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At least 1,333 records · Page 74

Group-III quantum defects in diamond are stable spin-1 color centers

Color centers in diamond have emerged as leading solid-state “artificial atoms” for a range of quantum technologies, from quantum sensing to quantum networks. Concerted research activities are now underway to identify new color centers that combine stable spin and optical properties of the nitrogen vacancy (NV – ) with the spectral stability of the silicon vacancy (SiV – ) centers in diamond, with recent research identifying other group-IV color centers with superior properties. In this paper, we investigate a class of diamond quantum emitters from first principles, the group-III color centers, which we show to be thermodynamically stable in a spin-1, electric-field-insensitive structure. Further, from ab initio electronic structure methods, we characterize the product Jahn-Teller (pJT) effect present in the excited-state manifold of these group-III color centers, where we capture symmetry-breaking distortions associated with strong electron-phonon coupling. These predictions can guide experimental identification of group-III vacancy centers and their use in applications in quantum information science and technology.

36 MATERIALS SCIENCE↗

Mass spectrometry-based proteomics for analysis of hydrophilic phosphopeptides

Protein phosphorylation is a critical post-translational modification (PTM), with cell signaling networks being tightly regulated by protein phosphorylation. Despite recent technological advances in reversed-phase liquid chromatography (RPLC)-mass spectrometry (MS)-based proteomics, comprehensive phosphoproteomic coverage in complex biological systems remains challenging, especially for hydrophilic phosphopeptides that often have multiple phosphorylation sites. Herein we describe an MS-based phosphoproteomics protocol for effective quantitative analysis of hydrophilic phosphopeptides. This protocol was built upon a simple tandem mass tag (TMT)-labeling method for significantly increasing peptide hydrophobicity, thus effectively enhancing RPLC-MS analysis of hydrophilic peptides. Through phosphoproteomic analyses of MCF7 cells, this method was demonstrated to greatly increase the number of identified hydrophilic phosphopeptides and improve MS signal detection. With the TMT labeling method, we were able to identify a previously unreported phosphopeptide from the G protein-coupled receptor (GPCR) CXCR3, QPpSSSR, which is thought to be important in regulating receptor signaling. This protocol is easy to adopt and implement, and thus should have broad utility for effective RPLC-MS analysis of the hydrophilic phosphoproteome as well as other highly hydrophilic analytes.

Hydrophilic phosphopeptide, Phosphoproteomics, TMT↗

Complete Phase Transformation of Ir Nanowire Network into Defect-Rich Oxide Catalyst for High-Performance PEM Water Electrolysis

Iridium-based catalysts remain the most reliable option for the oxygen evolution reaction (OER) in proton exchange membrane water electrolyzers (PEMWEs). However, their high cost and limited performance represent critical barriers to the commercialization of this green hydrogen production technology. Herein, we report the creation of a metallic Ir nanowire network (IrNWN), which exhibits superior OER performance through its in-situ transition into an oxide structure with high intrinsic activity. At a low loading of 0.25 mgIr/cm2 in PEMWEs, IrNWN achieved a current density of 3.13 A/cm2 at a cell voltage of 1.8 V, outperforming the commercial Ir-based catalyst and surpassing the Department of Energy (DOE) 2026 technical target. Moreover, the high activity of IrNWN was maintained for 900 hours in a durability test at 2 A/cm2, showing a low degradation rate of 0.042 mV/hour. Structural analysis of the electrochemically oxidized IrNWN revealed the presence of mixed Ir oxidation states and a high density of surface terminal oxygen groups (μ1-O), which contributed to a reduced energy barrier for the rate-determining O-O coupling step.

oxygen evolution reaction↗

A multi-dimensional parametric study of variability in multi-phase flow dynamics during geologic CO 2 sequestration accelerated with machine learning

