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Record W3168253962 · doi:10.1039/d1na00203a

Does black phosphorus hold potential to overcome graphene oxide? A comparative review of their promising application for cancer therapy

2021· review· en· W3168253962 on OpenAlexfundno aff
Amalia Ruiz, Cristina Martín, Giacomo Reina

Bibliographic record

VenueNanoscale Advances · 2021
Typereview
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsnot available
FundersQueen's UniversityUniversity of BrightonUniversidad de La HabanaUniversity of East Anglia
KeywordsBlack phosphorusSurface modificationGrapheneCancer therapyNanotechnologyImmunotherapyCancer immunotherapyMedicineCancerMaterials scienceInternal medicineEngineeringChemical engineeringOptoelectronics

Abstract

fetched live from OpenAlex

Although graphene oxide (GO) is leading the way in the biomedical field of 2D materials, nanosized black phosphorus (NBP) has recently come to attention for use in this challenging field. A direct comparison between these two materials, in this context, has never been described. Therefore, in this mini-review, we will critically compare the applications of NBP and GO in cancer therapy. Material functionalisation, biodegradation by design, phototherapy and immunotherapy will be summarised. This work aims to inspire researchers in designing the next generation of safe NBP platforms for cancer treatment, taking advantage of the vast experience gained with GO.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.950
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
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 head0.029
GPT teacher head0.330
Teacher spread0.300 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations9
Published2021
Admission routes1
Has abstractyes

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