Electron Reconstruction Research Articles

General descriptors for glycerol oxidation to glyceric acid over PtM3‐derived bimetallic catalysts in base‐free medium

AbstractExploring efficient Pt‐based catalysts for the selective oxidation of polyol is still a challenge due to the lack of a mechanism‐driven approach. Here, low‐Pt content PdM3 were systematically investigated for glycerol oxidation to glyceric acid (GLYA) by density functional theory (DFT) linear synchronous transit and quadratic synchronous transit (LST/QST) assisted descriptor‐based micro‐kinetic modeling. Results revealed that the PdM3 surface induces the dissociation of O2 and H2O to form a polarized PdM3‐OH* surface, participating in the subsequent adsorption and activation of glycerol and oxygen‐containing intermediates. Moreover, the binding energy of O and H on the PdM3 could be labeled as descriptors describing catalytic selectivity and activity. On this basis, a strong electron reconstruction effect described by EO (−4.5 to −3.0 eV) and appropriate dehydrogenation ability described by EH (−3.5 to −2.5 eV) contribute to the improvement of catalytic performance for the selective oxidation of glycerol to GLYA. This study may give insights into the rational design of high‐efficient Pt‐based catalysts for polyol oxidation.

A Kalman filter for track reconstruction in very large time projection chambers

This study introduces a Kalman Filter tailored for homogeneous gas Time Projection Chambers (TPCs), adapted from the algorithm utilized by the ALICE experiment. In order to describe semi-circular paths in the plane perpendicular to the magnetic field, we introduce a novel mirror rotation technique into the Kalman Filter algorithm, enabling effective tracking of trajectories of varying lengths, including those with multiple circular paths within the detector, also known as “loopers”. Demonstrated relative improvements of up to 80% in electron momentum resolution and up to 50% in muon and pion momentum resolution underscore the significance of this enhancement. Significant improvements in the reconstruction efficiency for relatively short low momentum “looper” tracks are also shown. Such advancements hold promise not only for the future of the ALICE TPC but also for neutrino high-pressure gas TPCs, where loopers become significant owing to the randomness of production points and their relatively low energies in neutrino interactions. In particular, an improvement in low energy electron reconstruction, for which the production of “looping” tracks is likely and the impact of the new algorithm is directly demonstrated, could significantly impact the quality of flux determination, which in accelerator neutrino experiments relies on the measurement of νe electron scatterings.

Heteroatom Engineering in Earth-Abundant Cobalt Electrocatalyst for Energy-Saving Hydrogen Evolution Coupling with Urea Oxidation.

The development of multifunctional electrocatalysts with high performance for electrocatalyzing urea oxidation-assisted water splitting is of great significance for energy-saving hydrogen production. In this work, we demonstrate a novel heteroatom engineering strategy for development of B-doped Co as a multifunctional electrocatalyst for the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and urea oxidation reaction (UOR). Density functional theory (DFT) results suggest that a B dopant can efficiently adjust the electron reconstruction of the exposure of Co sites nearby and facilitate electron transfer, resulting in an optimal d-band center along with a lower Gibbs free energy barrier. Ultimately, the obtained B-Co exhibits pH-universal HER properties in various electrolytes. A highly efficient HER performance with overpotentials as low as 27, 163, and 430 mV to -10, -100, and -500 mA cm-2 in 1.0 M KOH, respectively, is observed for the B-Co electrode. More importantly, the UOR-assisted electrolyzer only requires a voltage input of 1.55 V to produce the current densities of 50 mA cm-2, resulting in a 200 mV saving-energy potential compared to water electrolysis, demonstrating its high efficiency of hydrogen production in industrial applications.