Successful geologic CO 2 storage projects depend on numerical simulations to predict reservoir performance during site selection, injection verification, and post-injection monitoring phases of the project. These numerical simulations solve non-linear sets of coupled partial differential equations, while accounting for multi-phase fluid dynamics on the basis of constitutive equations that are embedded into the solution scheme. As a consequence, individual simulations often require tens to hundreds of hours to complete on high-performance computing clusters. Moreover, laboratory experiments reveal that parametric functions for capillary pressure and relative permeability exhibit substantial variability, even within the same rock type. This combination of computational expense and wide-ranging parametric variability means that there remains substantial uncertainty in the behavior of multi-phase CO 2 -water systems, particularly in the context of feedbacks between relative permeability and capillary pressure. To bridge this knowledge gap, here we develop a novel workflow that utilizes physics-based numerical simulation to train an artificial neural network (ANN) emulator for interrogating the multivariate parameter space that governs both capillary pressure and relative permeability. With this approach, the ANN is trained to emulate both fluid pressure distribution and CO 2 saturation, which are then interrogated quantitatively to generate parametric response surface mappings with high-fidelity resolution. Results from this study initially show that capillary entry pressure is the dominant control on both CO 2 plume geometry and fluid pressure propagation when considering the combined effects of capillary pressure and relative permeability, particularly when phase interference is low and residual CO 2 saturation is high. Moreover, the ANN emulator provides tremendous computational speed-up by computing 2691 individual simulations in several minutes; whereas, the same simulation ensemble would have required ~3 years of simulation time using only physics-based simulation methods (25,000 times speed up).

58 GEOSCIENCES↗

Enthalpy of Formation of C 2 H 2 O 4 (Oxalic Acid) from High-Level Calculations and the Active Thermochemical Tables Approach

High-level coupled cluster calculations obtained with the Feller–Peterson–Dixon (FPD) approach and new data from the most recent version of the Active Thermochemical Tables (ATcT) are used to reassess the enthalpy of formation of gas-phase C 2 H 2 O 4 (oxalic acid). The theoretical value was further calibrated by comparing FPD and ATcT gas-phase enthalpies of formation for H 2 CO (formaldehyde) and the two low-lying conformations of C 2 H 4 O 2 ( syn and anti acetic acid). The FPD approach produces a theoretical enthalpy of formation of gas-phase oxalic acid of –732.2 ± 4.0 kJ/mol at 298.15 K (–721.8 ± 4.0 kJ/mol at 0 K). An independently obtained ATcT value, based on reassessing the existent experimental determinations and expanding the resulting thermochemical network with select mid-level composite theoretical results, disagrees with several earlier recommendations that were based solely on experimental determinations but is in excellent accord with the current FPD value. The inclusion of the latter in the most recent ATcT thermochemical network produces a further refined value for the gas-phase enthalpy of formation, –731.6 ± 1.2 kJ/mol at 298.15 K (–721.0 ± 1.2 kJ/mol at 0 K). In conclusion, the condensed-phase ATcT enthalpy of formation of oxalic acid is –829.7 ± 0.5 kJ/mol, and the resulting sublimation enthalpy is 98.1 ± 1.3 kJ/mol, both at 298.15 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

pH-Dependent Assembly of Porphyrin–Silica Nanocomposites and Their Application in Targeted Photodynamic Therapy

Structurally controlled nanoparticles, such as core–shell nanocomposite particles by combining two or more compositions, possess enhanced or new functionalities that benefited from the synergistic coupling of the two components. In this paper, we report new nanocomposite particles with self-assembled porphyrin arrays as the core surrounded by amorphous silica as the shell. The synthesis of such nanocomposite nanoparticles was conducted through a combined surfactant micelle confined self-assembly and silicate sol–gel process using optically active porphyrin as a functional building block. Depending on kinetic conditions, these particles exhibit structure and function at multiple length scales and locations. At the molecular scale, the porphyrins as the building blocks provide well-defined macromolecular structures for noncovalent self-assembly and unique chemistry for high-yield generation of singlet oxygen for photodynamic therapy (PDT). On the nanoscale, controlled noncovalent interactions of the porphyrin building block result in an extensive self-assembled porphyrin network that enables efficient energy transfer and impressive fluorescence for cell labeling, evidenced by absorption and photoluminescence spectra. The thin silicate shell on the nanoparticle surface allows easy functionalization, and the resultant targeting porphyrin-silica nanocomposites can selectively destroy tumor cells upon receiving light irradiation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Quantum Memristors in Frequency-Entangled Optical Fields

A quantum memristor is a passive resistive circuit element with memory, engineered in a given quantum platform. It can be represented by a quantum system coupled to a dissipative environment, in which a system–bath coupling is mediated through a weak measurement scheme and classical feedback on the system. In quantum photonics, such a device can be designed from a beam splitter with tunable reflectivity, which is modified depending on the results of measurements in one of the outgoing beams. Here, we show that a similar implementation can be achieved with frequency-entangled optical fields and a frequency mixer that, working similarly to a beam splitter, produces state superpositions. We show that the characteristic hysteretic behavior of memristors can be reproduced when analyzing the response of the system with respect to the control, for different experimentally attainable states. Since memory effects in memristors can be exploited for classical and neuromorphic computation, the results presented in this work could be a building block for constructing quantum neural networks in quantum photonics, when scaling up.

