Measurement of isotopic separation of argon with the prototype of the cryogenic distillation plant Aria for dark matter searches

The Aria cryogenic distillation plant, located in Sardinia, Italy, is a key component of the DarkSide-20k experimental program for WIMP dark matter searches at the INFN Laboratori Nazionali del Gran Sasso, Italy. Aria is designed to purify the argon, extracted from underground wells in Colorado, USA, and used as the DarkSide-20k target material, to detector-grade quality. In this paper, we report the first measurement of argon isotopic separation by distillation with the 26 m tall Aria prototype. We discuss the measurement of the operating parameters of the column and the observation of the simultaneous separation of the three stable argon isotopes: 36Ar , 38Ar , and 40Ar . We also provide a detailed comparison of the experimental results with commercial process simulation software. This measurement of isotopic separation of argon is a significant achievement for the project, building on the success of the initial demonstration of isotopic separation of nitrogen using the same equipment in 2019

Directionality of nuclear recoils in a liquid argon time projection chamber

The direct search for dark matter in the form of weakly interacting massive particles (WIMP) is performed by detecting nuclear recoils (NR) produced in a target material from the WIMP elastic scattering. A promising experimental strategy for direct dark matter search employs argon dual-phase time projection chambers (TPC). One of the advantages of the TPC is the capability to detect both the scintillation and charge signals produced by NRs. Furthermore, the existence of a drift electric field in the TPC breaks the rotational symmetry: the angle between the drift field and the momentum of the recoiling nucleus can potentially affect the charge recombination probability in liquid argon and then the relative balance between the two signal channels. This fact could make the detector sensitive to the directionality of the WIMP-induced signal, enabling unmistakable annual and daily modulation signatures for future searches aiming for discovery. The Recoil Directionality (ReD) experiment was designed to probe for such directional sensitivity. The TPC of ReD was irradiated with neutrons at the INFN Laboratori Nazionali del Sud, and data were taken with 72 keV NRs of known recoil directions. The direction-dependent liquid argon charge recombination model by Cataudella et al. was adopted and a likelihood statistical analysis was performed, which gave no indications of significant dependence of the detector response to the recoil direction. The aspect ratio R of the initial ionization cloud is estimated to be 1.037 +/- 0.027 and the upper limit is R < 1.072 with 90% confidence levelComment: 20 pages, 10 figures, submitted to Eur. Phys. J.

Sensitivity projections for a dual-phase argon TPC optimized for light dark matter searches through the ionization channel

Dark matter lighter than 10 GeV/c$^2$ encompasses a promising range of candidates. A conceptual design for a new detector, DarkSide-LowMass, is presented, based on the DarkSide-50 detector and progress toward DarkSide-20k, optimized for a low-threshold electron-counting measurement. Sensitivity to light dark matter is explored for various potential energy thresholds and background rates. These studies show that DarkSide-LowMass can achieve sensitivity to light dark matter down to the solar neutrino floor for GeV-scale masses and significant sensitivity down to 10 MeV/c$^2$ considering the Migdal effect or interactions with electrons. Requirements for optimizing the detector's sensitivity are explored, as are potential sensitivity gains from modeling and mitigating spurious electron backgrounds that may dominate the signal at the lowest energies

Dynamic mechanical properties of activated carbon–filled epoxy nanocomposites.

Nano-activated carbons obtained from oil palm empty fiber bunch (AC-EFB), bamboo stem (AC-BS), and coconut shells (AC-CNS) were reinforced in epoxy matrix to fabricate epoxy nanocomposites. The dynamic mechanical analysis of epoxy nanocomposites was carried out, and 5% AC-CNS treated with KOH-filled epoxy composites displayed the highest storage modulus of all the activated carbon–filled epoxy composites. The incorporation of a small amount of AC-BS, AC-EFB, and AC-CNS to the epoxy matrix enhanced the damping characteristics of the epoxy nanocomposites. The 5% AC-EFB treated with H3PO4 filled epoxy composites showed the highest glass transition temperature (Tg) in all temperature ranges

Geostatistical Models for the Prediction of Water Supply Network Failures in Bogotá, Integrating Machine Learning Algorithms

[EN] Currently new strategies of spatial referencing, data analysis, and machine learning methods are integrated with Geographical Information Systems (GISs) to understand specific characteristics and water supply dynamics. This work explores the variables that can cause spacial failures and potential risk areas with application to a zone in the Bogotá water supply network. Machine learning algorithms are proposed to generate prediction models and potential failure maps. A sensitivity analysis was held to identify the model with the best fit for the estimation. This study will allow water supply decisions makers to focalize their efforts in the field.[ES] Actualmente se buscan nuevas estrategias y/o metodologías basadas en la integración de los Sistemas de Información Geográfica (SIGs) como forma de georeferenciacion espacial y visualización de las variables analizadas, junto con métodos de aprendizaje automático (Machine Learning) que permitan entender características puntuales, variables influyentes y dinámicas de los sistemas de abastecimiento de agua potable.En este trabajo se hace la identificación espacial de los fallos y zonas potenciales de riesgo que se presentan en una zona de la red de abastecimiento de Bogotá, explorando las variables que puedan tener mayor incidencia en los mismos. Se propone el uso de algoritmos de aprendizaje automático para la generación de modelos de predicción y la elaboración de mapas de fallos potenciales, identificando, a través de un análisis de sensibilidad, cuál de estos modelos presenta un mejor ajuste en la estimación. Este estudio permite a los gestores del abastecimiento una localización precisa y eficiente de los fallos en la red, apoyando el proceso de toma de decisiones.Navarrete-López, CF.; Calderón-Rivera, D.; Díaz Arévalo, JL.; Herrera Fernández, AM.; Izquierdo Sebastián, J. (2018). Modelos geoestadísticos para la predicción de fallos de una zona de la red de abastecimiento de agua de Bogotá, integrando algoritmos de Machine Learning. Social Science Research Network. 1-8. https://doi.org/10.2139/ssrn.3113048S1

In-line water heating system for industrial application

Due to the increase of electricity prices in South Africa and the World, there exists the need for energy efficient water heating systems. The authors of this paper conducted a literature study on existing water heating systems. The study shows that there is a possibility of designing more efficient water heating system at lower cost. An in-line water heating system for industrial applications was designed and tested. Before the system was designed a Finite Element Method analysis of the system was carried out to predict the systems’ pressure and temperature. During a physical model test the energy consumption was measured and compared to a conventional storage water heating system. The designed in-line water heating system is shown to be more energy efficient than the conventional storage water heating system. It also had a variety of advantages such as: size, adaptability to a variety of locations, and robustnes