Insights into the removal of sulfamethazine and sulfonamide-resistant bacteria from wastewater by Fe-Mn spinel oxide modified cow manure biochar activated peroxymonosulfate: A nonradical pathway regulated by enhanced adsorption and 3d orbital electron reconstruction

Effectively regulating nonradical pathways is crucial for adapting to complex water environments in sulfate radical-based advanced oxidation processes (SR-AOPs). This study prepared manganese ferrite-modified cow-manure biochar composites (BC-FM) as peroxymonosulfate activators to remove sulfamethazine (SMT) and sulfonamide-resistant bacteria (SA-ARB) from wastewater. The extensive conjugated graphitic structure in biochar induced Fe 3d orbital electron reconstruction, promoting empty orbitals formation and enhancing peroxymonosulfate inner-sphere complexation. This facilitated 100 % electron-transfer-dominated nonradical regulation. BC-FM exhibited remarkable catalytic performance, with apparent rate constant of 0.170 min−1 for SMT, inactivating ∼8.6 log of SA-ARB, and effectively blocking horizontal transfer of antibiotic resistance genes in both single and complex pollution systems (SMT/SA-ARB). Moreover, ion leaching experiments, cycling tests, real-water experiments, fluidized-bed and fixed-bed experiments demonstrated BC-FM’s potential for prolonged utilization and practical implementation. This study offers new insights into designing high-performance, environment-friendly bimetal catalysts and provides a basis for remediating organic contaminants with SR-AOPs in livestock wastewater.

Enhanced Fenton-like reactions via interface electron reconstruction in low-crystallinity Fe[sbnd]Co bimetallic metal–organic frameworks: Bidirectional control of Fe (III) and Co (II) sites

In this study, the activity and stability of Fenton-like reactions are enhanced by constructing a low-crystallinity FeCo bimetallic metal–organic framework (FeCox-BDC (BDC denotes as terephthalic acid)) through interface electron reconstruction. However, the specific origins and mechanisms of their enhanced activity, particularly in Fenton-like reactions, remains unclear. Systematic analysis revealed that the isomorphic substitution of Co (II) reduces the coordination number and d-electron count at local Fe (III) sites, shifting the d-band centers (−1.59 eV) closer to the Fermi level. Additionally, Co 3d-orbitals can accept electrons, improving the occupation of antibonding orbitals. Notably, Fe (III) and Co (II) sites exhibit a synergistic effect: Fe (III) sites strongly adsorbed the Oα point of the peroxy bond (lOαOβ), while Co (II) sites efficiently activated Oβ. Within 5 min, FeCo1/3-BDC achieved a 98 % reduction in Rhodamine-B (RhB), surpassing Fe-BDC by a factor of 76 and homogeneous Fenton catalytic systems (Co (II)/peroxymonosulfate (PMS) and Fe (III)/Co (II)/PMS). This work provides a profound understanding of interface electron reconstruction, offering valuable insights into guiding Fenton-like mechanisms.

Boosting nitrogen photofixation through interface electron reconstruction using mixed-valent copper as mediator

Solar energy as the inexhaustible and pollution-free renewable energy is widely accepted. Photocatalysis is an efficient green process to store solar energy in the form of chemical energy or utilize it for NH3 production or water treatment reactions. Herein, CeO2–CeCO3OH all-solid-state heterojunctions with Cu2O/Cu Schottky barrier medium are assembled firstly, and the remarkable photo-catalytic performances are obtained. The heterojunction structure can promote the separation efficiencies of photo-induced electron-hole pairs. Otherwise, the as-prepared catalysts also have more visible light absorption capacities and photo-generated oxygen vacancies. In this photocatalytic system, the NH3 yield rate can reach above 597 or 992 μmol·gcal−1·h−1 under the vis-light or full-spectrum lighting without any sacrificial additives, and increase at least 3 times compared to the initial materials. The AQE at 420 nm also reaches more than 0.8 %. Furthermore, the RhB degradation efficiency for the modified sample is approximately 90 %, and the kinetic constant significantly exceeds that of initial sample by 2.7 times. It is the photo-introduced h+ and·O2− species working together to further improve the dye degradation process. This research provides a new insight for the fabrication strategy of photo-catalytic N2 fixation materials.