36 MATERIALS SCIENCE↗

Device Access Abstractions for Resilient Information Architecture Platform for Smart Grid

An open application platform distributes the intelligence and control capability to local endpoints (or nodes) reducing total network traffic, improving speed of local actions by avoiding latency, and improving reliability by reducing dependencies on numerous devices and communication interfaces. The platform must be multi-tasking and able to host multiple applications running simultaneously. Given such a system, the core functions of power grid control systems include grid state determination, low level control, fault intelligence and reconfiguration, outage intelligence, power quality measurement, remote asset monitoring, configuration management, power and energy management (including local distributed energy resources, such as wind, solar and energy storage) can be eventually distributed. However, making this move requires extensive regression testing of systems to prove out new technologies, such as phasor measurement units (PMU). Additionally, as the complexity of the systems increase with the inclusion of new functionality (especially at the distribution and consumer levels), hidden coupling issues becomes a challenge with possible N-way interactions known and not known by device and application developers. Therefore, it is very important to provide core abstractions that ensure uniform operational semantics across such interactions. Here in this paper, we describe the pattern for abstracting device interactions we have developed for the RIAPS platform in the context of a microgrid control application we have developed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

All‐Printed Ultrahigh‐Responsivity MoS 2 Nanosheet Photodetectors Enabled by Megasonic Exfoliation

Abstract Printed 2D materials, derived from solution‐processed inks, offer scalable and cost‐effective routes to mechanically flexible optoelectronics. With micrometer‐scale control and broad processing latitude, aerosol‐jet printing (AJP) is of particular interest for all‐printed circuits and systems. Here, AJP is utilized to achieve ultrahigh‐responsivity photodetectors consisting of well‐aligned, percolating networks of semiconducting MoS 2 nanosheets and graphene electrodes on flexible polyimide substrates. Ultrathin (≈1.2 nm thick) and high‐aspect‐ratio (≈1 μm lateral size) MoS 2 nanosheets are obtained by electrochemical intercalation followed by megasonic atomization during AJP, which not only aerosolizes the inks but also further exfoliates the nanosheets. The incorporation of the high‐boiling‐point solvent terpineol into the MoS 2 ink is critical for achieving a highly aligned and flat thin‐film morphology following AJP as confirmed by grazing‐incidence wide‐angle X‐ray scattering and atomic force microscopy. Following AJP, curing is achieved with photonic annealing, which yields quasi‐ohmic contacts and photoactive channels with responsivities exceeding 10 3 A W −1 that outperform previously reported all‐printed visible‐light photodetectors by over three orders of magnitude. Megasonic exfoliation coupled with properly designed AJP ink formulations enables the superlative optoelectronic properties of ultrathin MoS 2 nanosheets to be preserved and exploited for the scalable additive manufacturing of mechanically flexible optoelectronics.

2D materials↗

A Variational Autoencoder Model Toward Molecular Structure Representation Learning of Fuels

Here, in this work, a Variational Autoencoder (VAE)-based data-driven modeling framework is developed with the overarching goal of enabling fuel design. The VAE model is trained on a large dataset with several chemical species to learn a compressed latent space molecular representation. Chemical structure in the form of Simplified Molecular Input Line Entry System (SMILES) string is fed as input, encoded into the VAE latent space, and decoded back to the SMILES string using Long Short-Term Memory (LSTM) networks. Complexities of the VAE training loss function are thoroughly examined by varying the weightage (beta (𝜷) parameter) of the latent space regularization term, thereby assessing the balance between reconstruction accuracy and validity, and focusing on both accurate molecular structure reconstruction and latent space consistency. Two different strategies for 𝜷 variation are evaluated: linear annealing and cyclic annealing. In addition, the impact of total correlation adjustment and hierarchical priors is also studied with regard to the balance between reconstruction fidelity and latent space regularization, and potential issues such as posterior collapse, over-regularization, and poor disentanglement of latent variables. Overall, the best performance of the model is achieved with hierarchical priors and incrementally increasing 𝜷 from 0 to a threshold value of 0.25 over 75 epochs. The generative VAE model can be readily coupled with Quantitative Structure–Property Relationship (QSPR) analysis to develop an integrated end-to-end framework for fuel-property prediction and molecular design of novel promising fuels.

