Direct sound printing: Way of manipulating ultrasonic chemistry to print directly engineering structures and remotely inside body
Bibliographic record
Abstract
Direct sound printing (DSP) is a new class of additive manufacturing processes developed in our lab, in which chemical reactions during the 3D printing process are driven by sonochemical route using cavitation bubbles induced by focused ultrasound waves. This invited paper will present methods and possibilities of printing engineering structures with DSP. In addition, this talk will cover a new area called remote distance printing (RDP) and consequent applications. RDP is a new realm introduced by DSP method in which the printing location is not accessible by common energy sources like light or heat. In this situation, ultrasound could penetrate optically opaque materials and conduct printing without direct access to the printing location. This concept opens a wide variety of applications in engineering or medical fields. The focus of this paper is the application of DSP-RDP in biomedical application to print objects inside body without open surgery in a non-invasive manner. Ultrasound penetrates skin and tissues in DSP and is focused on the printing location inside body where the printing material is injected. This work explains DSP in detail and the interaction of the sound with the printing material and how the material is transformed from liquid to solid. The process is demonstrated using a test study conducted using tissue phantoms and also real porcine tissue. This work opens new applications to 3D print with ultrasound where no other 3D printing approaches can achieve.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".