Surface Electron Reconstruction of Catalyst Through Alloying Strategy for Accelerating Sulfur Conversion in Lithium‐Sulfur Batteries

AbstractAlloy catalyst is considered to be an important strategy to solve the shuttle effect and sluggish kinetics of lithium‐sulfur batteries (LSBs). However, the effect of the electronic structure of the alloy catalyst on the sulfur conversion process has not been effectively analyzed. In this paper, based on alloying strategy, the electronic structure of such a FeCoNi catalyst is regulated and optimized, and the sulfur adsorption configuration and catalytic conversion process are defined. The in situ Raman spectroscopy and the density functional theory (DFT) are employed to deeply understand the catalytic mechanism of such a sulfur conversion. A cell with FeCoNi modified separator delivers an ultra‐low capacity attenuation of 0.056% per cycle over 1000 cycles at 3 C. The outstanding anti‐self‐discharge performance of 8.1% over 7 days is also achieved. Furthermore, the obtained cell with a high sulfur loading of 9.7 mg cm−2 and lean electrolyte of 5.6 µL mgs−1 exhibits 81% capacity retention after 100 cycles, providing a research prospect for the practical application of lithium‐sulfur batteries.

Electron and photon efficiencies in LHC Run 2 with the ATLAS experiment

Precision measurements of electron reconstruction, identification, and isolation efficiencies and photon identification efficiencies are presented. They use the full Run 2 data sample collected by the ATLAS experiment in pp collisions at a centre-of-mass energy of 13 TeV during the years 2015–2018, corresponding to an integrated luminosity of 139 fb−1. The measured electron identification efficiencies have uncertainties that are around 30%–50% smaller than the previous Run 2 results due to an improved methodology and the inclusion of more data. A better pile-up subtraction method leads to electron isolation efficiencies that are more independent of the amount of pile-up activity. Updated photon identification efficiencies are also presented, using the full Run 2 data. When compared to the previous measurement, a 30%–40% smaller uncertainty is observed on the photon identification efficiencies, thanks to the increased amount of available data.

Development of particle flow algorithms based on Neural Network techniques for the IDEA calorimeter at future colliders

IDEA (Innovative Detector for an Electron-positron Accelerator) is an innovative general-purpose detector concept, designed to study electron-positron collisions at future e+e− circular colliders. The detector will be equipped with a dual read-out calorimeter able to measure separately the hadronic component and the electromagnetic component of the showers initiated by the impinging hadrons. Particle flow algorithms (PFAs) have become the paradigm of detector design for the high energy frontier and this work focuses on a project to build a particle flow algorithm for the IDEA detector using Machine Learning (ML) techniques. ML is used for particle reconstruction and identification profiting of the high granularity of the fiber-based dual-readout calorimeter. Neural Networks (NN) are built for electron reconstruction inside the calorimeter. The performance of several NN architectures is shown, with particular attention to the layer setup and the activation function choices. The performance is evaluated on the energy resolution function of the reconstructed particles. The algorithm is trained using both parallel CPUs and GPU, and the time performance and the memory usage of the two approaches are systematically compared.

Track reconstruction for the COMET Phase-II experiment with ACTS

An implementation of A Common Tracking Software (ACTS) toolkit for signal electron reconstruction for the COMET muon to electron conversion experiment is discussed. The COMET experiment in J-PARC, Japan, will search for neutrinoless conversion of muons into electrons in the field of an aluminium nucleus, a lepton flavour violating process, aiming target sensitivity of 10-17. To achieve its scientific goals, the experiment requires a reconstructed momentum resolution of lower than 150 keV/c. For the first time by applying ACTS to signal events in the 100 MeV energy range with multiple-turn trajectories in the presence of background events, it is found that the reconstruction efficiency is around 14% with no fake reconstructed events. The implementation details, performance, and issues of ACTS in the context of COMET are presented.

