Dual-channel laser confocal microscopy lignocellulose enzymatic digestion dataset

This dataset contains original 3D volumetric images (exported as 2×512×512×64 .npz files) capturing the microstructural evolution of Caragana korshinskii biomass under various enzymatic treatments.
Key features include:
Substrate: Ball-milled C. korshinskii (1–100 µm particles).
Treatments: Cellulase (CEL), Lignin Peroxidase (LIP), Laccase (LAC), and their synergistic combinations (LL, LLC).
Imaging Technology: Dual-channel Confocal Laser Scanning Microscopy (CLSM) using 405 nm and 552 nm lasers to distinguish lignin autofluorescence (435–500 nm) and cellulose-Congo red fluorescence (570–620 nm).
Standardization: All samples were imaged within a custom-fabricated microfluidic chip to ensure geometric confinement and eliminate deformation artifacts.
Data Format: Each volume is a 4-dimensional NumPy array representing (Channels, Height, Width, Depth).
This dataset provides a spatially resolved, non-destructive foundation for training 3D deep learning models to identify degradation signatures in biorefinery processes.