Nilseia Aparecida Barbosa

Simulations of quantum-dot-based ionizing radiation detectors require the electronic gap ofthe dots, which enters the light-yield bound, the mean energy per electron–hole pair, and theemission wavelength. For silicon quantum dots, this gap is usually computed on hydrogen-passivated Si𝑛 H𝑚 clusters, whose construction involves geometric and connectivity choices that are rarely reported. An open Python/PySCF protocol that builds and validates such clusters is presented, and the dependence of the structures and of the HOMO–LUMO gap on these choices is quantified for diameters of 0.8–2.0 nm. Only one value of the minimum-coordination criterion, 𝑛_min = 2, changed the structure without emptying the cluster. Directional passivation produced H–H pairs 1.423 Å apart, a distance derived here in closed form; a filling criterion removed them by construction, at a steric cost above 1 eV and with little effect on the gap. The cutting criterion, checked with two basis sets, changed the gap by up to 0.74 eV below 1 nm, three times the size-only estimate. Propagated to detector inputs, it shifts the light-yield bound and the pair-creation energy by 3.5–11% for 0.8–1.5 nm clusters, comparable to the residual basis-set error and larger than the effects of relaxation or density fitting. The protocol also yields models of the typical radiation-induced defect: removing one hydrogen atom leaves a localized dangling bond whose levels lie deep in the gap and roughly halve the effective gap in PBE, several times the construction effects. Geometries and scripts are made available.

Nanostructured carbon materials are low-dimensional systems relevant to oncology biosensing, with their utility arising from an electronic structure coupled to defect and edge states, a charge-transfer behavior and optical response that report molecular binding, an interfacial chemistry governing contact with the analyte, and, in selected cases, magnetic properties enabling manipulation and readout. In functional terms, these behaviors trace to specific quantum-relevant features—quantum confinement, edge and defect states, and the resulting size-dependent optical and charge-transfer responses—rather than to a generic quantum-material designation. This critical review treats nanocarbons as engineered biointerfaces whose performance is set by how the carbon surface behaves in biological fluid, how recognition chemistry is anchored, and how the binding event is transduced, with magnetic responsiveness, stability, reproducibility, and fabrication control as decisive constraints. The analysis separates three material classes—non-magnetic nanocarbon sensors, hybrid carbon–magnetic systems, and defect-associated or potentially metal-free magnetic carbons—while grading evidence as direct, adjacent, comparator-derived, or prospective. Directly, graphene, carbon nanotubes, carbon dots, graphene quantum dots, and magnetic carbon hybrids serve in electrochemical, optical and fluorescent, field-effect, and magnetic-assisted formats. Clinical translation, however, remains constrained by biofouling, protein-corona formation, matrix interference, unstable functionalization, batch variability, incomplete standardization, and scarce validation in real samples and patient cohorts. Metal-free or defect-associated magnetic carbons therefore warrant caution, remaining prospective platforms until the preservation of magnetic response, reproducible functionalization, matrix compatibility, safety, and measurable analytical advantage are directly demonstrated.

Nano-carbon biointerfaces offer versatile platforms for cancer biomarker detection, but their progression from analytical proof-of-concept to clinically usable diagnostic evidence remains uneven. This scoping review maps 191 primary studies published from 2024 to 2026, covering nano-carbon families, surface chemistries, transduction architectures, biological matrices, and translational endpoints in cancer biosensing. The evidence space spans four nano-carbon dimensional classes: zero-dimensional carbon dots and quantum dots, one-dimensional carbon nanotubes, two-dimensional graphene-derived materials, and three-dimensional hybrid composites. Across these platforms, analytical sensitivity did not scale monotonically with nano-carbon dimensionality; instead, performance was shaped by the interaction between material architecture, biointerface chemistry, recognition strategy, transduction modality, and matrix context. A Translational Readiness Matrix showed that approximately 67% of studies remained at Low Evidence Level, whereas only approximately 10% reached Strong Evidence Level. Five recurring bottlenecks constrained translation: incomplete reproducibility reporting, limited Real-matrix Validation, scarce comparator-based clinical evidence, insufficient manufacturing-scale data, and weak regulatory or deployment planning. To address these gaps, this review proposes a nine-item minimum reporting checklist and a four-stage validation roadmap to support more reproducible, comparable, and clinically oriented nano-carbon biosensor development.