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Record W100195480 · doi:10.1007/0-306-48628-8_2

Biomedical Applications of Semiconductor Quantum Dots

2006· book-chapter· en· W100195480 on OpenAlexaff
Anupam Singhal, H. C. Fischer, Johnson M. S. Wong, Warren C. W. Chan

Bibliographic record

VenueKluwer Academic Publishers eBooks · 2006
Typebook-chapter
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFluorophorePhotobleachingQuantum dotNanotechnologyBiomoleculeFluorescenceMaterials scienceSemiconductorOptoelectronicsNanocrystalOrganic semiconductorPhotoluminescenceMolecular bindingChemistryMoleculeOpticsPhysics

Abstract

fetched live from OpenAlex

In recent decades‚ the exquisite sensitivity and versatility of optical technologies have led to numerous breakthroughs in biological research‚ including real-time imaging of live cells‚ gene expression profiling‚ cell sorting‚ and clinical diagnostics. A key component in optical detection schemes is the probe design. These probes are constructed from organic fluorophores‚ such as fluorescein and tetramethylrhodamine (TMR)‚ and recognition molecules. The optical emission of fluorophores is used to visualize the activities of biomolecules‚ while the recognition molecules direct the fluorophores to specific cells‚ tissues‚ or organs. Although optical probes are widely used‚ most organic fluorophores exhibit unfavourable properties that have hampered their applications in single-protein tracking in living cells‚ molecular pathology‚ and other research areas. These properties include photobleaching‚ sensitivity to environmental conditions‚ and inability to excite multiple fluorophores using a single wavelength. A new generation of probes has emerged in the last five years that overcomes many of the limitations associated with organic fluorophores. These probes employ fluorophores that are sub-100 nm in size and composed of inorganic atoms. Unlike organic-only fluorophores‚ the optical and electronic properties of inorganic fluorophores can be tuned during the synthesis process by changing their size‚ shape‚ or composition. In this chapter‚ we will describe the use of one type of inorganic fluorophore‚ semiconductor nanocrystals‚ for the development of “custom-designed” probes for biomedical detection. Semiconductor nanocrystals‚ also known as “quantum dots” (qdots)‚ are typically composed of atoms from groups II-VI (CdSe‚ CdS‚ ZnSe) and III-V (InP and InAs)‚ and are defined as particles with physical dimensions smaller than the Bohr exciton radius. The Bohr exciton radius of prototypical CdSe qdots‚ as illustrated in Fig. 1‚ is ~10 nm. The unique optical and electronic properties of qdots have spurred a great deal of research into their potential applications in the design of novel biological probes‚ light emitting diodes‚ photovoltaic cells‚ among other devices.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.378
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.242
Teacher spread0.216 · 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
GenreOther

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

Citations2
Published2006
Admission routes1
Has abstractyes

Explore more

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