences in the composition of the materials without sample processing and powerful analytical characterization techniques. It is a simple and cost-effective fingerprint tool for quality control and optimization of material and devices in the development of sensor platforms.