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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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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Momentum-Space Observation of Optically Excited Nonthermal Electrons in Graphene with Persistent Pseudospin Polarization

The unique optical properties of graphene, with broadband absorption and ultrafast response, make it a critical component of optoelectronic and spintronic devices. Using time-resolved momentum microscopy with high data rate and high dynamic range, we report momentum-space measurements of electrons promoted to the graphene conduction band with visible light and their subsequent relaxation. Here, we observe a pronounced nonthermal distribution of nascent photoexcited electrons with lattice pseudospin polarization in remarkable agreement with results of simple tight-binding theory. By varying the excitation fluence, we vary the relative importance of electron-electron vs electron-phonon scattering in the relaxation of the initial distribution. Increasing the excitation fluence results in increased noncollinear electron-electron scattering and reduced pseudospin polarization, although up-scattered electrons retain a degree of polarization. These detailed momentum-resolved electron dynamics in graphene demonstrate the capabilities of high-performance time-resolved momentum microscopy in the study of 2D materials and can inform the design of graphene devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

How are Heterogeneous Nucleation Rate Observations Influenced by Instrument Resolution?

Experimental measurements of the heterogeneous nucleation rate rely on counting the number of nuclei with time. However, the size of a thermodynamically stable nucleus is often a few nanometers in diameter and is below the resolution of most (in situ) measurement techniques that provide a statistically valid sample. Due to the finite resolution of the instruments and analysis methods, it is challenging to capture the incipient nuclei and the subsequent evolution of nuclei density over time. In this work, we demonstrate the impact of instrument resolution on observed nuclei densities by comparing numerical modeling with experimental results. Further, to achieve this, we implemented heterogeneous nucleation within the pore-scale reactive transport modeling framework using classical nucleation theory (CNT). We compared the modeling results with nucleation rates measured using X-ray nanotomography (XnT) and evaluated how these impact the apparent values of the prefactor and interfacial energy based on CNT and the crystal growth rate. Specifically, we applied a resolution threshold (artificial resolution limit) in the model during nuclei counting to resemble an experimental resolution, ranging from 15 to 500 nm. The findings reveal that the instrument resolution significantly impacts the apparent prefactor and interfacial energy. Both apparent prefactor and interfacial energy decrease with a decrease in the instrument resolution. While deviation in the prefactor due to resolution is anticipated, those in the interfacial energy are unexpected. The approach described here allows one to correct apparent nucleation rates that depend on the instrument’s resolution to derive “intrinsic” CNT parameters for the prefactor and interfacial energy.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Impacts of Precipitation Events on Concentrations of Oxygenated Gas- and Particle-Phase Compounds Observed in the Amazon

Removal of gases and particles by precipitation (wet deposition) is a critical process that significantly influences the transport and chemical transformation of atmospheric compounds. However, there are few studies that directly measure or constrain the rates of this process under real-world conditions. This work quantifies the net change in ambient concentrations during precipitation events (removal rates) of gas- and particle-phase organic compounds at a surface site near Manaus, Brazil, during the GoAmazon2014/5 campaign. Removal rates of identified and unknown compounds that have been previously classified into source-based clusters are measured during rain events and categorized based on estimated properties of compounds and clusters. Highly oxygenated gases, such as isoprene oxidation products, are removed during precipitation events with a median removal rate of 0.09 h –1 and the fastest analyte is removed at a rate of 0.22 h –1 . Removal rates of particle-phase compounds are observed at roughly this median rate, while less soluble gases, such as terpenes, exhibit low removal rates. These results are roughly in agreement with prior theoretical estimates of wet deposition rates for comparable compounds, providing an empirical point of comparison while noting that our metric reflects the net influence of precipitation events rather than wet deposition alone.