fuel design↗

Discovery of novel bacterial queuine salvage enzymes and pathways in human pathogens

Queuosine (Q) is a complex tRNA modification widespread in eukaryotes and bacteria that contributes to the efficiency and accuracy of protein synthesis. Eukaryotes are not capable of Q synthesis and rely on salvage of the queuine base (q) as a Q precursor. While many bacteria are capable of Q de novo synthesis, salvage of the prokaryotic Q precursors preQ 0 and preQ 1 also occurs. With the exception of Escherichia coli YhhQ, shown to transport preQ 0 and preQ 1 , the enzymes and transporters involved in Q salvage and recycling have not been well described. In this work, we discovered and characterized 2 Q salvage pathways present in many pathogenic and commensal bacteria. The first, found in the intracellular pathogen Chlamydia trachomatis , uses YhhQ and tRNA guanine transglycosylase (TGT) homologs that have changed substrate specificities to directly salvage q, mimicking the eukaryotic pathway. The second, found in bacteria from the gut flora such as Clostridioides difficile , salvages preQ 1 from q through an unprecedented reaction catalyzed by a newly defined subgroup of the radical-SAM enzyme family. The source of q can be external through transport by members of the energy-coupling factor (ECF) family or internal through hydrolysis of Q by a dedicated nucleosidase. This work reinforces the concept that hosts and members of their associated microbiota compete for the salvage of Q precursors micronutrients.

59 BASIC BIOLOGICAL SCIENCES↗

Preferential quenching of 5 d antiferromagnetic order in Sr 3 (Ir 1– x Mn x ) 2 O 7

The breakdown of J eff = 1/2 antiferromagnetism in the limit of strong disorder is studied in Sr 3 (Ir 1– x Mn x ) 2 O 7 . Upon Mn-substitution, antiferromagnetic ordering of the Ir cations becomes increasingly two-dimensional, resulting in the complete suppression of longrange Ir magnetic order above x ≈ 0.25. Long-range antiferromagnetism however persists on the Mn sites to higher Mn concentrations ( x > 0.25) and is necessarily mediated via a random network of majority Ir sites. Our data suggest a shift in the Mn valence from Mn 4+ to Mn 3+ at intermediate doping levels, which in turn generates nonmagnetic Ir 5+ sites and suppresses long-range order within the Ir network. Here, the collapse of long-range J eff = 1/2 antiferromagnetism and the survival of percolating antiferromagnetic order on Mn-sites demonstrates a complex 3 d –5 d exchange process that surprisingly enables minority Mn spins to order far below the conventional percolation threshold for a bilayer square lattice.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Application of the variational autoencoder to detect the critical points of the anisotropic Ising model

We generalize the previous study on the application of variational autoencoders to the two-dimensional Ising model to a system with anisotropy. Due to the self-duality property of the system, the critical points can be located exactly for the entire range of anisotropic coupling. This presents an excellent test bed for the validity of using a variational autoencoder to characterize an anisotropic classical model. Furthermore, we reproduce the phase diagram for a wide range of anisotropic couplings and temperatures via a variational autoencoder without the explicit construction of an order parameter. Considering that the partition function of ($d$ + 1)-dimensional anisotropic models can be mapped to that of the $d$-dimensional quantum spin models, the present study provides numerical evidence that a variational autoencoder can be applied to analyze quantum systems via the quantum Monte Carlo method.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Discovery of Probabilistic Dirichlet-to-Neumann Maps on Graphs

