Advancing the design and management of marine protected areas by quantifying the benefits of coastal ecosystems for communities
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Based on 586 pb -1 of e + e - annihilation data collected at a center-of-mass energy of √s = 4.6 GeV with the BESIII detector at the BEPCII collider, the absolute branching fraction of $Λ^+_c$ → $pK^0_Sη$ decays is measured for the first time to be $B$($Λ^+_c$ → $pK^0_Sη$) = (0.414±0.084±0.028)%, where the first uncertainty is statistical and the second is systematic. The result is compatible with a previous CLEO result on the relative branching fraction B ( Λ c + → p K S 0 η ) B ( Λ c + → p K − π + ) , and consistent with theoretical predictions of SU(3) flavor symmetry.
The electromagnetic process e + e - → $p\bar{p}$ is studied with the initial-state-radiation technique using 7.5 fb -1 of data collected by the BESIII experiment at seven energy points from 3.773 to 4.600 GeV. The Born cross section and the effective form factor of the proton are measured from the production threshold to 3.0 GeV/c 2 using the $p\bar{p}$ invariant-mass spectrum. The ratio of electric and magnetic form factors of the proton is determined from the analysis of the proton-helicity angular distribution.
We search for an axion-like particle (ALP) a through the process $\psi$(3686) → π + π – J/$\psi$, J/$\psi$ → γa, a → γγ in a data sample of (2.71 ± 0.01) x 10 9 $\psi$(3686) events collected by the BESIII detector. No significant ALP signal is observed over the expected background, and the upper limits on the branching fraction of the decay J/$\psi$ → γa and the ALP-photon coupling constant g aγγ are set at 95% confidence level in the mass range of 0.165 ≤ m a ≤ 2.84 GeV/c 2 . The limits on $\mathscr{B}$(J/$\psi$ → γa) range from 8.3 x 10 –8 to 1.8 x 10 –6 over the search region, and the constraints on the ALP-photon coupling are the most stringent to date for 0.165 ≤ m a ≤ 1.468 GeV/c 2 .
We report a search for a dark photon using 14.9 fb –1 of e + e – annihilation data taken at center-of-mass energies from 4.13 to 4.60 GeV with the BESIII detector operated at the BEPCII storage ring. The dark photon is assumed to be produced in the radiative annihilation process of e + e – and to predominantly decay into light dark matter particles, which escape from the detector undetected. The mass range from 1.5 to 2.9 GeV is scanned for the dark photon candidate, and no significant signal is observed. The mass dependent upper limits at the 90% confidence level on the coupling strength parameter ϵ for a dark photon coupling with an ordinary photon vary between 1.6 x 10 –3 and 5.7 x 10 –3 .
We search for the semi-leptonic decays $Λ^+_c$ → Λπ + π - e + ν e and $Λ^+_c$ → p$K^0_S$π - e + ν e in a sample of 4.5 fb -1 of e + e - annihilation data collected in the center-of-mass energy region between 4.600 GeV and 4.699 GeV by the BESIII detector at the BEPCII. No significant signals are observed, and the upper limits on the decay branching fractions are set to be B($Λ^+_c$ → Λπ + π - e + ν e ) < 3.9 × 10 -4 and B($Λ^+_c$ → p$K^0_S$π - e + ν e ) < 3.3 × 10 -4 at the 90% confidence level, respectively
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The health of rotating machines is crucial to the stable operation and safety of nuclear power plants. However, research on machine learning-based fault diagnosis of rotating machines in the nuclear industry is still in its infancy. The signal noise generated in the plant may negatively affect the effectiveness of the analysis. In this paper, a plug-and-play anti-noise machine learning module is proposed to fill the knowledge and capability gap. The modules are loaded into a convolutional neural network called deep residual network (ResNet) to obtain a new model with noise reduction capability. The basic idea is that the module is able to identify noise features and include them in the subsequent analysis, effectively filtering noise at the feature level of the network. Nine variants of the new model are compared with the original ResNet as well as four classical machine learning models to test the effectiveness of the module and to examine the impact of the module's loading modes on the performance of the new model. Finally, this research helps facilitate the application of machine learning in the plant noise environment.
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Recently, deep learning models have shown promising performances in many research areas, including traffic states prediction, due to their ability to model complex nonlinear relationships. However, deep learning models also have drawbacks that make them less preferable for certain short-term traffic prediction applications. For example, they require a large amount of data for model training, which is also computationally expensive. Moreover, deep learning models lack interpretability of the results. This paper develops a short-term traffic states forecasting algorithm based on partial least square (PLS) to help enhance real-time decision-making and build better insights into traffic data. The proposed model is capable of predicting short-term traffic states accurately and efficiently by capturing dominant spatiotemporal features and day-to-day variations from collinear and correlated traffic data. Three case studies are developed to demonstrate the proposed model in short-term traffic prediction applications.
