arXiv subjects

Cleber L. Rodrigues

Publications and source records attributed to Cleber L. Rodrigues.

7 recordsLinked to original sources

arXiv2025

Uncertainty minimization in electronic stopping cross-section measurements using the backscattering method

Accurate determination of electronic stopping cross sections is critical for ion beam analysis and related applications. While transmission methods are well established, backscattering approaches remain less explored from a metrological perspective, often lacking a systematic treatment of uncertainties. In this work, we present a quantitative framework to optimize experimental geometry in backscattering-based stopping measurements, explicitly accounting for both statistical and systematic errors. Applying the method to helium ions in gold thin films, we identify angular conditions that balance precision and accuracy, achieving total uncertainties below 3\% over a wide energy range. The results, benchmarked against SRIM and ICRU-49, demonstrate that our approach improves the reliability of RBS-derived stopping data and strengthens their use for reference purposes and model validation.

physics.ins-det↗

arXiv2025

Electronic stopping cross sections of tungsten to swift ions and comparisons with models

Accurate stopping power data for tungsten is crucial for ion beam analysis (IBA) techniques applied to fusion-related materials. In this work, we present new experimental measurements of the stopping power of tungsten for protons and alpha particles, addressing key gaps in fundamental databases. Our results provide a densely spaced dataset, refining the practical uncertainty limits to approximately 1.5% for protons and 2.0% for alpha particles. We critically compare our findings with semi-empirical and theoretical models, evaluating their performance in describing the stopping power of tungsten for light projectiles. By improving the accuracy and reliability of stopping power data, we contribute to the enhancement of the applicability of ion-beam methods for characterizing tungsten in fusion-related research. These findings contribute to the refinement of semi-empirical models and support the ongoing efforts to develop more precise theoretical frameworks for ion-solid interactions in high-Z materials.

cond-mat.other↗

arXiv2022

Bias and synergy in the self-consistent approach of data analysis of ion beam techniques

Using multiple ion beam analysis measurements, or techniques, combined with self-consistent data processing, generally allows extracting more (or more accurate) information from the measurements than processing separately data from single measurements. Solving ambiguities, improving the final depth resolution, defining constraints and extending applicability are the main strengths of the data-fusion approach. It basically consists in formulating a multi-objective minimization problem that can be tackled by the adoption of the weighted-sum method. A simulation study is reported in order to evaluate the systematic error inserted in the analysis by the choice of a specific objective function, or even by the weights or normalization adopted in the weighted-sum method. We demonstrate that the bias of the analyzed objective functions asymptotically converges to the true value for better statistics. We also demonstrate that the joint analysis inherits the accuracy of the most accurate measurement, establishing a rank of information content, where some combinations of measurements are more valuable than others, i.e. when processed together they provide more information by means of a better constraint for the multi-objective optimization.

physics.data-an↗

arXiv2022

Ion beam analysis and big data: How data science can support next-generation instrumentation

With a growing demand for accurate ion beam analysis on a large number of samples, it becomes an issue of how to ensure the quality standards and consistency over hundreds or thousands of samples. In this sense, a virtual assistant that checks the data quality, emitting certificates of quality, is highly desired. Even the processing of a massive number of spectra is a problem regarding the consistency of the analysis. In this work, we report the design and first results of a virtual layer under implementation in our laboratory. It consists of a series of systems running in the cloud that perform the mentioned tasks and serves as a virtual assistant for member staff and users. We aim to bring the concept of the Internet of Things and artificial intelligence closer to the laboratory to support a new generation of instrumentation.

physics.ins-det↗

arXiv2022

Processing of massive Rutherford Back-scattering Spectrometry data by artificial neural networks

Rutherford Backscattering Spectrometry (RBS) is an important technique providing elemental information of the near surface region of samples with high accuracy and robustness. However, this technique lacks throughput by the limited rate of data processing and is hardly routinely applied in research with a massive number of samples (i.e. hundreds or even thousands of samples). The situation is even worse for complex samples. If roughness or porosity is present in those samples the simulation of such structures is computationally demanding. Fortunately, Artificial Neural Networks (ANN) show to be a great ally for massive data processing of ion beam data. In this paper, we report the performance comparison of ANN against human evaluation and an automatic fit routine running on batch mode. 500 spectra of marker layers from the stellarator W7-X were used as study case. The results showed ANN as more accurate than humans and more efficient than automatic fits.

physics.comp-ph↗

arXiv2021

Self-consistent ion beam analysis: an approach by multi-objective optimization

Ion Beam Analysis (IBA) comprises a set of analytical techniques suited for material analysis, many of which are rather closely related. Self-consistent analysis of several IBA techniques takes advantage of this close relationship to combine different Ion Beam measurements in a unique model to obtain an improved characterization of the sample. This approach provides a powerful tool to obtain an unequivocal and reliable model of the sample, increasing confidence and reducing ambiguities. Taking advantage of the recognized reliability and quality of the simulations provided by SIMNRA, we developed a multi-process program for a self-consistent analysis based on SIMNRA calculations. MultiSIMNRA uses computational algorithms to minimize an objective function running multiple instances of SIMNRA. With four different optimization algorithms, the code can handle sample and setup parameters (including correlations and constraints), to find the set of parameters that best fits simultaneously all experimental data.

physics.comp-ph↗

arXiv2018

MultiSIMNRA: a computational tool for self-consistent ion beam analysis using SIMNRA

SIMNRA is widely adopted by the scientific community of ion beam analysis for interpretation of nuclear scattering analysis. Taking advantage of its recognized reliability and quality of the simulations, we developed a computer program that use parallel sessions of SIMNRA to perform self-consistent analysis for energy spectra of a given sample obtained using different techniques or experimental setups. In this paper, we present a result using MultiSIMNRA on self-consistent analysis for a multielemental thin film produced by magnetron sputtering. The results demonstrate the potentialities of the self-consistent analysis and its feasibility when using MultiSIMNRA.

physics.ins-det↗