Numerical Simulation of a Novel Laser-Assisted Method Enabling Multiscale Bio-Printing
Bibliographic record
Abstract
Three-dimensional (3D) bio-printing has emerged as one of the most influential applications of printing technologies, aiming to address the increased demand for living constructs with long term mechanical and biological stability suitable for transplantation and drug screening applications [1]. Currently, an open challenge in the laser bioprinting field is the fabrication of living constructs of biologically relevant size (~cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) with micrometric resolution, i.e., multiscale printing. The paper proposes a novel laser-assisted method that enables multiscale printing of 3D constructs. The method is inspired by studies in the field of laser-assisted drug injection [2]. We will discuss the bio-printing principle, involving a sequence of mechanisms (Figure 1): i) nanosecond (ns) pulsed laser (τ = 6 ns, λ = 532 nm) interaction with liquid, ii) cavitation, iii) bubble dynamics, iv) fluid structure interaction, and v) jet dynamics. We used a multiphysics simulation software (COMSOL) to numerically simulate the involved mechanisms. To calculate laser-induced bubble dynamics in a closed chamber, we solved the Rayleigh-Plesset differential equation coupled to a modified Tait equation of state, which accounts for the pressure increase in the chamber because of the laser-induced bubble expansion. We considered 20% conversion of the laser pulse energy to bubble energy, which is a value well documented in the literature [3]. We applied the calculated spatiotemporal dynamics of the bubble boundary as a moving wall to calculate fluid-membrane interaction and the resulting membrane velocity. The membrane velocity profile was then applied to a two-phase flow model to simulate the bio-ink ejection dynamics. We will present the dependence of the jet-dynamics on various key experimental conditions, including liquid rheological properties (dynamic viscosity: 0.89-26.85 mPa·s, density: 996.89-1190.4 kg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , laser energy: 5-500J). Finally, we will present an optimization study aiming to reproducible and controllable printing of bio-ink drops with the following characteristics: ejection velocity 5-50 m/s, volume 0.05-30 nL, at the kHz repetition rate regime. Our results demonstrate reliable high-resolution bio-printing for an extended bio-ink viscosity range, representing a model bio-ink that is currently impossible to print using a single conventional bio-printing technology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".