Selective conversion of methane (CH
) into value-added chemicals represents a grand challenge for the efficient utilization of rising hydrocarbon sources. We report here dimeric copper centers ...supported on graphitic carbon nitride (denoted as Cu
@C
N
) as advanced catalysts for CH
partial oxidation. The copper-dimer catalysts demonstrate high selectivity for partial oxidation of methane under both thermo- and photocatalytic reaction conditions, with hydrogen peroxide (H
O
) and oxygen (O
) being used as the oxidizer, respectively. In particular, the photocatalytic oxidation of CH
with O
achieves >10% conversion, and >98% selectivity toward methyl oxygenates and a mass-specific activity of 1399.3 mmol g Cu
h
. Mechanistic studies reveal that the high reactivity of Cu
@C
N
can be ascribed to symphonic mechanisms among the bridging oxygen, the two copper sites and the semiconducting C
N
substrate, which do not only facilitate the heterolytic scission of C-H bond, but also promotes H
O
and O
activation in thermo- and photocatalysis, respectively.
Ammonia represents a promising liquid fuel for hydrogen storage, but its large-scale application is limited by the need for precious metal ruthenium (Ru) as catalyst. Here we report on highly ...efficient ammonia decomposition using novel high-entropy alloy (HEA) catalysts made of earth abundant elements. Quinary CoMoFeNiCu nanoparticles are synthesized in a single solid-solution phase with robust control over the Co/Mo atomic ratio, including those ratios considered to be immiscible according to the Co-Mo bimetallic phase diagram. These HEA nanoparticles demonstrate substantially enhanced catalytic activity and stability for ammonia decomposition, with improvement factors achieving >20 versus Ru catalysts. Catalytic activity of HEA nanoparticles is robustly tunable by varying the Co/Mo ratio, allowing for the optimization of surface property to maximize the reactivity under different reaction conditions. Our work highlights the great potential of HEAs for catalyzing chemical transformation and energy conversion reactions.
Phosphorus is a crucial element for living systems and plays significant roles in plant growth. The world’s supply of phosphorus today, however, relies on depleting feedstocks such as phosphate ...rocks, while the demand for phosphorus fertilizers escalates as the population continues to grow. It is thus urgent to develop sustainable sources and production methods for phosphorus. Here, we report on catalytic dephosphorylation for phosphorus recovery from organic and biological molecules. Ceria (CeO2) nanocrystals were synthesized with shape control and applied as artificial phosphatases to cleave the phosphate ester bond in para-nitrophenyl phosphate and release free phosphate anions in aqueous solutions. The dephosphorylation reaction was studied on the CeO2 nanocrystals at various temperatures to evaluate the dependences of rate constant, activation energy, and recyclability on the particle shape. The structure–property relationships established in these studies suggest that the oxygen vacancies on the surface of CeO2 are the active sites for dephosphorylation.
Wearable biosensors are of great interest in recent years due to their potential in health related applications. Multiplex biomarker analysis is needed in wearable devices to improve the sensitivity ...and reliability. Electronic barcoding of micro-particles has the possibility to enable multiplexed biomarker analysis. Compared with traditional optical and plasmonic methods for barcoding, electronically barcoded particles can be classified using ultra-compact electronic readout platforms. Nano-electronic barcoding works by depositing a thin layer of oxide on the top half of a micro-particle. The thickness and dielectric property of the oxide layer can be tuned to modulate the frequency dependent impedance signature of the particles. A one to one correspondence between a target biomarker and each barcoded particle can potentially be established using this technique. The barcoded particles could be tested with wearable devices to enable multiplex analysis for portable point-of-care diagnostics and real-time monitoring. In this work, we fabricated nine barcoded particles by forming oxide layers of different thicknesses and different dielectric materials using atomic layer deposition and assessed the ability to accurately classify particle barcodes using multi-frequency impedance cytometry in conjunction with supervised machine learning.
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•Different single thin-film deposited barcoded particles were fabricated.•Multi-frequency impedance cytometry captured the impedance of bead barcodes.•Supervised machine learning enhanced the ability to classify bead barcodes.
The stability of single-atom catalysts is critical for their practical applications. Although a high temperature can promote the bond formation between metal atoms and the substrate with an enhanced ...stability, it often causes atom agglomeration and is incompatible with many temperature-sensitive substrates. Here, we report using controllable high-temperature shockwaves to synthesize and stabilize single atoms at very high temperatures (1,500-2,000 K), achieved by a periodic on-off heating that features a short on state (55 ms) and a ten-times longer off state. The high temperature provides the activation energy for atom dispersion by forming thermodynamically favourable metal-defect bonds and the off-state critically ensures the overall stability, especially for the substrate. The resultant high-temperature single atoms exhibit a superior thermal stability as durable catalysts. The reported shockwave method is facile, ultrafast and universal (for example, Pt, Ru and Co single atoms, and carbon, C
N
and TiO
substrates), which opens a general route for single-atom manufacturing that is conventionally challenging.