A 50 l Cygno prototype overground characterization

The nature of dark matter is still unknown and an experimental program to look for dark matter particles in our Galaxy should extend its sensitivity to light particles in the GeV mass range and exploit the directional information of the DM particle motion (Vahsen et al. in CYGNUS: feasibility of a nuclear recoil observatory with directional sensitivity to dark matter and neutrinos, arXiv:2008.12587, 2020). The Cygno project is studying a gaseous time projection chamber operated at atmospheric pressure with a Gas Electron Multiplier (Sauli in Nucl Instrum Meth A 386:531, https://doi.org/10.1016/S0168-9002(96)01172-2, 1997) amplification and with an optical readout as a promising technology for light dark matter and directional searches. In this paper we describe the operation of a 50 l prototype named LIME (Long Imaging ModulE) in an overground location at Laboratori Nazionali di Frascati (LNF) of INFN. This prototype employs the technology under study for the 1 cubic meter Cygno demonstrator to be installed at the Laboratori Nazionali del Gran Sasso (LNGS) (Amaro et al. in Instruments 2022, 6(1), https://www.mdpi.com/2410-390X/6/1/6, 2022). We report the characterization of LIME with photon sources in the energy range from few keV to several tens of keV to understand the performance of the energy reconstruction of the emitted electron. We achieved a low energy threshold of few keV and an energy resolution over the whole energy range of 10–20%, while operating the detector for several weeks continuously with very high operational efficiency. The energy spectrum of the reconstructed electrons is then reported and will be the basis to identify radio-contaminants of the LIME materials to be removed for future Cygno detectors.

An automatic measurement method for ankle key angles based on point cloud segmentation network

AbstractIn clinical practice, anterior distal tibialangle (ADTA) and lateral distal tibialangle (LDTA) are important evaluation indexes, which can reflect the degree of deformity and determine the type of deformity to a certain extent. The measurement process of these parameters involves the localization of multiple key regions. To simplify the process of measuring ADTA and LDTA, an automatic measurement method is proposed. First, this study constructs an ankle 3D point cloud dataset by 3D reconstruction of ankle electron computed tomography (CT) data, and then, an ankle point cloud segmentation network with a Self‐Attention mechanism and kernel point convolution combined with each other is constructed, and the ankle point cloud data with labels are input to this network to predict the ankle bone feature points. Finally, the segmented feature points are fitted, and the tibial backbone axis and distal tibial articular surface can be drawn, and based on the fitting results, ADTA and LDTA are obtained. Compared with manual measurements by physicians, the proposed method in this study achieved an average error of 0.46° with a passing rate of 97.7% for ADTA measurement. For LDTA measurement, the average error was 0.69° with a passing rate of 98%. The ankle joint key angle measurement system developed in this study meets the accuracy requirements of orthopedics and significantly reduces the workload for doctors.

Synergistic electrocatalysis of crystal facet and O-vacancy for enhancive urea synthesis from nitrate and CO2

One-pot green urea electrosynthesis from CO2 and nitrogenous pollutants via the co-reductive C−N coupling route under ambient conditions is prospective to replace the traditional urea synthesis process. Here, a bifunctional indium hydroxide (Vo-S-IO-6) bearing oxygen vacancy (Vo) sites on {100} facets was developed to markedly enable urea synthesis (unprecedented faradaic efficiency of 60.6% with a high production rate of 910.4 μg h-1 mg-1cat.) from NO3− and CO2 by subtly integrating facet and defect engineering. ln-O-x-O-ln (x = C or N) configuration and Vo in Vo-S-IO-6 provided dual active sites for the adsorption and activation of NO3− and CO2 to exclusively form *NO2 and *CO2 species, respectively, which ensured highly selective C−N coupling. Moreover, theoretical calculations elaborated that Vo-induced local electron reconstruction accelerated the protonation rate-determining step, thus lowering overall energy barriers. This work offers a design strategy of synergistic electrocatalysts based on geometrical nature for efficient urea synthesis.

