The Coherent Neutrino-Nucleus Interaction Experiment (CONNIE) aims to detect the coherent scattering (CE nu NS) of reactor antineutrinos off silicon nuclei using thick fully depleted high-resistivity silicon CCDs. Two Skipper-CCD sensors with subelectron readout noise capability were installed at the experiment next to the Angra-2 reactor in 2021, making CONNIE the first experiment to employ Skipper-CCDs for reactor neutrino detection. We report on the performance of the Skipper-CCDs, the new data processing, data quality, and event selection for CE nu NS interactions, which enable CONNIE to reach a record low detection threshold of 15 eV. The data were collected over 300 days in 2021-2022 and correspond to exposures of 14.9 g-days with the reactor-on and 3.5 g-days with the reactor-off. The difference between the reactor-on and off event rates shows no excess and yields upper limits for the neutrino interaction rates, comparable with previous CONNIE limits from standard CCDs and higher exposures. Searches for new neutrino interactions beyond the Standard Model improve the previous CONNIE limit on a simplified model with light vector mediators. A first dark matter (DM) search by diurnal modulation by CONNIE obtains the best limits on the DM-electron scattering cross section by a surface-level experiment. These promising results, obtained using a very small-mass sensor, illustrate the potential of Skipper-CCDs to probe rare neutrino interactions and motivate the plans to increase the detector mass in the near future.
The flux of Hidden Sector particles from the Galactic halo reaching an underground detector can be significantly attenuated by interactions within the Earth for sufficiently large scattering crosssections. This attenuation gives rise to a characteristic daily modulation in the detection rate, due to Earth's rotation. We present results from a search for such a modulation using a 1.257 kg-day dataset collected with the DAMIC-M Low Background Chamber. A model-independent analysis reveals no significant modulation in the 1e- event rate over periods from 1 to 48 h, highlighting the excellent temporal stability of the detector. In a complementary model-dependent analysis, we target the expected daily modulation signature of Hidden Sector particles, with masses in the range [0.53,2] MeV/c2, interacting with electrons via a dark photon mediator. By leveraging the expected temporal evolution of the signal, we set improved constraints on Dark Matter masses below 1.2 MeV/c2, surpassing our previous limits.
This study investigates the mechanical behavior of BCC HfNbTaTiZr high-entropy alloy (HEA) nanoparticles (NPs) subjected to compression by a flat indenter using molecular dynamics simulations. NPs with random atomic distribution and diameters from 10 to 50 nm were considered. For comparison purposes, a 20 nm NP with chemical short-range order (CSRO) was also explored. The mechanical response revealed an increasing trend of the Young's modulus with respect to the NP size. However, maximum stress, yield stress, and flow stress showed negligible variations. Furthermore, the NP with CSRO showed enhanced mechanical properties, attributed to SRO cluster formation. Analysis of plastic activity revealed surface-dominated dislocation emission and absorption, with CSRO NP exhibiting an increase in dislocation density as the strain increased. Structural analysis elucidated persistent twin formation in all NPs, with a remarkable increase in the HCP population observed in the CSRO NP. These findings further improve our understanding of the mechanical behavior of HEA NPs, contributing to the design and development of advanced materials.
Large language models enable the creation of autonomous agents that interact in social environments, raising the question of whether agent-based platforms reproduce the organizational properties of human social networks. We compare Moltbook, a social network populated by AI agents, with early Reddit, focusing on how communities organize and differentiate semantic content, using network analysis and NLP methods to characterize semantic coherence and diversity within and between communities, and their relationship to user activity. We find a systematic difference between the two platforms. Reddit communities show stronger semantic coherence, closer alignment with community names, and greater semantic diversity, with individual communities spanning broader content and communities more differentiated from one another. This combination distinguishes Reddit from Moltbook, whose communities are more homogeneous, less differentiated, and increasingly misaligned with their names over time. Users on Reddit also participate across communities that are more semantically related than those connected by activity in Moltbook. At the interaction level, comment-network motif analysis shows Moltbook dominated by non-reciprocal, broadcast-like exchanges, whereas Reddit shows more reciprocal, chained interaction patterns. These results indicate that Reddit combines semantic coherence with diversity across organizational levels, a pattern not reproduced by the AI-agent network.
In this work, a new bionanosorbent of graphene oxide (GO) and Dunaliella salina (DS) microalgae was synthesized and applied for the adsorption and removal of malachite green (MG) from water and wastewater samples. The bionanosorbent (GO@DS) was characterized by Fourier transform infrared spectroscopy, zero charge point, scanning electron microscopy coupled to an energy dispersive X-ray detector, and X-ray diffraction. Using multivariate optimization and response surface methodology, the optimal adsorption variables were pH = 5, GO@DS mass = 5 mg, and malachite green concentration = 75 mg L−1, for which an adsorption capacity of 232.77 mg g−1 was obtained. Experimental kinetic results were adequately fit to the Elovich model. The Langmuir equilibrium model satisfactorily described the data obtained and a maximum adsorption capacity of 160.8 mg g−1 was obtained. The adsorption process was thermodynamically favorable, spontaneous, and exothermic. Finally, the GO@DS was applied to remove the dye from natural water and effluents, obtaining MG removal values between 85 and 99