atmospheric chemistry↗
DOE OSTI2024

Observation of a Picosecond Light-Induced Spin Transition in Polymeric Nanorods

Spin transition (ST) materials are attractive for developing photoswitchable devices, but their slow material transformations limit device applications. Size reduction could enable faster switching, but the photoinduced dynamics at the nanoscale remains poorly understood. Here, we report a femtosecond optical pump multimodal X-ray probe study of polymeric nanorods. Simultaneously tracking the ST order parameter with X-ray emission spectroscopy and structure with X-ray diffraction, we observe photodoping of the low-spin–lattice within ~150 fs. Above a ~16% photodoping threshold, the transition to the high-spin phase occurs following an incubation period assigned to vibrational energy redistribution within the nanorods activating the molecular spin switching. Above ~60% photodoping, the incubation period disappears, and the transition completes within ~50 ps, preceded by the elastic nanorod expansion in response to the photodoping. These results support the feasibility of ST material-based GHz optical switching applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Direct Observation and Analysis of Low-Energy Magnons with Raman Spectroscopy in Atomically Thin NiPS 3

van der Waals (vdW) magnets have rapidly emerged as a fertile playground for fundamental physics and exciting applications. Despite the impressive developments over the past few years, technical limitations pose a severe challenge to many other potential breakthroughs. High on the list is the lack of suitable experimental tools for studying spin dynamics on atomically thin samples. Here, Raman scattering techniques are employed to directly observe the low-lying magnon (~1 meV) even in bilayer NiPS 3 . Further, the advantage is that it offers excellent energy resolutions far better on low-energy sides than most inelastic neutron spectrometers can offer. More importantly, with appropriate theoretical analysis, the polarization dependence of the Raman scattering by those low-lying magnons also provides otherwise hidden information on the dominant spin-exchange scattering paths for different magnons. By comparing with high-resolution inelastic neutron scattering data, these low-energy Raman modes are confirmed to be indeed of magnon origin. Because of the different scattering mechanisms involved in inelastic neutron and Raman scattering, this information is fundamental in pinning down the final spin Hamiltonian. This work demonstrates the capability of Raman spectroscopy to probe the genuine two-dimensional spin dynamics in atomically thin vdW magnets, which can provide insights that are obscured in bulk spin dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Direct Observation of Morphological and Chemical Changes during the Oxidation of Model Inorganic Ligand-Capped Particles

Functionalization and volatilization are competing reactions during the oxidation of carbonaceous materials and are important processes in many different areas of science and technology. Here, we present a combined ambient pressure X-ray photoelectron spectroscopy (APXPS) and grazing incidence X-ray scattering (GIXS) investigation of the oxidation of oleic acid ligands surrounding NaYF 4 nanoparticles (NPs) deposited onto SiO x /Si substrates. While APXPS monitors the evolution of the oxidation products, GIXS provides insight into the morphology of the ligands and particles before and after the oxidation. Our investigation shows that the oxidation of the oleic acid ligands proceeds at O 2 partial pressures of below 1 mbar in the presence of X-rays, with the oxidation eventually reaching a steady state in which mainly CH x and –COOH functional groups are observed. The scattering data reveal that the oxidation and volatilization reaction proceeds preferentially on the side of the particle facing the gas phase, leading to the formation of a chemically and morphologically asymmetric ligand layer. This comprehensive picture of the oxidation process could be obtained only by combining the X-ray scattering and APXPS data. The investigation presented here lays the foundation for further studies of the stability of NP layers in the presence of reactive trace gases and ionizing radiation and for other nanoscale systems where chemical and morphological changes happen simultaneously and cannot be understood in isolation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Direct Observation of Phase Change Accommodating Hydrogen Uptake in Bimetallic Nanoparticles

Hydrogen holds great promise as a cleaner alternative to fossil fuels, but its efficient and affordable storage remains a significant challenge. Bimetallic systems, such as Pd and Ni, present a promising option for storing hydrogen. In this study, using the combination of different cutting-edge X-ray and electron techniques, we observed the transformations of Pd–Ni nanoparticles, which initially consist of a NiO-rich shell surrounding a Pd-rich core but undergo a major transformation when they interact with hydrogen. During hydrogen exposure, the Pd core breaks into smaller pockets, dramatically increasing its surface area and enhancing the hydrogen storage capacity, especially in nanoparticles with lower Pd content. The findings provide a deep understanding of the morphological changes at the atomic level during hydrogen storage and contribute to designing cost-effective hydrogen storage using multimetallic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Observing the Surface Termination of LaScO 3 Perovskite Using Solid-State Nuclear Magnetic Resonance

