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Industrial Silicon Metal-Oxide-Semiconductor Spin Qubits as Quantum Sensors for Single-Molecule Magnet Qudits
No full textSingle-molecule magnets (SMMs) provide chemically engineered, long-lived spin states that are attractive as qudits and quantum memories, but fast, local readout at the single-molecule level remains experimentally challenging. This thesis examines whether industrial silicon metal-oxide-semiconductor (SiMOS) spin qubits can serve as quantum sensors for SMMs, and which advances are required to approach single-molecule sensitivity.
Industrial SiMOS quantum dots in enriched 28Si are operated as spin qubits with reliable spin-selective readout and coherence times of μs (Ramsey) and μs (Hahn echo), establishing them as a suitable platform for quantum magnetometry. Quantitative estimates of the dipolar coupling identify the qubit-molecule separation imposed by the industrial gate stack as the main limitation for single-molecule readout. To amplify the molecular signal into the detectable range for current SiMOS devices, an ensemble of the SMM terbium bis(phthalocyanine), TbPc, heavily diluted in a YPc matrix, is deposited on the chip surface. In this hybrid architecture, TbPc serves as a prototypical molecular qudit with long-lived spin states, while the SiMOS qubit provides fast, electrically controlled nanoscale readout. Using a compact rapid adiabatic passage during spin-selective tunnelling protocol, the qubit resonance can be followed in a robust and time-efficient manner across a 100 MHz window, enabling measurements of magnetic hysteresis, angular anisotropy, and slow relaxation of the TbPc ensemble, with relaxation times of (107 ± 2) min at 48 mK and (0.8 ± 0.3) min at 140 mK. These results provide the first demonstration of a hybrid SiMOS-SMM sensing architecture.
Beyond single-dot sensing, double quantum dot schemes based on singlet-triplet qubits and Pauli spin blockade are developed, enabling magnetic-field-gradient sensing and low-field measurements of the absolute Zeeman splitting. Finally, self-consistent Schrödinger-Poisson simulations of the industrial gate stack show that the qubit-surface separation can be reduced to well below 10 nm with realistic design modifications, and a first generation of such sensing-optimised devices is fabricated in the 300 mm industrial cleanrooms of imec, outlining a concrete pathway towards single-molecule sensitivity
A Waveform Design Practice Guided by Rheology for Achieving Satellite-Free Droplet Formation in 3D Inkjet Printing
No full textIn inkjet printing (IJ), achieving stable, satellite-free droplet formation is critical for good print quality. Traditional approaches rely on “printability windows” based on dimensionless numbers that relate material properties such as density, surface tension, and viscosity. However, these windows often fail to predict jetting behavior accurately, particularly for polymer-based inks, as they neglect non-Newtonian effects under the highly dynamic conditions of IJ, characterized by high shear rates, high frequency and short timescales. To address this issue, a systematic approach that correlates rheological characterisation of inks waveform design and droplet formation attributes was proposed. Three UV-curable acrylate inks with unknown formulation were systematically characterized in terms of complex viscosity, viscoelasticity, relaxation time, dynamic surface tension, oscillatory damping behavior, and density. Measurements were conducted across five temperatures using a highfrequency squeeze-flow rheometer, bubble tensiometer and densimeter. Cross, Hua & Rosen, Maxwell, Arrhenius, and Eötvös models were applied to extrapolate these properties to inkjet-relevant regimes and across several temperatures. Dropwatching studies were conducted with the inks and satellite formation was found to depend primarily on droplet velocity, with distinct regimes: <3 m/s (no satellite droplets occur) and 2.2–3.8 m/s (single satellite). The derived regression equation linking ink properties, droplet velocity and driving voltage accelerates the prediction of waveform parameters for single droplet satellite-free droplet formation directly from rheological data
@kit-data-manager/react-search-component v0.4.1
No full textAll-in-one component for rendering an elastic search UI for searching anything. Includes an interactive graph of related records and unique identifier resolving
Is the Gompertz family a good fit to your data?
No full textThat data follow a Gompertz distribution is a widely used assumption in diverse fields of applied sciences, e.g., in biology or when analysing survival times. Since misspecified models may lead to false conclusions, assessing the fit of the data to an underlying model is of central importance. We propose a new family of characterisation-based weighted -type tests of fit to the family of Gompertz distributions, hence tests for the composite hypothesis when the parameters are unknown. The characterisation is motivated by distributional transforms connected to Stein\u27s method of distributional approximation. We provide the limit null distribution of the test statistics in a Hilbert space setting and, since the limit distribution depends on the unknown parameters, we propose a parametric bootstrap procedure. Consistency of the testing procedure is shown. An extensive simulation study as well as applications to real data examples show practical benefits of the procedures: the first data set we analyse consists of lifetimes of fruitflies, the second has been synthetically generated from life tables for women born in Germany in 1948
Seamless Machine Integration in Smart Manufacturing: Utilizing OPC UA for Machinery with Skill-Based Engineering of Varying Granularity
No full textGrand Challenges and Opportunities in Stimulated Dynamic and Resonant Catalysis
No full textTraditional heterogeneous catalysis is constrained by kinetic and thermodynamic limits, such as the Sabatier principle and reaction equilibrium. Dynamic and resonant catalysts hold promise to overcome these limitations by actively oscillating a catalyst’s physical or electronic structure at the time scale of the catalytic cycle, allowing programmable control over reaction pathways, and leading to improved rate and selectivity. External stimuli such as temperature swing, mechanical strain, electric charge, and light can perturb catalyst surfaces in different ways, altering adsorbate coverage, binding energies, and transition states beyond what steady-state catalysis allows. This work surveys the current state of dynamic catalysis, introduces the concept of “stimulando” characterization for observing transient dynamics, and outlines key modeling, mechanistic, and benchmarking strategies to advance the field toward improved chemical transformation
Investigation of mass transfer and reaction characteristics in three-dimensional fully morphological channel-ridge type gas distribution zones for proton exchange membrane fuel cells
No full textA well-designed gas distribution zone (GDZ) ensures uniform distribution of reactant gases over the active area, improves the output performance of the cell, and enhances the water and thermal management in the proton exchange membrane fuel cell (PEMFC), thereby ensuring efficient and stable operation of large scale PEMFCs. This study focuses on the design of a forced-flow type GDZ. A three–dimensional, multiphase, non-isothermal numerical model was established. Using this model, the effects of the GDZ ridge-to-channel number ratio, secondary split-flow, and oblique channel ridge length on cell performance were systematically investigated. The results demonstrate that, in comparison to the conventional empty-chamber gas distribution zone, a GDZ with a ridge-to-channel number ratio of 1:1 improves the uniformity of oxygen distribution in the middle plane of the gas diffusion layer by 3.401%. This enhancement concurrently suppresses concentration polarization effectively, thereby contributing to a 2.29% increase in the peak power density of the PEMFC. Further research revealed that secondary flow channel ridges only yielded suboptimal performance due to mass transfer limitation and liquid water accumulation. Adopting moderately long oblique ridges is crucial, as it strikes a balance between reactant gas distribution, pressure drop and water management
AI Ethics and Philosophy: A Critical Need for Inclusive, Equitable, and Sustainable AI Framework
No full textTowards a Child-Appropriate LLM for Child–Robot Conversation
No full textLarge Language Models (LLMs) hold significant promise for enhancing Child–Robot Interaction (CRI), offering advanced conversational skills and adaptability to the diverse abilities, requests and needs of young children. Little attention, however, has been paid to evaluating the age and developmental appropriateness of LLMs. This paper brings together experts in psychology, social robotics and LLMs to define metrics for the validation of LLMs for child–robot interaction