Nanoscale gold nanoparticle (GNP)-laden tumor cell model and its use for estimation of intracellular dose from GNP-induced secondary electrons.

BACKGROUND: Gold nanoparticles (GNPs) accumulated within tumor cells have been shown to sensitize tumors to radiotherapy. From a physics point of view, the observed GNP-mediated radiosensitization is due to various downstream effects of the secondary electron (SE) production from internalized GNPs such as GNP-mediated dose enhancement. Over the years, numerous computational investigations on GNP-mediated dose enhancement/radiosensitization have been conducted. However, such investigations have relied mostly on simple cellular geometry models and/or artificial GNP distributions. Thus, it is at least desirable, if not necessary, to conduct further investigations using cellular geometry models that properly reflect realistic cell morphology as well as internalized GNP distributions at the nanoscale. PURPOSE: The primary aim of this study was to develop a nanometer-resolution geometry model of a GNP-laden tumor cell for computational investigations of GNP-mediated dose enhancement/radiosensitization. The secondary aim was to demonstrate the utility of this model by quantifying GNP-induced SE tracks/dose distribution at sub-cellular levels for further validation of a nanoscopic dose point kernel (nDPK) method against full-fledged Geant4 Monte Carlo (MC) simulation. METHODS: A transmission electron microscopy (TEM) image of a single cell showing cytoplasm, cellular nucleus, and internalized GNPs in the cellular endosome was segmented into sub-cellular levels based on pixel value thresholding. A corresponding material density was allocated to each pixel, and, by adding a thickness, each pixel was transformed to a geometric voxel and imported as a Geant4-acceptable input geometry file. In Geant4-Penelope MC simulation, a clinical 6 MV photon beam was applied, vertically or horizontally to the cell surface, and energy deposition to the cellular nucleus and cytoplasm, due to SEs emitted by internalized GNPs, was scored. Next, nDPK calculations were performed by generating virtual electron tracks from each GNP voxel to all nucleus and cytoplasm voxels. Subsequently, another set of Geant4 simulation was performed with both Penelope and DNA physics models under the geometry closely mimicking in vitro cell irradiation with a clinical 6 MV photon beam, allowing for derivation of nDPK specific to this geometry and further comparison between Gean4 simulation and nDPK method. RESULTS: The Geant4-calculated SE tracks and associated energy depositions showed significant dependence on photon incidence angle. For perpendicular incidence, nDPK results showed good agreement (average percentage pixel-to-pixel difference of 0.4% for cytoplasm and 0.5% for nucleus) with Geant4 results, while, for parallel incidence, the agreement became worse (-1.7%-0.7% for cytoplasm and -5.5%-0.8% for nucleus). Under the 6 MV cell irradiation geometry, nDPK results showed reasonable agreement (pixel-to-pixel Pearson's product moment correlation coefficient of 0.91 for cytoplasm and 0.98 for nucleus) with Geant4 results. CONCLUSIONS: The currently developed TEM-based model of a GNP-laden cell offers unprecedented details of realistic intracellular GNP distributions for nanoscopic computational investigations of GNP-mediated dose enhancement/radiosensitization. A benchmarking study performed with this model showed reasonable agreement between Geant4- and nDPK-calculated intracellular dose deposition by SEs emitted from internalized GNPs, especially under perpendicular incidence - a popular cell irradiation geometry and when the Geant4-Penelope physics model was used.

Deep Learning-Based TEM Image Analysis for Fully Automated Detection of Gold Nanoparticles Internalized Within Tumor Cell.

Transmission electron microscopy (TEM) imaging can be used for detection/localization of gold nanoparticles (GNPs) within tumor cells. However, quantitative analysis of GNP-containing cellular TEM images typically relies on conventional/thresholding-based methods, which are manual, time-consuming, and prone to human errors. In this study, therefore, deep learning (DL)-based methods were developed for fully automated detection of GNPs from cellular TEM images. Several models of "you only look once (YOLO)" v5 were implemented, with a few adjustments to enhance the model's performance by applying the transfer learning approach, adjusting the size of the input image, and choosing the best optimization algorithm. Seventy-eight original (12,040 augmented) TEM images of GNP-laden tumor cells were used for model implementation and validation. A maximum F1 score (harmonic mean of the precision and recall) of 0.982 was achieved by the best-trained models, while mean average precision was 0.989 and 0.843 at 0.50 and 0.50-0.95 intersection over union threshold, respectively. These results suggested the developed DL-based approach was capable of precisely estimating the number/position of internalized GNPs from cellular TEM images. A novel DL-based TEM image analysis tool from this study will benefit research/development efforts on GNP-based cancer therapeutics, for example, by enabling the modeling of GNP-laden tumor cells using nanometer-resolution TEM images.