Materials with well-defined surfaces are drawing increased attention for the design of bespoke catalysts and nanomaterials. Gaining a detailed understanding of the surfaces of these materials is an important challenge, which is often complicated by surface polymorphism and dynamic restructuring. We introduce the use of surface-enhanced NMR spectroscopy for the observation of such surfaces, focusing on LaScO 3 as an example. Here, we show that double-resonance NMR experiments correlating surface oxygen and probe molecules to the 139 La and 45 Sc nuclei at the surface reveal the material to be terminated by a ScO x monolayer. Surface-selective 17 O and 45 Sc NMR experiments further showed the material to be hydroxyl terminated and that the surface may be prone to dynamic restructuring as a result of moisture exposure. Perhaps most interestingly, surface-selective 139 La NMR experiments revealed the existence of previously undetected surface lanthanum defects, suggesting that surface-enhanced NMR may be useful as a guide in the synthesis of defect-free surfaces in the design of various nanomaterials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Assessment of Storm-Associated Precipitation and Its Extremes Using Observational Data Sets and Climate Model Short-Range Hindcasts

Heavy precipitation, often associated with weather phenomena such as tropical cyclones, extratropical cyclones (ETCs), atmospheric rivers (ARs), and mesoscale convective systems (MCSs), can cause significant socio-economic loss. Here, in this study, we apply atmospheric feature trackers to quantify the contributions of these storm types in observational data sets and climate model short-range hindcasts. We generate a global hourly storm data set at 0.25° spatial resolution covering 2006–2020, based on the tracking results from TempestExtremes and Python FLEXible object TRacKeR. Our analyses show that these four storm types account for 67% of global annual mean precipitation and 82% of top 1% precipitation extremes, with MCSs mainly over the tropics, and ARs and ETCs over the midlatitudes. The percentage of precipitation contributions from these storms also show strong seasonality over many geographical locations. We further apply the tracking results to the Energy Exascale Earth System Model (E3SM) short-range hindcasts and evaluate how well these storms are simulated. The evaluation show that E3SM, with ∼1° resolution, significantly underestimates storm-associated precipitation totals and extremes, especially for MCSs in the tropics. Our analysis also suggests that model fails to capture the correct mean diurnal phases and amplitude of MCS precipitation. This phenomenon-based approach provides a better understanding of precipitation characteristics and can lead to enhanced model evaluation by revealing underlying problems in model physics related to precipitation processes associated with the heavy-precipitating storms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Phenology of Photosynthesis in Winter‐Dormant Temperate and Boreal Forests: Long‐Term Observations From Flux Towers and Quantitative Evaluation of Phenology Models

Abstract We examined the seasonality of photosynthesis in 46 evergreen needleleaf (evergreen needleleaf forests (ENF)) and deciduous broadleaf (deciduous broadleaf forests (DBF)) forests across North America and Eurasia. We quantified the onset and end (Start GPP and End GPP ) of photosynthesis in spring and autumn based on the response of net ecosystem exchange of CO 2 to sunlight. To test the hypothesis that snowmelt is required for photosynthesis to begin, these were compared with end of snowmelt derived from soil temperature. ENF forests achieved 10% of summer photosynthetic capacity ∼3 weeks before end of snowmelt, while DBF forests achieved that capacity ∼4 weeks afterward. DBF forests increased photosynthetic capacity in spring faster (1.95% d −1 ) than ENF (1.10% d −1 ), and their active season length (End GPP –Start GPP ) was ∼50 days shorter. We hypothesized that warming has influenced timing of the photosynthesis season. We found minimal evidence for long‐term change in Start GPP , End GPP , or air temperature, but their interannual anomalies were significantly correlated. Warmer weather was associated with earlier Start GPP (1.3–2.5 days °C −1 ) or later End GPP (1.5–1.8 days °C −1 , depending on forest type and month). Finally, we tested whether existing phenological models could predict Start GPP and End GPP . For ENF forests, air temperature‐ and daylength‐based models provided best predictions for Start GPP , while a chilling‐degree‐day model was best for End GPP . The root mean square errors (RMSE) between predicted and observed Start GPP and End GPP were 11.7 and 11.3 days, respectively. For DBF forests, temperature‐ and daylength‐based models yielded the best results (RMSE 6.3 and 10.5 days).

