Engineering topics

Zheng, Yi

Publications and source records attributed to Zheng, Yi.

5 records

DOE OSTI2024

Beyond Conventional Charge Density Wave for Strongly Enhanced 2D Superconductivity in 1H‐TaS 2 Superlattices

Noncentrosymmetric transition metal dichalcogenide (TMD) monolayers offer a fertile platform for exploring unconventional Ising superconductivity (SC) and charge density waves (CDWs). However, the vulnerability of isolated monolayers to structural disorder and environmental oxidation often degrade their electronic coherence. Herein, an alternative approach is reported for fabricating stable and intrinsic monolayers of 1H-TaS2 sandwiched between SnS blocks in a (SnS) 1.15 TaS 2 van der Waals (vdW) superlattice. The SnS block layers not only decouple individual 1H-TaS 2 sublayers to endow them with monolayer-like electronic characteristics, but also protect the 1H-TaS 2 layers from electronic degradation. The results reveal the characteristic 3 × 3 CDW order in 1H-TaS 2 sublayers associated with electronic rearrangement in the low-lying sulfur p band, which uncovers a previously undiscovered CDW mechanism rather than the conventional Fermi surface-related framework. Additionally, the (SnS) 1.15 TaS 2 superlattice exhibits a strongly enhanced Ising-like SC with a layer-independent T c of ≈3.0 K, comparable to that of the isolated monolayer 1H-TaS 2 sample, presumably attributed to their monolayer-like characteristics and retained Fermi states. These results provide new insights into the long-debated CDW order and enhanced SC of monolayer 1H-TaS 2 , establishing bulk vdW superlattices as promising platforms for investigating exotic collective quantum phases in the 2D limit.

2D superconductivity↗

DOE OSTI2024

Fertilizer management for global ammonia emission reduction

Crop production is a large source of atmospheric ammonia (NH 3 ), which poses risks to air quality, human health and ecosystems. However, estimating global NH 3 emissions from croplands is subject to uncertainties because of data limitations, thereby limiting the accurate identification of mitigation options and efficacy. In this report we develop a machine learning model for generating crop-specific and spatially explicit NH 3 emission factors globally (5-arcmin resolution) based on a compiled dataset of field observations. We show that global NH 3 emissions from rice, wheat and maize fields in 2018 were 4.3 ± 1.0 Tg N yr -1 , lower than previous estimates that did not fully consider fertilizer management practices. Furthermore, spatially optimizing fertilizer management, as guided by the machine learning model, has the potential to reduce the NH3 emissions by about 38% (1.6 ± 0.4 Tg N yr -1 ) without altering total fertilizer nitrogen inputs. Specifically, we estimate potential NH3 emissions reductions of 47% (44–56%) for rice, 27% (24–28%) for maize and 26% (20–28%) for wheat cultivation, respectively. Under future climate change scenarios, we estimate that NH3 emissions could increase by 4.0 ± 2.7% under SSP1–2.6 and 5.5 ± 5.7% under SSP5–8.5 by 2030–2060. However, targeted fertilizer management has the potential to mitigate these increases.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Historical and projected future runoff over the Mekong River basin

The Mekong River (MR) crosses the borders and connects six countries, including China, Myanmar, Laos, Thailand, Cambodia, and Vietnam. It provides critical water resources and supports natural and agricultural ecosystems, socioeconomic development, and the livelihoods of the people living in this region. Understanding changes in the runoff of this important international river under projected climate change is critical for water resource management and climate change adaptation planning. However, research on long-term runoff dynamics for the MR and the underlying drivers of runoff variability remains scarce. Here, we analyse historical runoff variations from 1971 to 2020 based on runoff gauge data collected from eight hydrological stations along the MR. With these runoff data, we then evaluate the runoff simulation performance of five global hydrological models (GHMs) forced by four global climate models (GCMs) under the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). Furthermore, based on the best simulation combination, we quantify the impact of future climate change on river runoff changes in the MR. The result shows that the annual runoff in the MR has not changed significantly in the past 5 decades, while the establishment of dams and reservoirs in the basin visibly affected the annual runoff distribution. The ensemble-averaged result of the Water Global Assessment and Prognosis version 2 (WaterGAP2; i.e. GHM) forced by four GCMs has the best runoff simulation performance. Under Representative Concentration Pathways (RCPs; i.e. RCP2.6, RCP6.0 and RCP8.5), the runoff of the MR is projected to increase significantly (p<0.05); e.g. 3.81 ± 3.47 m 3 s -1 a -1 (9 ± 8 % increase in 100 years) at the upper reach under RCP2.6 and 16.36 ± 12.44 m 3 s -1 a -1 (13 ±10 % increase in 100 years) at the lower reach under RCP6.0. In particular, under the RCP6.0 scenario, the increase in annual runoff is most pronounced in the middle and lower reaches, due to increased precipitation and snowmelt. Under the RCP8.5 scenario, the runoff distribution in different seasons varies obviously, increasing the risk of flooding in the wet season and drought in the dry season.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Gaussian beam quantum radar protocol

We present an entangled quantum radar protocol. It consists in scanning the sky with a thin Gaussian beam and measuring the travel time of the radiation reflected from the target, as in conventional radars. Here the Gaussian beam is composed of $N$ photons entangled in the frequency degrees of freedom. We show that this provides a $\sqrt{N}$ quantum enhancement over the unentangled case, as is usual in quantum metrology.

Maccone, Lorenzo↗

DOE OSTI2023

Differentiable modelling to unify machine learning and physical models for geosciences

Process-based modelling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills yet are unable to answer specific scientific questions. Here, in this Perspective, we explore differentiable modelling as a pathway to dissolve the perceived barrier between process-based modelling and machine learning in the geosciences and demonstrate its potential with examples from hydrological modelling. ‘Differentiable’ refers to accurately and efficiently calculating gradients with respect to model variables or parameters, enabling the discovery of high-dimensional unknown relationships. Differentiable modelling involves connecting (flexible amounts of) prior physical knowledge to neural networks, pushing the boundary of physics-informed machine learning. It offers better interpretability, generalizability, and extrapolation capabilities than purely data-driven machine learning, achieving a similar level of accuracy while requiring less training data. Additionally, the performance and efficiency of differentiable models scale well with increasing data volumes. Under data-scarce scenarios, differentiable models have outperformed machine-learning models in producing short-term dynamics and decadal-scale trends owing to the imposed physical constraints. Differentiable modelling approaches are primed to enable geoscientists to ask questions, test hypotheses, and discover unrecognized physical relationships. Future work should address computational challenges, reduce uncertainty, and verify the physical significance of outputs.

58 GEOSCIENCES↗