arXiv subjects

Carlos Cardenas

Publications and source records attributed to Carlos Cardenas.

7 recordsLinked to original sources

arXiv2025

Optical Properties and Spin States of Inter-layer Carbon Defect Pairs in Hexagonal Boron Nitride: A First-Principles Study

Substitutional carbon defects in hexagonal boron nitride (hBN) are prominent single photon emitters (SPEs), and their potential for spin activity ($S\geq1$) is particularly intriguing. While studies have largely focused on intra-layer defects, we employ density functional theory (DFT) to investigate inter-layer dimers of identical carbon species (C$_X$C$_X$). We demonstrate that these C$_X$C$_X$ pairs can exhibit a stable triplet spin state at room temperature when closely spaced (e.g., within 3.5-7.1 Å) across hBN layers. As their separation increases beyond this range (e.g., $>7$ Å), they transition into weakly interacting $S=1/2$ pairs, characterized by singlet-triplet degeneracy. This regime is predicted to result in a very small zero-field splitting for the triplet manifold, offering a potential explanation for certain optically detected magnetic resonance (ODMR) signals. The zero-phonon line (ZPL) energy of these inter-layer C$_X$C$_X$ pairs is found to be practically monochromatic and within the visible range. Furthermore, we identify specific C$_B$C$_B$ inter-layer configurations exhibiting atypical low-energy phonon replicas due to out-of-plane vibrational coupling, a finding that may clarify the vibronic structure of other hBN emitters, such as the 'yellow emitters'.

cond-mat.mtrl-sci↗

arXiv2025

Virtual Dosimetrists: A Radiotherapy Training "Flight Simulator"

Effective education in radiotherapy plan quality review requires a robust, regularly updated set of examples and the flexibility to demonstrate multiple possible planning approaches and their consequences. However, the current clinic-based paradigm does not support these needs. To address this, we have developed 'Virtual Dosimetrist' models that can both generate training examples of suboptimal treatment plans and then allow trainees to improve the plan quality through simple natural language prompts, as if communicating with a dosimetrist. The dose generation and modification process is accurate, rapid, and requires only modest resources. This work is the first to combine dose distribution prediction with natural language processing; providing a robust pipeline for both generating suboptimal training plans and allowing trainees to practice their critical plan review and improvement skills that addresses the challenges of the current clinic-based paradigm.

physics.med-ph↗

arXiv2024

Manipulating the wavelength of single photons in insulating van der Waals heterostructures: theory and application to bilayer hexagonal boron nitride

Van der Waals heterostructures are promising for adding new functionalities to two-dimensional materials. In this study, we focus on single photon emitters hosted in one layer and adjacent to other insulating two-dimensional material. Specifically, we show how the emission energy is modified by such a heterostructure. We developed a general approach to elucidate the mechanisms affecting the emission energy and studied the particular case of carbon substitutions in the hexagonal boron nitride bilayer.

cond-mat.mtrl-sci↗

arXiv2022

Resection cavity auto-contouring for patients with pediatric medulloblastoma using only CT information

Purpose: Target delineation for radiation therapy is a time-consuming and complex task. Autocontouring gross tumor volumes (GTVs) has been shown to increase efficiency. However, there is limited literature on post-operative target delineation, particularly for CT-based studies. To this end, we trained a CT-based autocontouring model to contour the post-operative GTV of pediatric patients with medulloblastoma. Methods: 104 retrospective pediatric CT scans were used to train a GTV auto-contouring model. 80 patients were then preselected for contour visibility, continuity, and location to train an additional model. Each GTV was manually annotated with a visibility score based on the number of slices with a visible GTV (1 = <25%, 2 = 25%-50%, 3 = >50%-75%, and 4 = >75%-100%). Contrast and the contrast-to-noise ratio (CNR) were calculated for the GTV contour with respect to a cropped background image. Both models were tested on the original and pre-selected testing sets. The resulting surface and overlap metrics were calculated comparing the clinical and autocontoured GTVs and the corresponding clinical target volumes (CTVs). Results: 80 patients were pre-selected to have a continuous GTV within the posterior fossa. Of these, 7, 41, 21, and 11 were visibly scored as 4, 3, 2, and 1, respectively. The contrast and CNR removed an additional 11 and 20 patients from the dataset, respectively. The Dice similarity coefficients (DSC) were 0.61 +/- 0.29 and 0.67 +/- 0.22 on the models without pre-selected training data and 0.55 +/- 13.01 and 0.83 +/- 0.17 on the models with pre-selected data, respectively. The DSC on the CTV expansions were 0.90 +/- 0.13. Conclusion: We automatically contoured continuous GTVs within the posterior fossa on scans that had contrast >=10 HU. CT-Based auto-contouring algorithms have potential to positively impact centers with limited MRI access.

