An Efficient and Compact Wireless Solution for Blood Sterilization Apparatus
Bibliographic record
Abstract
Advancements in the medical industry are some of the key contributions of the scientific community. One related direction of research is the X-ray sterilization of blood that is stored for future use. Currently, the tags used to indicate the irradiation level of the blood rely on visual indicators rather than an automated method. As a result, the irradiation levels of the blood cannot be ascertained. This paper proposes a new wireless dosimeter tag to measure the radiation levels with sufficient precision so as to avoid any material wastage and costs. The tag relies on an RF tag which consists of a CMOS transmitter, energy harvesting component and an off-chip inkjet printed antenna. In this paper, using 0.13 μm CMOS technology a floating gate MOSFET dosimeter design and an off-chip inkjet printed antenna are presented. These two components are the basic building blocks of the complete wireless dosimeter system. The floating gate MOSFET design is studied for its pre and post-radiation performance. The measured results show how the difference in the device current can be used for the radiation dose measurements. For the antenna, a printed antenna with suitable shielding is employed to enhance its radiation performance in the dissipative blood environment. The measured radiation pattern shows adequate gain and back-lobe radiation characteristics, thus making the surroundings invisible to the antenna and best suited for the intended application.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".