Environmental Sciences & Ecology↗
DOE OSTI2024

Using Convex Optimization to Efficiently Apportion Tracer and Pollutant Sources From Point Concentration Observations

Abstract Rivers transport elements, minerals, chemicals, and pollutants produced in their upstream basins. A sample from a river is a mixture of all of its upstream sources, making it challenging to pinpoint the contribution from each individual source. Here, we show how a nested sample design and convex optimization can be used to efficiently unmix downstream samples of a well‐mixed, conservative tracer in a steady state system into the contributions of their upstream sources. Our approach is significantly faster than previous methods. We represent the river's sub‐catchments, defined by sampling sites, using a directed acyclic graph. This graph is used to build a convex optimization problem which, thanks to its convexity, can be quickly solved to global optimality—in under a second on desktop hardware for data sets of ∼100 samples or fewer. Uncertainties in the upstream predictions can be generated using Monte Carlo resampling. We provide an open‐source implementation of this approach in Python. The inputs required are straightforward: a table containing sample locations and observed tracer concentrations, along with a D8 flow‐direction raster map. As a case study, we use this method to map the elemental geochemistry of sediment sources for rivers draining the Cairngorms mountains, UK. This method could be extended to non‐conservative and non‐steady state tracers. We also show, theoretically, how multiple tracers could be simultaneously inverted to recover upstream run‐off or erosion rates as well as source concentrations. Overall, this approach can provide valuable insights to researchers in various fields, including water quality, geochemical exploration, geochemistry, hydrology, and wastewater epidemiology.

Barnes, Richard↗
DOE OSTI2025

Prediction of Complex Observed Shear Wave Splitting Patterns at Ryukyu Subduction Zone Using a Strong Intra-Slab Anisotropy Model

Complex shear wave splitting (SWS) patterns in subduction zones are often interpreted geodynamically as resulting from complex mantle flow; however, this may not always be necessary. We analyzed 7,093 high-quality SWS measurements from teleseismic S waves recorded by Hi-net stations across the Ryukyu arc in Japan. Our findings show a systematic rotation of the fast S polarization from trench-parallel to trench-perpendicular depending on the earthquake backazimuth. For the same earthquake, the measured splitting patterns also vary spatially across the southwest Japan. Using full-wave seismic modeling, we showed that a dipping slab with ~30% shear anisotropy of the tilted transverse isotropy (TTI) type, with a symmetry axis perpendicular to the slab interface, can predict the observed delay times and polarization rotation. Our results highlight the importance of considering dipping anisotropic slabs in interpreting SWS at subduction zones.

58 GEOSCIENCES↗
DOE OSTI2024

Higher‐Order Gravity Waves and Traveling Ionospheric Disturbances From the Polar Vortex Jet on 11–15 January 2016: Modeling With HIAMCM‐SAMI3 and Comparison With Observations in the Thermosphere and Ionosphere

Abstract In Vadas et al. (2024, https://doi.org/10.1029/2024ja032521 ), we modeled the atmospheric gravity waves (GWs) during 11–14 January 2016 using the HIAMCM, and found that the polar vortex jet generates medium to large‐scale, higher‐order GWs in the thermosphere. In this paper, we model the traveling ionospheric disturbances (TIDs) generated by these GWs using the HIAMCM‐SAMI3 and compare with ionospheric observations from ground‐based Global Navigation Satellite System (GNSS) receivers, Incoherent Scatter Radars (ISR) and the Super Dual Auroral Radar Network (SuperDARN). We find that medium to large‐scale TIDs are generated worldwide by the higher‐order GWs from this event. Many of the TIDs over Europe and Asia have concentric ring/arc‐like structure, and most of those over North/South America have planar wave structure and occur during the daytime. Those over North/South America propagate southward and are generated by higher‐order GWs from Europe/Asia which propagate over the Arctic. These latter TIDs can be misidentified as arising from geomagnetic forcing. We find that the higher‐order GWs that propagate to Africa and Brazil from Europe may aid in the formation of equatorial plasma bubbles (EPBs) there. We find that the simulated GWs, TIDs and EPBs agree with EISCAT, PFISR, GNSS, and SuperDARN measurements. We find that the higher‐order GWs are concentrated at N at 200 km, in agreement with GOCE and CHAMP data. Thus the polar vortex jet is important for generating TIDs in the northern winter ionosphere via multi‐step vertical coupling through GWs.

