Istituto Nazionale di Ricerca Metrologica
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Analysis of spin-squeezing generation in cavity-coupled atomic ensembles with continuous measurements
Get PDFWe analyze the generation of spin-squeezed states via coupling of three-level atoms to an optical cavity and continuous quantum measurement of the transmitted cavity field in order to monitor the evolution of the atomic ensemble. Using analytical treatment and microscopic simulations of the dynamics, we show that one can achieve significant spin squeezing, favorably scaling with the number of atoms N. However, contrary to some previous literature, we clarify that it is not possible to obtain Heisenberg scaling without the continuous feedback that is proposed in optimal approaches. In fact, in the adiabatic cavity removal approximation and large N limit, we find the scaling behavior N - 2 / 3 for spin squeezing and N - 1 / 3 for the corresponding protocol duration. These results can be obtained only by considering the curvature of the Bloch sphere, since linearizing the collective spin operators tangentially to its equator yields inaccurate predictions. With full simulations, we characterize how spin-squeezing generation depends on the system parameters and departs from the bad cavity regime, by gradually mixing with cavity-filling dynamics until metrological advantage is lost. Finally, we discuss the relevance of this spin-squeezing protocol to state-of-the-art optical clocks
Fast Frequency Characterization of Inductive Voltage Transformers Using Damped Oscillatory Waves
No full textElectrochemical rewiring through quantum conductance effects in single metallic memristive nanowires
No full textMemristive devices have been demonstrated to exhibit quantum conductance effects at room temperature. In these devices, a detailed understanding of the relationship between electrochemical processes and ionic dynamic underlying the formation of atomic-sized conductive filaments and corresponding electronic transport properties in the quantum regime still represents a challenge. In this work, we report on quantum conductance effects in single memristive Ag nanowires (NWs) through a combined experimental and simulation approach that combines advanced classical molecular dynamics (MD) algorithms and quantum transport simulations (DFT). This approach provides new insights on quantum conductance effects in memristive devices by unravelling the intrinsic relationship between electronic transport and atomic dynamic reconfiguration of the nanofilment, by shedding light on deviations from integer multiples of the fundamental quantum of conductance depending on peculiar dynamic trajectories of nanofilament reconfiguration and on conductance fluctuations relying on atomic rearrangement due to thermal fluctuations.In this work, Milano et al. reported on quantum conductance effects in memristive nanowires, unveiling the origin of deviations of conductance levels from integer multiples of the conductance quantum and analyzing conductance fluctuations over time of memristive devices
Thermal characterization and cost analysis of cement-based composite materials for thermochemical energy storage
No full textThe objective of this study is the synthesis and thermal characterization of cement-based composites for thermochemical energy storage (TES), focusing on three cement families: Portland Cement (PC), Calcium Aluminate Cement (CAC), and Calcium Sulfoaluminate Cement (CSA). We explore the potential of those composites in enhancing energy storage capabilities, while being cost-effective and robust. The research has involved composites with varying proportions of sepiolite to enhance porosity and reduce costs. An in-situ synthesis technique was conveniently employed allowing promising results on control over salt content. Water vapor-sorption analyses were conducted on six selected samples at two temperatures (30 °C and 50 °C) and across five relative humidity points. The Polanyi adsorption potential theory was employed to extend the analysis and model realistic operating cycles. We found that the top-performing composite exhibited an energy density of 85 MJ/m3 with a storage cost of 9.30 €/kWh, thus resulting comparable or superior to materials like Zeolite 13×/MgSO4 and silica gel/CaCl2, but lower than vermiculite/CaCl2 or LiCl. Nonetheless, the novel composites demonstrate lower costs and promising behavior with respect to important challenges as deliquescence or poor mass transport. The synthesized cement-based composites show significant potential in TES technology, though further optimization is still requited in terms of energy density and material cost. This research also suggests that cost reductions for CAC and CSA cements through scale economies and material mixing strategies, like combining CAC with PC might be feasible to further enhance the viability of these composites in TES applications
All-optical multilevel physical unclonable functions
Get PDFDisordered photonic structures are promising for the realization of physical unclonable functions—physical objects that can overcome the limitations of conventional digital security and can enable cryptographic protocols immune against attacks by future quantum computers. The physical configuration of traditional physical unclonable functions is either fixed or can only be permanently modified, allowing one token per device and limiting their practicality. Here we overcome this limitation by creating reconfigurable structures made by light-transformable polymers in which the physical structure of the unclonable function can be reconfigured reversibly. Our approach allows the simultaneous coexistence of multiple physical unclonable functions within one device. The physical transformation is done all-optically in a reversible and spatially controlled fashion, allowing the generation of more complex keys. At the same time, as a set of switchable individual physical unclonable functions, it enables the authentication of multiple clients and allows for the practical implementations of quantum secure authentication and nonlinear generators of cryptographic keys
