No abstract

Gold nanoparticles (AuNPs) have been investigated extensively in the past twenty years as a sensitizing agent in photon radiotherapy. Targeted delivery of AuNPs to specific sites in cells and tissues contributes to highly localized radiation dose enhancement, whereby the surrounding healthy structures can be largely spared from the unwanted radiation effects. The efficiency of introduced AuNPs with regard to dose enhancement depends on the properties of the nanoparticles since not all of deposited radiation energy reaches the intended biological target but is partially absorbed within the nanoparticles themselves or distributed elsewhere. The present paper investigates the influence of AuNP shape and localization on the enhancement and intracellular distribution of deposited energy in radiation therapy with photons. Energy deposition patterns are calculated with nanoscale accuracy through Monte Carlo simulations of radiation transport, which are optimized to accommodate a structured geometrical representation of the region loaded with AuNPs, i.e., to allow discrete modeling of individual nanoparticles. Same-volume nanoparticles of three commonly encountered shapes—nanospheres, nanorods, and square nanoplates—are examined, in order to inspect the differences in the propagation and absorption of secondary charged particles produced by the incident photons. Five different spatial distributions of spherical AuNPs at the single-cell level are studied in the simulations and compared according to the energy deposited in the cell nucleus. Photon energy, nanoparticle size, and concentration are also varied across simulation runs, and their influence is analyzed in connection to nanoparticle shape and localization. The obtained results reveal how the investigated nanoparticle properties affect their dose-enhancing ability and irradiation specificity in AuNP-augmented radiotherapy.

Abstract. Efficiency of a Monte Carlo algorithm for neutron dose calculation is compared in two implementations: a standard C++ code executed sequentially, and a CUDA C/C++ code which utilizes GPU resources for highly parallel processing. Both versions of the algorithm, developed specifically for this investigation, are based on the same physical model for the assessment of neutron dose in tissues, including lung, cortical bone and adipose tissue. The model treats emission and interaction of neutrons stochastically, utilizing cross sections for relevant interaction types. Several intentional simplifications have been introduced into the physical model used for simulations, which have allowed parts of the two codes to be related to one another in a straightforward way. A neutron's history is terminated when it leaves the outer ellipsoid (representing the human body), experiences any of the absorption interactions (inside one of the inner geometrical regions, representing tissues or organs), or if its energy falls below the cut-off limit set at 0.001 eV. The two approaches to algorithm implementation are compared according to execution speed, at various neutron source energies and for an increasing number of neutron histories. The fact that particle histories in a Monte Carlo simulation are independent from one another makes this kind of calculation suitable for implementation on parallel processing platforms. CUDA framework offers higher speeds of code execution, allowing more particle histories to be processed within a set time frame, and thus yields lower statistical uncertainty and higher reliability of the calculated neutron dose values. Appropriating standard C++ codes for CUDA is faced with specific challenges, which are described in the investigated case of neutron dose assessment. Despite the physical representation of neutron transport being somewhat simplified, comparison of both implementations to results obtained from MCNP shows good agreement in a wide range of neutron energies.

Organic contaminants from building materials negatively affect the health of people. This study presents an analytical method for the simultaneous identification and quantification of 9 phenolic compounds, i.e., phenol, 2-chlorophenol, 2,4-dimethylphenol, 2,4-dichlorophenol, 2,6-dichlorophenol, 4-chloro-3-methylphenol, 2,4,6-trichlorophenol, 2,3,4,6-tetrahlorophenol and pentachlorophenol, in concrete by a gas chromatographic method with mass spectrometric detection (GC-MS). By comparing the MS spectra of the test compounds with MS spectra of analytical standards, reliable identification was achieved. The method could be applied in a given range (from 0.01 to 7.5 mg kg-1) with appropriate parameters of precision, accuracy, repeatability and linearity. The developed method could be used for quality control testing of phenols in concrete during the construction of new buildings, old residences and construction waste. The measurement uncertainty of the phenolic compounds in concrete was evaluated using two approaches, i.e., GUM recommendations and a Monte Carlo method. Disagreement of those methods was observed. The Monte Carlo method could be used in the evaluation of combined measurement uncertainty for the determination of phenolic compounds in concrete.