arXiv subjects

A. Lamelas

Publications and source records attributed to A. Lamelas.

2 recordsLinked to original sources

arXiv2026

Lattice location of ion-implanted 6He in diamond

The properties of He in diamond are of interest with respect to geological dating and the creation of color centers with possible quantum applications. We report on the lattice location of the short-lived ion implanted nuclear probe 6He (t1/2=807 ms) in diamond, which was performed using the beta emission channeling method at CERN's ISOLDE facility. 6He was implanted with 30 keV into a single-crystalline artificial diamond sample kept at a temperature ranging from 30 deg C up to 800 deg C. By means of comparing the measured emission channeling patterns along different crystallographic directions with simulated yields for a variety of possible sites, we conclude that the implanted 6He occupies tetrahedral (T) interstitial sites, in agreement with theoretical predictions that T sites should be the preferred position of He in diamond. Implantation at 800 deg C resulted in a drop in the tetrahedral interstitial fraction by 20%, which we interpret as the onset of diffusion, 6He thus being able to change to lattice sites of low crystallographic symmetry, or reach the surface of the sample or escape to the bulk during its lifetime. We estimate the activation energy for interstitial migration of He to be around 1.63-2.89 eV, which agrees with theoretical predictions of 1.41-2.36 eV from the literature. Activation energies around 2 eV would mean that simple interstitial He cannot be stable in diamond on geological time scales, thus to remain inside, it should be bound to some defect in the material or exist in another form such as within inclusions of other minerals or liquids, or possibly small He bubbles.

cond-mat.mtrl-sci↗

arXiv2025

Canoe Paddling Quality Assessment Using Smart Devices: Preliminary Machine Learning Study

Over 22 million Americans participate in paddling-related activities annually, contributing to a global paddlesports market valued at 2.4 billion US dollars in 2020. Despite its popularity, the sport has seen limited integration of machine learning (ML) and remains hindered by the cost of coaching and specialized equipment. This study presents a novel AI-based coaching system that uses ML models trained on motion data and delivers stroke feedback via a large language model (LLM). Participants were recruited through a collaboration with the NYU Concrete Canoe Team. Motion data were collected across two sessions, one with suboptimal form and one with corrected technique, using Apple Watches and smartphones secured in sport straps. The data underwent stroke segmentation and feature extraction. ML models, including Support Vector Classifier, Random Forest, Gradient Boosting, and Extremely Randomized Trees, were trained on both raw and engineered features. A web based interface was developed to visualize stroke quality and deliver LLM-based feedback. Across four participants, eight trials yielded 66 stroke samples. The Extremely Randomized Tree model achieved the highest performance with an F score of 0.9496 under five fold cross validation. The web interface successfully provided both quantitative metrics and qualitative feedback. Sensor placement near the wrists improved data quality. Preliminary results indicate that smartwatches and smartphones can enable low cost, accessible alternatives to traditional paddling instruction. While limited by sample size, the study demonstrates the feasibility of using consumer devices and ML to support stroke refinement and technique improvement.

cs.LG↗