Cones and spirals: Multi-axis acquisition for scalar and vector electron tomography

Electron tomography (ET) has become an important tool for understanding the 3D nature of nanomaterials, with recent developments enabling not only scalar reconstructions of electron density, but also vector reconstructions of magnetic fields. However, whilst new signals have been incorporated into the ET toolkit, the acquisition schemes have largely kept to conventional single-axis tilt series for scalar ET, and dual-axis schemes for magnetic vector ET. In this work, we explore the potential of using multi-axis tilt schemes including conical and spiral tilt schemes to improve reconstruction fidelity in scalar and magnetic vector ET. Through a combination of systematic simulations and a proof-of-concept experiment, we show that spiral and conical tilt schemes have the potential to produce substantially improved reconstructions, laying the foundations of a new approach to electron tomography acquisition and reconstruction.

Deep learning techniques for energy clustering in the CMS ECAL

The reconstruction of electrons and photons in CMS depends on topological clustering of the energy deposited by an incident particle in different crystals of the electromagnetic calorimeter (ECAL). These clusters are formed by aggregating neighbouring crystals according to the expected topology of an electromagnetic shower in the ECAL. The presence of upstream material (beampipe, tracker and support structures) causes electrons and photons to start showering before reaching the calorimeter. This effect, combined with the 3.8T CMS magnetic field, leads to energy being spread in several clusters around the primary one. It is essential to recover the energy contained in these satellite clusters in order to achieve the best possible energy resolution for physics analyses. Historically satellite clusters have been associated to the primary cluster using a purely topological algorithm which does not attempt to remove spurious energy deposits from additional pileup interactions (PU). The performance of this algorithm is expected to degrade during LHC Run 3 (2022+) because of the larger average PU levels and the increasing levels of noise due to the ageing of the ECAL detector. New methods are being investigated that exploit state-of-the-art deep learning architectures like Graph Neural Networks (GNN) and self-attention algorithms. These more sophisticated models improve the energy collection and are more resilient to PU and noise, helping to preserve the electron and photon energy resolution achieved during LHC Runs 1 and 2. This work will cover the challenges of training the models as well the opportunity that this new approach offers to unify the ECAL energy measurement with the particle identification steps used in the global CMS photon and electron reconstruction.

Hierarchical interfaces engineering-driven of the CoS2/MoS2/Ni3S2/NF electrode for high-efficient and stable oxygen evolution and urea oxidation reactions

Designing efficient electrocatalysts for both oxygen evolution reaction (OER) and urea oxidation reaction (UOR) is significant to reduce the power consumption for water electrolysis and wastewater degradation, but yet remains a challenge. In this work, an interface engineering strategy is applied to build the CoS2/MoS2/Ni3S2/NF electrode with nanoflowers morphology via a simple hydrothermal method. The hierarchical interfaces can drive electron reconstruction to acquire abundant high-valence Ni active species, which significantly accelerate the OER and UOR. Consequently, the prepared CoS2/MoS2/Ni3S2/NF electrode only needs the low potential of 1.30 and 1.43 V vs RHE to reach 10 mA cm−2 for UOR and OER, respectively. Furthermore, the CoS2/MoS2/Ni3S2/NF electrode can work stable over 100 h for both OER and UOR at the high current density of 100 mA cm−2. This work provides a simple method to fabricate hierarchical interfaces as highly efficient OER and UOR electrocatalysts.

Strain-induced Mn valence state variation in CaMnO3-δ/substrate interfaces: electronic reconstruction versus oxygen vacancies.

This study investigates the nanoscale crystalline and electronic structures of the interfaces between CaMnO3-δ and substrates such as SrTiO3 (001) and LaAlO3 (001) by employing advanced transmission electron microscopy and electron energy loss spectroscopy techniques. The objective is to comprehend the influence of different strains on the Mn valence state. Our findings reveal that the Mn valence state remains relatively stable in the region of a weakly tensile-strained interface, whereas it experiences a significant decrease from Mn4+ to Mn2.3+ in the region of a strongly tensile-strained interface. Although this reduction in valence appears to be consistent with the electron reconstruction scenario, the observed increase in the out-of-plane lattice constant at the interface implies the accumulation of oxygen vacancies at the interface. Consequently, the present study offers a comprehensive understanding of the intricate relationships among the Mn valence state, local structure, and formation of oxygen vacancies in the context of two distinct strain cases. This knowledge is essential for tailoring the interface properties and guiding future developments in the field of oxide heterostructures.

