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Record W7084597227 · doi:10.1002/adfm.202508766

Additively Manufactured Diamond for Energy Scavenging and Wireless Power Transfer in Implantable Devices

2025· article· en· W7084597227 on OpenAlexfundno aff

Bibliographic record

VenueAdvanced Functional Materials · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing in Agriculture
Canadian institutionsnot available
FundersRMIT UniversityAustralian Research CouncilOntario Ministry of Natural Resources and ForestryAustralian National Fabrication Facility
KeywordsMicroelectronicsWireless power transferDiamondWirelessEnergy transferPower (physics)Capacitive sensingLight-emitting diode

Abstract

fetched live from OpenAlex

Abstract Additive manufacturing is revolutionizing personalized medicine by enabling prostheses and implantable devices that better match the body's geometric constraints. This approach has primarily been used for mechanical implants, such as orthopaedic prostheses. Despite its clear benefits, additive manufacturing has not been used in microelectronic implants. This work introduces an additively manufactured diamond‐titanium hybrid as a material for the construction of electronically active implantable devices. Wireless power transfer using inductive and capacitive coupling is demonstrated and used to induce localized tissue heating as well as to power up an light emitting diode (LED). At the macroscale, the diamond‐titanium hybrid fulfils the requirements of traditional metallic biomaterials. At the nanoscale, the unique attributes of the hybrid material are used to demonstrate energy scavenging from the physiological flow of saline solution and its use for wireless flow sensing. In addition to fulfilling the structural role, additively manufactured diamond is a candidate material for use as part of microelectronic implants.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.194
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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