Nanoscale focused electron beam induced etching of nickel using a liquid reactant.

Nickel nanostructures have found widespread application as both functional components, e.g. in magnetic systems, and as part of the lithographic pattern transfer process as etch masks, EUV mask absorbers, and imprint templates. Electron-beam induced etching of nickel is highly desirable for the repair and editing of masks and templates with high resolution and without substrate damage. However, there are no known gas-phase reactants that produce volatile nickel products under e-beam irradiation. Here we report the successful local etching of nickel by a focused electron beam in an environmental scanning electron microscope using a liquid reactant, aqueous sulfuric acid. Sulfuric acid did not spontaneously etch nickel under ESEM conditions, but nickel was etched in areas exposed to the electron beam. Etching parameters such as dose, refresh time, and addition of a surfactant were investigated. The extent of the etch increases with dose before terminating at sub-micron feature sizes. The etch resolution improves with the addition of surfactant. This approach enables local nickel patterning with complete film removal but without damaging underlying layers. With further refinement, the process may enable nickel absorber repair and editing and remove a significant obstacle to the use of nickel in EUV lithography.

Enhancement in the performance of nanostructured CuO-ZnO solar cells by band alignment.

In this study, we investigated the effect of cobalt doping on band alignment and the performance of nanostructured ZnO/CuO heterojunction solar cells. ZnO nanorods and CuO nanostructures were fabricated by a low-temperature and cost-effective chemical bath deposition technique. The band offsets between Zn1-x Co x O (x = 0, 0.05, 0.10, 0.15, and 0.20) and CuO nanostructures were estimated using X-ray photoelectron spectroscopy and it was observed that the reduction of the conduction band offset with CuO. This also results in an enhancement in the open-circuit voltage. It was demonstrated that an optimal amount of cobalt doping could effectively passivate the ZnO related defects, resulting in a suitable conduction band offset, suppressing interface recombination, and enhancing conductivity and mobility. The capacitance-voltage analysis demonstrated the effectiveness of cobalt doping on enhancing the depletion width and built-in potential. Through impedance spectroscopy analysis, it was shown that recombination resistance increased up to 10% cobalt doping, thus decreased charge recombination at the interface. Further, it was demonstrated that the insertion of a thin layer of molybdenum oxide (MoO3) between the active layer (CuO) and the gold electrode hinders the formation of a Schottky junction and improved charge extraction at the interface. The ZnO/CuO solar cells with 10% cobalt doped ZnO and 20 nm thick MoO3 buffer layer achieved the best power conversion efficiency of 2.11%. Our results demonstrate the crucial role of the band alignment on the performance of the ZnO/CuO heterojunction solar cells and could pave the way for further progress on improving conversion efficiency in oxide-based heterojunction solar cells.

Thermo-Optical Properties of Cobalt-Doped Zinc Oxide (ZnO) Nanorods.

Thermo-optical and structural properties of cobalt-doped zinc oxide (ZnO) nanorods grown by chemical bath deposition were investigated. The average nanorods diameter was found to increase with cobalt doping. X-ray diffraction (XRD), Raman, and Fourier Transform Infrared (FTIR) analysis confirm the substitution of cobalt into ZnO lattice without forming any impurities at higher doping and preserving the hexagonal wurtzite structure. Variations in the absorption spectrum, band gap, photoluminescence, electronic structure by X-ray photoelectron spectroscopy (XPS), and index of refraction were analyzed at different cobalt doping levels and annealing temperatures. The thermooptic coefficient of ZnO nanorods was extracted and explained regarding cobalt doping. We will also discuss the nature of cobalt incorporation in ZnO nanorods at various doping levels.