Nanoceria-supported atomic Pt catalysts (denoted as Pt1@CeO2) have been synthesized and demonstrated with advanced catalytic performance for the nonoxidative, direct conversion of methane. These ...catalysts were synthesized by calcination of Pt-impregnated porous ceria nanoparticles at high temperature (ca. 1000 °C), with the atomic dispersion of Pt characterized by combining aberration-corrected high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM), X-ray photoelectron spectroscopy (XPS), X-ray absorption spectroscopy (XAS), and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) analyses. The Pt1@CeO2 catalysts exhibited much superior catalytic performance to its nanoparticulated counterpart, achieving 14.4% of methane conversion at 975 °C and 74.6% selectivity toward C2 products (ethane, ethylene, and acetylene). Comparative studies of the Pt1@CeO2 catalysts with different loadings as well as the nanoparticulated counterpart reveal the single-atom Pt to be the active sites for selective conversion of methane into C2 hydrocarbons.
Three frequently encountered problems—a variety of fault types, data with insufficient labels, and missing fault types—are the common challenges in the early fault diagnosis of space flywheel rotor ...systems. Focusing on the above issues, this paper proposes an intelligent early fault diagnosis method based on the multi-channel convolutional neural network with hierarchical branch and similarity clustering (HB-SC-MCCNN). First, a similarity clustering (SC) method is integrated into the parameter-shared dual MCCNN architecture to set up as the basic structural block. The hierarchical branch model and additional loss are then added to SC-MCCNN to form a hierarchical branch network, which simplifies the problem of fault multi-classification into binary classification with multi-steps. Based on the self-learning characteristics of the proposed model, the unlabeled data and the missing fault types in the training set are re-labeled to realize the re-training of the network. The results of the experiments for comparing the abilities between the proposed method and several advanced deep learning models confirm that on the established early fault dataset of the space flywheel rotor system, the proposed method successfully achieves the hierarchical diagnosis and presents stronger competitiveness in the case of insufficient labeled data and missing fault types at the same time.
Multimetallic nanoclusters (MMNCs) offer unique and tailorable surface chemistries that hold great potential for numerous catalytic applications. The efficient exploration of this vast chemical space ...necessitates an accelerated discovery pipeline that supersedes traditional “trial-and-error” experimentation while guaranteeing uniform microstructures despite compositional complexity. Herein, we report the high-throughput synthesis of an extensive series of ultrafine and homogeneous alloy MMNCs, achieved by 1) a flexible compositional design by formulation in the precursor solution phase and 2) the ultrafast synthesis of alloy MMNCs using thermal shock heating (i.e., ∼1,650 K, ∼500 ms). This approach is remarkably facile and easily accessible compared to conventional vapor-phase deposition, and the particle size and structural uniformity enable comparative studies across compositionally different MMNCs. Rapid electrochemical screening is demonstrated by using a scanning droplet cell, enabling us to discover two promising electrocatalysts, which we subsequently validated using a rotating disk setup. This demonstrated high-throughput material discovery pipeline presents a paradigm for facile and accelerated exploration of MMNCs for a broad range of applications.
Electronic biosensors for DNA detection typically utilize immobilized oligonucleotide probes on a signal transducer, which outputs an electronic signal when target molecules bind to probes. However, ...limitation in probe selectivity and variable levels of non-target material in complex biological samples can lead to nonspecific binding and reduced sensitivity. Here we introduce the integration of 2.8 μm paramagnetic beads with DNA fragments. We apply a custom-made microfluidic chip to detect DNA molecules bound to beads by measuring Impedance Peak Response (IPR) at multiple frequencies. Technical and analytical performance was evaluated using beads containing purified Polymerase Chain Reaction (PCR) products of different lengths (157, 300, 613 bp) with DNA concentration ranging from 0.039 amol to 7.8 fmol. Multi-frequency IPR correlated positively with DNA amounts and was used to calculate a DNA quantification score. The minimum DNA amount of a 300 bp fragment coupled on beads that could be robustly detected was 0.0039 fmol (1.54 fg or 4750 copies/bead). Additionally, our approach allowed distinguishing beads with similar molar concentration DNA fragments of different lengths. Using this impedance sensor, purified PCR products could be analyzed within ten minutes to determine DNA fragment length and quantity based on comparison to a known DNA standard.
As an important form of renewable energy utilization, microgrid (MG) is considered to be the main bearing form of distributed generation in the future. One of the most concerning issues in the ...operation of MG is how to realize its economic dispatch (ED). Nowadays, distributed algorithms have been increasingly used to solve the economic dispatch problem of MG. However, the MG based on the distributed optimization architecture must bear higher cyber‐attack risks. To address this issue, this paper investigates the distributed robust ED problem of MG. Firstly, a multi‐objective dispatch model of MG using a linear weighted sum (LWS) algorithm is developed, which considers the environmental and economic costs. On this basis, an event‐triggered fully distributed algorithm is proposed, which can effectively reduce communication times. Furthermore, an attack resilient strategy against false data injection (FDI) attacks is implemented in the proposed fully distributed algorithm, which has strong robustness against various colluding attacks and non‐colluding attacks, and can eliminate incorrect measurement of incremental cost and power generation data. Finally, the effectiveness of the proposed distributed control strategy is demonstrated through case studies in this paper.
In this work, a multi‐objective dispatch model of a microgrid using a linear weighted sum algorithm is developed, which considers the environmental and economic costs. On this basis, an event‐triggered fully distributed algorithm is proposed, which can effectively reduce communication times. Furthermore, an attack resilient strategy against false data injection attacks is implemented in the proposed algorithm, which can eliminate incorrect measurement of incremental cost and power generation data.
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