Nanosensors based on nanomaterials (NANOBIOSENS)
Bibliographic record
Abstract
The primary objective of this proposal is to bring together an international and interdisciplinary group of research teams who have different expertise areas to share the knowledge of different elements for building nano-biosensors. The development of future devices requires controlled assembly and placement of individual and/or multiple nano building blocks into the desired locations. By the accomplishment of this research proposal, the advantages of integration of nanomaterials into the structure of biosensors will be feasible. It is expected that the nanomaterial assembled biosensor structures will show an enhanced sensitivity due to the high surface area, higher porosity, and adjusted surface energy. In this research proposal, there are six participants from six countries with different expertise areas in the field of chemical engineering, biomedical engineering, materials science, physics, chemistry, and biology. Different pieces of work will thus be constructed into each other to first of all study their “separate” roles in that assembled piece of work. The international and interdisciplinary group of research team who came together is composed of Turkey, France, Ukraine, Canada, United States and (Rep. of) Korea. The estimated time of the project is 36 months. The proposed programme consists of three stages. STAGE 1 consists of shortly “synthesis of nanomaterials and their controlled assembly and organization of Si wafer substrates”. STAGE 2 is composed of the “biofunctionalization” of these materials built in STAGE 1 and the building a biosensor out of the assembled materials. STAGE 3 will focus on the “field tests” to see how affective these biosensors are working. Six trainings, three general meetings and three workshops are being planned for this research proposal in order to share all the knowledge and information gained throughout the work and to form the basis of long lasting collaborations.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.061 | 0.004 |
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; both teacher heads agree on what is shown here.
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".