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Record W2963393559 · doi:10.1002/cphc.201900551

Emerging Two‐Dimensional Nanomaterials for Cancer Therapy

2019· review· en· W2963393559 on OpenAlex
Zilei Guo, Jiang Ouyang, Na Yoon Kim, Jinjun Shi, Xiaoyuan Ji

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemPhysChem · 2019
Typereview
Languageen
FieldMaterials Science
TopicMXene and MAX Phase Materials
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsNanomedicineNanotechnologyBiomedicineNanomaterialsCancer therapyMaterials scienceCancerMedicineNanoparticleBioinformatics

Abstract

fetched live from OpenAlex

Two-dimensional (2D) nanomaterials have drawn tremendous attention due to their unique physicochemical properties and promising applications in the fields of electronics, energy storage, and catalysis. Recently, the biomedicine community has gradually started to recognize the great potential of these nanostructured materials for biomedical applications - in particular those related to cancer therapy. In this review, we provide a brief overview of a few representative 2D nanomaterials, discuss their preparation strategies and physicochemical properties, and highlight their applications in cancer nanomedicine. We expect that this review will shed some light on the new opportunities associated with 2D nanomaterials for biomedical research.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.681
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.101
GPT teacher head0.391
Teacher spread0.290 · 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