Long-wave infrared (LWIR) spectral imaging is critically important for next-generation machine perception systems, as most thermal emissions from room-temperature objects occur in the LWIR band. The atmospheric transparency in this spectral region further enables long-range thermal signal propagation, making it ideally suited for imaging in complete darkness. In addition to machine vision, LWIR spectral imaging is also a powerful tool for gas sensing in environmental monitoring and remediation. Many greenhouse gases and industrial pollutants have distinct absorption fingerprints in the LWIR region, enabling precise, non-contact detection of chemical species in the atmosphere. This opens new possibilities for real-time, remote sensing of leaks, emissions, and air quality. 

However, current LWIR imaging technologies face major limitations that impede their integration with wide-range applications. Conventional approaches to spectral decomposition—such as filter wheels, mosaic filters and interferometers—are bulky, fragile, and constrained by limited resolution or narrow fields of view. These challenges result in low-contrast, blurry thermal images that restrict the performance of learning-based perception tasks such as semantic segmentation, ranging and gas identification. To overcome existing limitations, electrochemically controlled polymers offer a promising platform for electrically tunable devices in the LWIR range. Their ability to dynamically modulate optical properties with applied voltage enables compact, reconfigurable, and spectrally agile devices that could transform both intelligent thermal imaging and environmental sensing technologies.