Evolution of the Anther Gland in Early-Branching Papilionoids (ADA Clade, Papilionoideae, Leguminosae).

Papilionoideae is the most diverse subfamily of Leguminosae, especially in terms of floral morphology. The ADA clade shows some exciting floral features among papilionoids, such as anther glands. However, the evolution of the anther glands in such early-branching papilionoids remains unknown. Thus, we compared the occurrence, distribution, morphology, and evolutionary history of the anther glands in species of the ADA clade. Floral buds and/or flowers in 50 species were collected from herbarium specimens and investigated using scanning electron and light microscopy and reconstruction of ancestral character states. The anther apex has a secretory cavity, secretory duct, and phenolic idioblast. The lumen shape of the cavity and duct is closely related to the shape of the anther apex. The oval lumen is located between two thecae, the spherical lumen in the prominent anther apex and the elongated lumen in anthers with a long apex. The occurrence of cavities/ducts in the anther in only two phylogenetically closely related subclades is a unifying character -state. The floral architecture is not correlated with cavity/ducts in the anther but is possibly related to the type of pollinator. Future research needs to combine floral morphology and pollination systems to understand the evolution of floral designs and their diversification.

Near-Infrared-Triggered Reversible Transformations of Gold Nanorod-Laden Lipid Assemblies: Implications for Cellular Delivery

Robust drug and gene delivery systems require innovative methods to control payload release and tune delivery efficiency. The most promising delivery materials are lipid-based and their efficiency often hinges on structural transformations activated by endogenous pH changes. Exogenously driving phase transitions in lipid assemblies is a tantalizing idea that could lead to better control of cargo release dynamics. Multiple reports have demonstrated phase transitions induced in lipid systems, achieved via plasmonic heating of entrained gold nanorods. However, undesirable nonlocalized heating is common due to the size mismatch between the nanorods and the lipid architecture in these systems. Lipid assemblies often exhibit lattice dimensions of just a few nanometers, rendering gold particles challenging to integrate due to their incommensurate sizes, especially in lipid nanoparticle or colloidal forms. We investigate these processes using a judiciously chosen ternary lipid system with entrained small gold nanorods that undergoes transitions between bicontinuous cubic and inverse hexagonal phases on exposure to near-infrared light. Utilizing small-angle X-ray scattering alongside electron reconstruction, we show that gold nanorods integrate into the lipid assembly core lattice by colocalizing in the water nanochannels. We also found that plasmonically activated transformations occur in a couple of minutes and are reversible.

On the use of neural networks for energy reconstruction in high-granularity calorimeters

We contrasted the performance of deep neural networks — Convolutional Neural Network (CNN) and Graph Neural Network (GNN) — to current state of the art energy regression methods in a finely 3D-segmented calorimeter simulated by GEANT4. This comparative benchmark gives us some insight to assess the particular latent signals neural network methods exploit to achieve superior resolution. A CNN trained solely on a pure sample of pions achieved substantial improvement in the energy resolution for both single pions and jets over the conventional approaches. It maintained good performance for electron and photon reconstruction. We also used the Graph Neural Network (GNN) with edge convolution to assess the importance of timing information in the shower development for improved energy reconstruction. We implement a simple simulation based correction to the energy sum derived from the fraction of energy deposited in the electromagnetic shower component. This serves as an approximate dual-readout analogue for our benchmark comparison. Although this study does not include the simulation of detector effects, such as electronic noise, the margin of improvement seems robust enough to suggest these benefits will endure in real-world application. We also find reason to infer that the CNN/GNN methods leverage latent features that concur with our current understanding of the physics of calorimeter measurement.