Flash Boiling Atomization of Suspension for Application of Suspension Plasma Spraying
Bibliographic record
Abstract
Suspension Plasma Spraying (SPS) is a thermal spray technique used to deposit sub-micron and nano-sized particles. The liquid is evaporated by exposing the suspension to the plasma jet. Then, the particles are melted and directed in the plasma jet to impact the substrate and form a coating. SPS has lower solid feed rates and deposition efficiency compared to other thermal spray techniques. Increasing particle concentration in the suspension can enhance the feedstock deposition rate, but high viscosity and nozzle clogging are issues. \nTo address these issues, this study explores flash boiling atomization (FBA) as a novel injection method in SPS for high solids concentrations, up to 70 wt.%. FBA uses thermodynamic instability to break up a liquid jet. When superheated suspension is accelerated through a nozzle and its pressure drops below the saturation pressure, rapid boiling occurs. Vapor bubbles expand within the liquid jet, causing it to fragment into smaller parts. FBA has applications in various industries such as fuel injection, desalination, and pharmaceuticals. The main objective is to use FBA to inject high-solids suspensions into the plasma flow to create SPS coatings. \nSuspension injection in SPS can be axial or radial. In axial injection, fragmentation occurs inside the torch, while in radial injection, the suspension is injected from outside the torch into the plasma flow. Conventional radial injection methods include spray atomization, which creates disintegrated droplets, and mechanical injection, which produces a continuous jet. This study compared coatings made with FBA to those made with mechanical injection, assessing microstructure, deposition weight per pass, deposition efficiency, and coating thickness. Results indicated improvements across these parameters. \nWater and ethanol are common suspension solvents. Water-based suspensions face challenges with atomization and evaporation resistance, impacting coating properties. FBA can improve fragmentation and prevent clogging by reducing viscosity and surface tension in the superheated state. The effect of high solids concentration and plasma power on coating microstructure, thickness per pass, deposition weight per pass, and deposition efficiency was investigated, showing that a dense coating microstructure with high solids deposition can be achieved using 70 wt.% suspension and a high power torch.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".