Nanoscale reference and test materials for the validation of characterization methods for engineered nanomaterials — current state, limitations, and needs
Bibliographic record
Abstract
The rational design of engineered nanomaterials (NMs) with improved functionality and their increasing industrial application requires reliable, validated, and ultimately standardized characterization methods for their application-relevant, physicochemical key properties such as size, size distribution, shape, or surface chemistry. This calls for nanoscale (certified) reference materials (CRMs; RMs) and well-characterized reference test materials (RTMs) termed also quality control (QC) samples, assessed, e.g., in interlaboratory comparisons, for the validation and standardization of commonly used characterization methods. Thereby, increasing concerns regarding potential risks of NMs are also addressed and the road for safe and sustainable-by-design concepts for the development of new functional NMs and their use as nanomedicines is paved. With this respect, we will provide an overview of relevant international standardization and regulatory activities, definitions, and recommendations on characterization methods and review currently available organic or inorganic nanoscale CRMs, RMs, and RTMs, including their characterization or certification. In addition, we will highlight typical applications to streamline the regulatory approval process and improve manufacturability including the special challenges imposed by the colloidal nature and sometimes limited stability of NMs. Subsequently, we will critically assess the limitations of currently available nanoscale RMs and RTMs and address the gaps to be filled in the future such as the availability of NMs that come with reference data on properties other than commonly addressed particle size, such as surface chemistry or particle number concentration, or more closely resemble commercially available formulations or address application-relevant matrices.
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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 | 0.058 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".