physics.med-ph↗

arXiv2022

Automation of Radiation Treatment Planning for Rectal Cancer

To develop an automated workflow for rectal cancer three-dimensional conformal radiotherapy treatment planning that combines deep-learning(DL) aperture predictions and forward-planning algorithms. We designed an algorithm to automate the clinical workflow for planning with field-in-field. DL models were trained, validated, and tested on 555 patients to automatically generate aperture shapes for primary and boost fields. Network inputs were digitally reconstructed radiography, gross tumor volume(GTV), and nodal GTV. A physician scored each aperture for 20 patients on a 5-point scale(>3 acceptable). A planning algorithm was then developed to create a homogeneous dose using a combination of wedges and subfields. The algorithm iteratively identifies a hotspot volume, creates a subfield, and optimizes beam weight all without user intervention. The algorithm was tested on 20 patients using clinical apertures with different settings, and the resulting plans(4 plans/patient) were scored by a physician. The end-to-end workflow was tested and scored by a physician on 39 patients using DL-generated apertures and planning algorithms. The predicted apertures had Dice scores of 0.95, 0.94, and 0.90 for posterior-anterior, laterals, and boost fields, respectively. 100%, 95%, and 87.5% of the posterior-anterior, laterals, and boost apertures were scored as clinically acceptable, respectively. Wedged and non-wedged plans were clinically acceptable for 85% and 50% of patients, respectively. The final plans hotspot dose percentage was reduced from 121%($\pm$ 14%) to 109%($\pm$ 5%) of prescription dose. The integrated end-to-end workflow of automatically generated apertures and optimized field-in-field planning gave clinically acceptable plans for 38/39(97%) of patients. We have successfully automated the clinical workflow for generating radiotherapy plans for rectal cancer for our institution.

physics.med-ph↗

arXiv2022

Automated WBRT Treatment Planning via Deep Learning Auto-Contouring and Customizable Landmark-Based Field Aperture Design

In this work, we developed and evaluated a novel pipeline consisting of two landmark-based field aperture generation approaches for WBRT treatment planning; they are fully automated and customizable. The automation pipeline is beneficial for both clinicians and patients, where we can reduce clinician workload and reduce treatment planning time. The customizability of the field aperture design addresses different clinical requirements and allows the personalized design to become feasible. The performance results regarding quantitative and qualitative evaluations demonstrated that our plans were comparable with the original clinical plans. This technique has been deployed as part of a fully automated treatment planning tool for whole-brain cancer and could be translated to other treatment sites in the future.

eess.IV↗

arXiv2019

Electronic Structure of First and Second Row Atoms under Harmonic Confinement

Atoms under pressure undergo a series of processes and modification of its electronic structure. Examples are the spontaneous ionization, stabilization of excited-state configurations that result in a level-crossing with the ground state of the free atom, and contraction of atomic shells. In this work, we do a systematic study of the effects of confinement with harmonic potential on the electronic structure of atoms from H to Ne. Dynamic and static correlation is taken into account by performing CCSD and CASSCF calculations, respectively. Because the strength of harmonic confinement cannot be translated into pressure, we envisioned a "calibration" method to transform confinement into pressure. We focused on the effect of confinement on: i) changes of electron distribution and localization within the $K$ and $L$ atomic shells, ii) confinement-induced ionization pressure, iii) level crossing of electronic states, and iv) the electron correlation energy. We found that contraction of valence and core shells are not negligible and that the use of standard pseudopotentials might be not adequate to study solids under extreme pressures. The critical pressure at which and atom ionizes follows a periodic trend, and it ranges from $28$ GPa for Li to $10.8$ TPa for Ne. In Li and a Be, pressure induces mixing of the ground state configuration with excited states. At high pressure, the ground state of Li and Be becomes a doublet and a triplet with configurations $1s^22p$ and $1s^22s2p$ respectively. The potential consequences of these changes of configuration on the chemistry of Be are discussed. Finally, the changes in the amount of electron correlation are characterized and analyzed in terms of the RPA approximation. For atoms with fewer electrons in the valence shell correlation increases, but for atoms with more electron, the increasing of kinetic energy dominates over electron correlation.

physics.chem-ph↗