Dirichlet-to-Neumann maps enable the coupling of multiphysics simulations across computational subdomains by ensuring continuity of state variables and fluxes at artificial interfaces. We present a novel method for learning Dirichlet-to-Neumann maps on graphs using Gaussian processes, specifically for problems where the data obey a conservation law arising from an underlying partial differential equation. Our approach combines discrete exterior calculus and nonlinear optimal recovery to infer relationships between vertex and edge values. This framework yields data-driven predictions with uncertainty quantification across the entire graph, even when observations are limited to a subset of vertices and edges. By minimizing the reproducing kernel Hilbert space norm while penalizing kernel complexity through maximum likelihood estimation, our method ensures that the resulting surrogate strictly enforces conservation laws without overfitting. We demonstrate our method on two representative applications: subsurface flow in fracture networks and arterial blood flow. Finally, the results demonstrate that the method maintains high accuracy and well-calibrated uncertainty estimates even under severe data scarcity, highlighting its potential for scientific applications where limited data and reliable uncertainty quantification are critical.

Dirichlet-to-Neumann map↗

Surrogate models for plasma displacement and current in 3D perturbed magnetohydrodynamic equilibria in tokamaks

Abstract A numerical database of over one thousand perturbed three-dimensional (3D) equilibria has been generated, constructed based on the MARS-F (Liu et al 2000 Phys. Plasmas 7 3681) computed plasma response to the externally applied 3D field sources in multiple tokamak devices. Perturbed 3D equilibria with the n = 1–4 ( n is the toroidal mode number) toroidal periodicity are computed. Surrogate models are created for the computed perturbed 3D equilibrium utilizing model order reduction (MOR) techniques. In particular, retaining the first few eigenstates from the singular value decomposition (SVD) of the data is found to produce reasonably accurate MOR-representations for the key perturbed quantities, such as the perturbed parallel plasma current density and the plasma radial displacement. SVD also helps to reveal the core versus edge plasma response to the applied 3D field. For the database covering the conventional aspect ratio devices, about 95% of data can be represented by the truncated SVD-series with inclusion of only the first five eigenstates, achieving a relative error (RE) below 20%. The MOR-data is further utilized to train neural networks (NNs) to enable fast reconstruction of perturbed 3D equilibria, based on the two-dimensional equilibrium input and the 3D source field. The best NN-training is achieved for the MOR-data obtained with a global SVD approach, where the full set of samples used for NN training and testing are stretched and form a large matrix which is then subject to SVD. The fully connected multi-layer perceptron, with one or two hidden layers, can be trained to predict the MOR-data with less than 10% RE. As a key insight, a better strategy is to train separate NNs for the plasma response fields with different toroidal mode numbers. It is also better to apply MOR and to subsequently train NNs separately for conventional and low aspect ratio devices, due to enhanced toroidal coupling of Fourier spectra in the plasma response in the latter case.

3D equilibrium↗

Feature Extraction for Pipeline Defects Inspection Based Upon Distributed Acoustic Fiber Optic Sensing Data

Fiber-optic distributed acoustic sensing (DAS) is becoming an increasingly important tool for real-time monitoring of energy and civil infrastructure structural health such as pipelines. We present a systematic theoretical study of the potential for DAS to be directly coupled with guided ultrasonic waves typically used in conventional acoustic non-destructive evaluation (NDE) methods for real-time pipeline health monitoring. We are referring to this innovative new NDE technique as ultrasonic guided wave and optical fiber sensor fusion. In the practical application of DAS coupled with guided ultrasonic waves, the structural design of (1) the specific guided waves excited, (2) the physical installation of the acoustic transducers and the fiber optic sensors, and (3) the functional performance specifications (gauge length, sensitivity, Etc.) of fiber optic DAS have an important influence on overall capabilities of the monitoring system. Meanwhile, physics-based analysis of acoustic waves is still a challenge due to the complex nature of the Lamb wave when it propagates, scatters, and disperses in the presence of structural defects. In this work, we simulate carbon steel pipes relevant for oil and gas pipeline applications with diameters of approximately 6-12” and wall thickness of 0.5” as the objects to be monitored. By establishing and implementing these capabilities, we seek to pursue an in-depth study on structural parameter optimization of DAS network, measurement range, and signal processing with an ultimate goal of increasing the sensitivity and efficacy of DAS to defect identification for various modes of corrosion expected in practice. To study the characteristics of scattered acoustic waves and performance of DAS for defect identification, we simulated the response of DAS for multiple pipe structures, defect types, and DAS sensor network configuration using finite element software Ansys, then the properties of signal response are extracted to construct defect-sensitive features. The raw data simulated, and the associated features extracted can ultimately be utilized as annotated training data to benchmark various designs for DAS applications, guided acoustic excitation sources, and learning model parameters to enhance early detection of potentially problematic defects.