Augmenting Metrology: A Framework for Interactive 3D Data Visualization and Contextual Analysis in Semiconductor Manufacturing
Get PDFThis work presents the first application of an Augmented Reality (AR) and Virtual Reality (VR) environment for the navigation and analysis of complex 3D metrology datasets in chip manufacturing and debugging. Our platform overcomes the limitations of traditional 2D data visualization by facilitating seamless access, navigation, and co-registration of multi-modal datasets acquired through tomographic scanning probe microscopy, 3D rendering software, and finite element simulations. Our goal is to demonstrate the functionality and advantages of such a platform for the analysis of complex 3D datasets, typically generated during the process of failure analysis (FA). First, we describe our platform with details for the access and navigation of existing datasets. Second, we offer the seamless presentation of various co-registered datasets acquired by a combination of tomographic scanning probe microscopy, multiple FA techniques, and 3D finite elements software simulations. Our first-person perspective enables the enhancement of data navigation for each of these tasks. For example, with the simultaneous observation of static 2D information about the sample under study e.g., graphic data stream (GDS) floorplan or scanning electron microscopy (SEM), while offering 3D interactive inspection of an inherently tomographic dataset
Evaluation of the cytotoxic and immunomodulatory effects of sonodynamic therapy in human pancreatic cancer spheroids
Get PDFSonodynamic therapy (SDT) exploits the energy generated by ultrasound (US) to activate sound-sensitive drugs (sonosensitizers), leading to the generation of reactive oxygen species (ROS) and cancer cell death. Two-dimensional (2D) and three-dimensional (3D) cultures of human pancreatic cancer BxPC-3 cells were chosen as the models with which to investigate the therapeutic effects of the US-activated sonosensitizer IR-780 as pancreatic cancer is still one of the most lethal types of cancer. The effects of SDT, including ROS production, cancer cell death and immunogenic cell death (ICD), were extensively investigated. When subjected to US, IR-780 triggered significant ROS production and caused cancer cell death after 24 h (p ≤ 0.01). Additionally, the activation of dendritic cells (DCs) led to an effective immune response against the cancer cells undergoing SDT-induced death. BxPC-3 spheroids were developed and studied extensively to validate the findings observed in 2D BxPC-3 cell cultures. An analysis of the pancreatic cancer spheroid section revealed significant SDT-induced cancer cell death after 48 h after the treatment (p ≤ 0.01), with this being accompanied by the presence of SDT-induced damage-associated molecular patterns (DAMPs), such as calreticulin (CRT) and high mobility group box 1 (HMGB1). In conclusion, the data obtained demonstrates the anticancer efficacy of SDT and its immunomodulatory potential via action as an ICD-inducer
Machine Learning Allowed Interpreting Toxicity of a Fe-Doped CuO NM Library Large Data Set─An Environmental In Vivo Case Study
Get PDFThe wide variation of nanomaterial (NM) characters (size, shape, and properties) and the related impacts on living organisms make it virtually impossible to assess their safety; the need for modeling has been urged for long. We here investigate the custom-designed 1-10% Fe-doped CuO NM library. Effects were assessed using the soil ecotoxicology model Enchytraeus crypticus (Oligochaeta) in the standard 21 days plus its extension (49 days). Results showed that 10þ-CuO was the most toxic (21 days reproduction EC50 = 650 mg NM/kg soil) and Fe3O4 NM was the least toxic (no effects up to 3200 mg NM/kg soil). All other NMs caused similar effects to E. crypticus (21 days reproduction EC50 ranging from 875 to 1923 mg NM/kg soil, with overlapping confidence intervals). Aiming to identify the key NM characteristics responsible for the toxicity, machine learning (ML) modeling was used to analyze the large data set [9 NMs, 68 descriptors, 6 concentrations, 2 exposure times (21 and 49 days), 2 endpoints (survival and reproduction)]. ML allowed us to separate experimental related parameters (e.g., zeta potential) from particle-specific descriptors (e.g., force vectors) for the best identification of important descriptors. We observed that concentration-dependent descriptors (environmental parameters, e.g., zeta potential) were the most important under standard test duration (21 day) but not for longer exposure (closer representation of real-world conditions). In the longer exposure (49 days), the particle-specific descriptors were more important than the concentration-dependent parameters. The longer-term exposure showed that the steepness of the concentration-response decreased with an increased Fe content in the NMs. Longer-term exposure should be a requirement in the hazard assessment of NMs in addition to the standard in OECD guidelines for chemicals. The progress toward ML analysis is desirable given its need for such large data sets and significant power to link NM descriptors to effects in animals. This is beyond the current univariate and concentration-response modeling analysis
