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Organic mixed ionic–electronic conductors (OMIECs) are a unique class of soft, conjugated polymeric materials. The simultaneous electronic and ionic transport of OMIECs enables a new type of device, namely, organic electrochemical transistors, among other emerging technologies. However, the dynamic nature—where charge transport, doping kinetics, and morphological changes occur concurrently—poses significant challenges in the characterization and understanding of OMIECs. Recent advances in in situ optical techniques, including ultraviolet–visible–near-infrared spectroscopy, Raman spectroscopy, and microscopy imaging, have provided valuable insights into the charge transport mechanisms and ionic doping dynamics spanning from the microscopic to the device scale. In this perspective, based on several archetypal OMIECs, we survey how spectroscopic signatures were used to reveal key physical processes in these materials. Looking forward, we propose that ultrafast spectroscopy and microscopy techniques—such as transient absorption spectroscopy, terahertz time-domain spectroscopy, pump–probe microscopy, and photothermal microscopy—hold great potential for uncovering more fundamental mechanisms of OMIEC operation, including quasiparticle dynamics, intrinsic electrical conductivity, and carrier mobility, which remain under-explored. Integrating optical characterization with electrochemical measurements will enable in operando studies on state-of-the-art devices, with results further refined by parallel advancements in theoretical modeling. Altogether, we envision in operando optical characterization with spatial, spectral, and temporal resolution across multiple scales as a powerful pathway to advance the understanding of OMIEC mechanisms and their structure–property relationships.
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Mind the gap: Bridging the divide between AI aspirations and the reality of autonomous microscopy
What does materials science look like in the “Age of Artificial Intelligence?” Each material’s domain—synthesis, characterization, and modeling—has a different answer to this question, motivated by unique challenges and constraints. This work focuses on the tremendous potential of autonomous characterization within electron microscopy. We present our recent advancements in developing domain-aware, multimodal models for microscopy analysis capable of describing complex atomic systems. We then address the critical gap between the theoretical promise of autonomous microscopy and its current practical limitations, showcasing recent successes while highlighting the necessary developments to achieve robust, real-world autonomy.
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- Award ID(s):
- 2134607
- PAR ID:
- 10599564
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- APL Machine Learning
- Volume:
- 3
- Issue:
- 2
- ISSN:
- 2770-9019
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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