Electrospinning is a versatile technique for producing continuous nanofibers with diameters ranging from tens of nanometers to several micrometers. The process involves applying a high voltage to a polymer solution or melt, forming a charged jet that elongates and solidifies into fibers collected on a grounded substrate. Monitoring the electrospinning process in real time is critical for ensuring consistent fiber morphology, diameter distribution, and defect-free production. Inline techniques such as imaging and spectroscopy provide valuable insights into jet dynamics, fiber formation, and potential defects.

High-speed imaging is a widely used method for observing the electrospinning jet in real time. Cameras with frame rates exceeding thousands of frames per second capture the jet's behavior, including the formation of the Taylor cone, jet initiation, and whipping instability. The whipping instability, a key factor in fiber thinning, can be analyzed to optimize process parameters such as voltage, flow rate, and solution conductivity. Irregularities in jet motion, such as branching or bead formation, can be detected early, allowing for immediate parameter adjustments. Studies have shown that stable jet formation typically occurs at electric field strengths between 0.5 and 3 kV/cm, depending on polymer solution properties.

Spectroscopic techniques complement imaging by providing chemical and structural information during fiber formation. Raman spectroscopy can monitor polymer chain alignment and crystallization in real time, particularly for semi-crystalline polymers like polycaprolactone or polyethylene oxide. Shifts in Raman peaks indicate changes in molecular orientation as the jet transitions from solution to solid fiber. Similarly, infrared spectroscopy can detect solvent evaporation rates and polymer-solvent interactions, ensuring complete drying before fiber collection. For example, residual solvent content below 5% is often targeted to prevent fiber fusion or poor mechanical integrity.

Defect detection during electrospinning primarily focuses on bead formation and fiber inhomogeneity. Beads, or spindle-like structures along the fiber, arise from insufficient polymer chain entanglement or improper solvent evaporation. Inline light scattering techniques can identify bead formation by detecting variations in scattered light intensity caused by irregular fiber diameters. Dynamic light scattering (DLS) systems adapted for electrospinning setups have demonstrated sensitivity to diameter variations as small as 20 nm, enabling early detection of bead defects.

Fiber inhomogeneity, including diameter fluctuations and branching, can be monitored using laser diffraction or optical coherence tomography (OCT). Laser diffraction patterns change with fiber diameter, allowing real-time assessment of uniformity. OCT provides cross-sectional images of the fiber deposition process, revealing inconsistencies in fiber packing density or alignment. For aligned nanofibers, deviations in orientation greater than 5 degrees from the target direction can be detected and corrected by modifying collector speed or electric field distribution.

Electrical monitoring techniques also play a role in defect detection. The current carried by the electrospinning jet can be measured using a sensitive ammeter connected to the collector. Sudden current fluctuations often indicate jet instability or the onset of bead formation. Research has shown that stable electrospinning typically produces current values in the range of 100–500 nA, with deviations beyond this range signaling potential defects.

Environmental factors such as humidity and temperature significantly influence fiber quality and must be monitored inline. Capacitive humidity sensors positioned near the spinning zone provide real-time feedback, as humidity levels above 60% can lead to incomplete solvent evaporation. Temperature sensors track heat transfer from the polymer solution to the surrounding environment, ensuring optimal conditions for fiber solidification. Some systems incorporate feedback loops that adjust voltage or flow rate in response to environmental changes, maintaining consistent fiber production.

Automated vision systems combined with machine learning algorithms have emerged as powerful tools for defect detection. These systems analyze high-speed camera footage to classify defects such as beads, splattering, or non-uniform deposition. Training datasets consisting of thousands of images enable the identification of defect patterns with over 90% accuracy in some implementations. Real-time processing allows for immediate process adjustments, reducing material waste and improving yield.

The integration of multiple monitoring techniques provides a comprehensive view of the electrospinning process. For example, combining high-speed imaging with Raman spectroscopy enables correlation of jet dynamics with molecular orientation changes. Similarly, simultaneous electrical and optical monitoring can distinguish between different types of defects based on their unique signatures. Such multimodal approaches enhance process control and reproducibility, particularly for complex systems like coaxial or multi-jet electrospinning.

Process parameters influencing defect formation include solution viscosity, conductivity, and surface tension. Inline viscometers and conductivity meters ensure these properties remain within optimal ranges throughout production. For most polymer solutions, viscosities between 200–2000 cP and conductivities between 0.1–5 mS/cm are typically maintained to prevent defects. Surface tension measurements using pendant drop analysis can detect contamination or solvent evaporation effects that may lead to bead formation.

Emerging techniques such as X-ray phase-contrast imaging offer new possibilities for monitoring nanofiber formation with high spatial resolution. These methods can visualize the internal structure of the electrified jet, revealing subtle density variations that precede defect formation. While currently limited to specialized facilities, such advanced characterization methods provide fundamental insights into electrospinning physics that inform simpler inline monitoring strategies.

The development of standardized protocols for inline monitoring remains an active area of research. Challenges include the integration of multiple sensors without disrupting the electric field, real-time data processing for high-speed processes, and the establishment of correlation between measured signals and final fiber properties. Progress in these areas contributes to the transition from laboratory-scale electrospinning to industrial production, where consistent quality and defect minimization are paramount.

Future directions in electrospinning monitoring may incorporate advanced spectroscopic techniques such as terahertz spectroscopy for non-contact thickness measurement or hyperspectral imaging for spatial resolution of chemical composition. The increasing availability of compact, high-performance sensors and computing systems enables more sophisticated process control strategies. These advancements support the growing applications of electrospun nanofibers in filtration, biomedical devices, energy storage, and other fields where precise control over fiber properties is essential.