Solid-state batteries represent a promising technology that offers safer and more densely packed energy storage. A primary cause of failure in solid-state cells is the penetration of metal dendrites through the solid electrolyte. Here, we report that fluorine-ion implantation can enhance the mechanical resistance of solid electrolyte Li 6.5 La 3 Zr 1.5 Ta 0.5 O 12 to dendrite propagation by inducing residual compressive stress in the subsurface of the electrolyte. Ion implantation modifies subsurface residual stress and also alters the electrolyte’s surface, resulting in multifunctional enhancement. The combined chemical and mechanical effects of ion implantation enable reversible lithium metal stripping and plating at elevated current densities while enhancing air stability and mitigating the formation of a harmful carbonate layer at the surface. This study provides new insights into a scalable dendrite-suppression strategy for designing solid-state batteries suitable for cycling at room temperature and low stack pressures.
Molecularly doped conjugated polymers with polar side chains are an emerging class of conducting materials exhibiting enhanced and thermally stable conductivity. In this work, we study the electronic conductivity (σ) and the corresponding thermal stability of two polythiophene derivatives comprising oligoethylene glycol side chains: one having oxygen attached to the thiophene ring (poly(3-(methoxyethoxyethoxy)thiophene) (P3MEET)) and the other having a methylene spacer between the oxygen and the thiophene ring (poly(3-(methoxyethoxyethoxymethyl)thiophene) (P3MEEMT)). Thin films were vapor-doped with fluorinated derivatives of tetracyanoquinodimethane (FnTCNQ, n = 4, 2, 1) to determine the role of dopant strength (electron affinity) in maximum achievable σ. Specifically, when vapor doping with F 4 TCNQ, P3MEET achieved a substantially higher σ of 37.1 ± 10.1 S/cm compared to a σ of 0.82 ± 0.06 S/cm for P3MEEMT. Structural characterization using a combination of X-ray and optical spectroscopy reveals that the higher degree of conformational order of polymer chains in the amorphous domain upon doping with F 4 TCNQ in P3MEET is a major contributing factor for the higher σ of P3MEET. Additionally, vapor-doped P3MEET exhibited superior thermal stability compared to P3MEEMT, highlighting that the presence of polar side chains alone does not ensure higher thermal stability. Molecular dynamics simulations indicate that the dopant–side-chain nonbond energy is lower in the P3MEET:F 4 TCNQ mixture, suggesting more favorable dopant–side-chain interaction, which is a factor in improving the thermal stability of a polymer/dopant pair. Our results reveal that additional factors such as polymer ionization energy and side-chain–dopant interaction should be taken into account for the design of thermally stable, highly conductive polymers.
With the ability to modulate electronic properties through molecular doping coupled with ease in processability, semiconducting polymers are at the forefront in enabling organic thermoelectric devices for thermal energy management. In contrast to uniform thermoelectric material properties, an alternative route focuses on functionally graded materials (FGMs) where one spatially controls and optimizes transport properties across the length of a thermoelectric material. While primarily studied in the context of inorganic materials, the concept of FGMs for organic thermoelectrics has not been explored. Herein, we introduce how molecular doping of semiconducting polymers enables spatial compositional control of thin-film FGMs. Specifically, we utilize sequential vapor doping of poly[2,5-bis(3-tetradecylthiophen-2-yl) thieno [3,2-b]thiophene] (PBTTT) with the small molecule acceptor 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4TCNQ) to fabricate the simplest form of FGMs – double- segmented thin films. The two thin-film segments are of equal length (7.5 mm), but each set to different doping levels. Our study focuses on understanding the thermoelectric properties (Seebeck coefficient, α, and electronic conductivity, σ) and structural properties (through X-ray scattering, UV-vis-NIR spectroscopy, and Raman spectroscopy) within and across the two segments. We observe the presence of a small diffuse interfacial region of 0.5 – 1 mm between the two segments where the doping level and transport properties vary continuously. Despite the diffuse interface, the measured effective Seebeck coefficient (αeff) across the two segments is simply the average of α within each segment. Importantly, this experimental result is consistent with reported mathematical models describing αeff in graded thermoelectric materials. Finally, our results demonstrate the facile fabrication and characterization of functionally graded organic thermoelectric materials, providing guidelines for further development on more complex FGMs.
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