OPTICAL DIAGNOSTICS OF ATMOSPHERIC-PRESSURE PLASMA JETS: FROM EMISSION SPECTROSCOPY TO THREE-DIMENSIONAL IMAGING AND DATA ANALYSIS

Atmospheric-pressure plasma jets (APPJs) are strongly non-equilibrium plasma sources whose optical emission, reactive chemistry, gas dynamics, and propagation behavior can vary over short spatial and temporal scales. These features make optical diagnostics indispensable, but they also expose the limitations of conventional line-of-sight and time-averaged measurements. This review critically examines the progression of APPJ optical diagnostics from conventional optical emission spectroscopy (OES) to spatially and temporally resolved spectroscopy, fast and intensified imaging, schlieren visualization, hyperspectral imaging, tomographic reconstruction, and data-driven analysis. Particular emphasis is placed on the trade-offs among spectral, spatial, and temporal resolution and on the growing role of digital image processing and machine learning. Recent studies have shown that tomographic OES can recover three-dimensional emission distributions without imposing cylindrical symmetry, hyperspectral imaging can map wavelength-dependent emission across two spatial dimensions, and machine-learning approaches can extract process-related information from large spectral datasets. Persistent challenges include line-of-sight integration, limited standardization of preprocessing and reconstruction pipelines, calibration and synchronization burdens, reconstruction artifacts, and the uncertain transferability of machine-learning models across plasma sources. The review identifies the integration of spectroscopy, high-speed imaging, volumetric reconstruction, and physics-informed analysis as a promising route toward more interpretable APPJ characterization. Achieving reliable real-time operation will require further validation, calibration, and uncertainty assessment.