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Small· 2026Q1

A Dual‐Band Regulatory Capacity of GQDs‐ p TSA/PANI Composites: Electrochromism, Infrared Modulation and Machine Learning Integration

Longlong Qin, Liang Liu, Shengxiang Huang, Xiaohui Gao et al.

Short summary

Graphene quantum dots (GQDs) and p-toluenesulfonic acid (pTSA) co-doped polyaniline (PANI) films achieve dual-band modulation with 47.6% optical contrast and 203.2 cm²/C coloration efficiency, alongside a 0.71 emissivity modulation range and ±8°C temperature tunability.

AI-generated from the title and abstract; the full text is not read.

Key points

  • Co-doping PANI with GQDs and pTSA creates a porous network enhancing electrochemical kinetics.
  • Optimized films show 47.6% optical contrast at 667 nm and 203.2 cm²/C coloration efficiency.
  • Composites achieve a 0.71 emissivity modulation range and ±8°C temperature tunability.
  • An interpretable machine-learning framework was developed for multi-property prediction and composition screening.

AI-generated from the title and abstract; the full text is not read.

Abstract

ABSTRACT Construction of acid‐doped polyaniline films with excellent dual‐band modulation ability remains extremely challenging. Herein, graphene quantum dots (GQDs) and p‐toluenesulfonic acid ( p TSA) are co‐doped in electrodeposited polyaniline (PANI) films, transforming the compact PANI layer into an interconnected porous network. The reconstructed morphology facilitates electrolyte accessibility and shortens ion‐transport pathways, while the interfacial interactions among GQDs, p TSA, and PANI promote localized charge redistribution and charge transport. These synergistic morphological and electronic effects contribute to accelerated electrochemical kinetics and enhanced electrochromic performance. The optimized film exhibits a maximum optical contrast (ΔT) of 47.6% at 667 nm and a high coloration efficiency of 203.2 cm 2 C −1 in the potential range from −0.2 to +0.3 V. Meanwhile, a broad infrared emissivity modulation range (Δε) and a maximum temperature tunable range was obtained as 0.71°C and ±8°C, respectively. Furthermore, an interpretable machine‐learning framework integrating composition, morphology, and transport descriptors was established for multi‐property prediction and composition screening, with its predictive capability further evaluated by independent blind validation. The results reported in this work provide an effective strategy and concept for advanced electrochromic materials toward applications in smart windows and adaptive thermal management systems.

The authors' abstract, as published at the source. Small, 2026 · DOI ↗

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Field: Polymers and Plastics

Polymers and PlasticsMaterials Science