Pipeline Defects Inspection, Fiber-optic sensors, ↗

Collaborative Research: Quantifying Climate Feedbacks of the Terrestrial Biosphere under Thawing Permafrost Conditions in the Arctic

Our overall goal in this research was to quantify the potential for threshold changes in natural emission rates of trace gases, particularly methane and carbon dioxide, from pan-arctic terrestrial systems under the spectrum of anthropogenically-forced climate warming, and the conditions under which these emissions provide a strong feedback mechanism to global climate warming. This goal was motivated under the premise that polar amplification of global climate warming will induce widespread thaw and degradation of the permafrost, and would thus cause substantial changes to the landscape of wetlands and lakes, especially thermokarst (thaw) lakes, across the Arctic. Through a suite of numerical experiments that encapsulate the fundamental processes governing methane emissions and carbon exchanges – as well as their coupling to the global climate system - we tested the following hypothesis in the proposed research: There exists a climate warming threshold beyond which permafrost degradation becomes widespread and stimulates large increases in methane emissions (via thermokarst lakes and poorly-drained wetland areas upon thawing permafrost along with microbial metabolic responses to higher temperatures) and increases in carbon dioxide emissions from well-drained areas. Besides changes in biogeochemistry, this threshold will also influence global energy dynamics through effects on surface albedo, evapotranspiration and water vapor. These changes would outweigh any increased uptake of carbon (e.g. from peatlands and higher plant photosynthesis) and would result in a strong, positive feedback to global climate warming. In collaboration with our Purdue and MIT colleagues, we have attempted to quantify global climate warming effects on land-atmosphere interactions, land-river network interactions, permafrost degradation, vegetation shifts, and land use influence water, carbon, and nitrogen fluxes to and from terrestrial ecosystems in the pan-arctic along with their uncertainties. Based on our study results along with a review of observed and projected climate changes in Northern Eurasia by others, we have also outlined a more integrated modelling approach that may be developed and applied in future studies to better capture the influence of earth system feedbacks and human activities on the evolution of climate change effects over time. Specifically, we have examined: 1) how evapotranspiration and water availability have been changing in Northern Eurasia and may change in the future including the impact of forcing uncertainties (Liu et al., 2013, 2014, 2015); 2) how soil consumption of atmospheric methane across the globe have been influenced and may be influenced by climate change and nitrogen deposition during the 20th and 21st centuries (Zhuang et al., 2013); 3) how wetland inundation extent influences net CO2 and CH4 fluxes from northern high latitudes (Zhuang et al., 2015); 4) the relative effects of various environmental factors (including permafrost degradation) on terrestrial dissolved organic carbon (DOC) loading of river networks across the pan-Arctic and how they have changed over the 20th century (Kicklighter et al., 2013); 5) the impacts of recent and future permafrost thaw on land-atmosphere greenhouse gas exchange across the pan-Arctic (Gao et al., 2012, 2013; Hayes et al., 2014; Kicklighter et al. 2015a, 2018); 6) how climate-induced vegetation shifts may affect carbon fluxes and future land use in Northern Eurasia (Jiang et al., 2012, 2016; Kicklighter et al., 2014a) and the globe (Zhuang et al. 2015b); 7) the relative importance of legacies from past land use, future land-use change and climate change on projections of terrestrial carbon fluxes (Monier et al., 2015; Kicklighter et al., 2016); and 8) how the effects of earth system feedbacks and human activities can be better incorporated in assessments of climate change impacts (Monier et al., 2017; Groisman et al., 2018).

54 ENVIRONMENTAL SCIENCES↗

Multimode Storage of Quantum Microwave Fields in Electron Spins over 100 ms

In this work, we report long coherence times (up to 300 ms) for near-surface bismuth donor electron spins in silicon coupled to a superconducting microresonator, biased at a clock transition. This enables us to demonstrate the partial absorption of a train of weak microwave fields in the spin ensemble, their storage for 100 ms, and their retrieval, using a Hahn-echo-like protocol. Phase coherence and quantum statistics are preserved in the storage.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