Vadas, Sharon L. [Northwest Research Associates Bo↗
DOE OSTI2025

Comparison of the Distribution of the In Situ Ring Current Density Based on Magnetic Field Observations From THEMIS, MMS and Cluster

Abstract We review and extend previous work on the ring current density distribution by applying the curlometer technique to multi‐spacecraft magnetic field data on different spatial scales, directly comparing the results of three space missions: Time History of Events and Macroscale Interactions during Substorms (THEMIS), The Magnetospheric Multiscale (MMS) and Cluster, that have so far accrued decades of data on a range of spatial separation scales. We suggest that the statistical results of Cluster data over two decades can illustrate well the overall form of the ring current (in the ring plane), but can underestimate the current density, while THEMIS (partially) and MMS are able to observe the detailed structure of the ring current and indeed show different behavior on some (smaller) spatial scales. These results suggest that, in addition to the local time and radial form, reported previously, the morphology of the ring current may also depend on magnetic latitude, as influenced by the tilt angle between the inner and outer magnetic fields.

Tan, X. [School of Space and Earth Sciences Beihan↗
DOE OSTI2024

Evaluation of the Planetary Boundary Layer Height From ERA5 Reanalysis With MOSAiC Observations Over the Arctic Ocean

The planetary boundary layer height (PBLH) is a crucial indicator reflecting the region of the atmosphere characterized by continuous turbulence. Here, we use radiosonde and surface meteorological observations (4–7 times per day, year-round measurements) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition to derive the PBLH (PBLH MOSAiC ), and further evaluate the PBLH from the ERA5 reanalysis (PBLH ERA5 ). Comparisons between PBLH MOSAiC and PBLH ERA5 from different perspectives reveal that: (a) The overestimation of PBLH ERA5 when the sea ice concentration is >90% is significant with the centered root mean squared error reaching up to 201 m; (b) The difference between the two products is notably pronounced in cold seasons, while it is comparatively diminished in warm seasons; (c) In neutral boundary layers, differences in PBLH ERA5 are larger compared with stable and convective boundary layers. In addition, the analysis of error sources indicates that the bias of PBLH ERA5 is sensitive to the bias of vertical thermal structure and wind speed profiles in ERA5 data sets in all conditions. Finally, we find a Random Forest model effectively reduces the bias of PBLH ERA5 with the index of agreement reaching up to 0.71 in the test data set, while a multiple linear regression demonstrates comparable performance to the Random Forest model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Examining Daily Temporal Characteristics of Oil and Gas Methane Emissions in the Delaware Basin Using Continuous Tower Observations

Top-down studies have found consistent underestimations in the United States Environmental Protection Agency (EPA) methane emissions inventory from the oil and gas (O&G) sector. Many of these studies use observations that bias toward hours when worktime activity occurs. In this study, we analyze over 2 years of methane measurements from a tower network in the Delaware basin to analyze hourly temporal emission patterns. Inversion results suggest a range in emissions from 137 Mg/hr at night to 197 Mg/hr during the day, present during both weekdays and weekends. If these results are applicable to other basins, daytime-influenced methodologies may overestimate daily emission rates by up to 27%. This bias does not reconcile the more than 200% difference between the EPA inventory and top-down estimates in the Delaware basin. This study demonstrates how continuous measurement networks can be combined with detailed activity data to improve bottom-up inventories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Numerical Investigation of Observational Flux Partitioning Methods for Water Vapor and Carbon Dioxide

Abstract While yearly budgets of CO 2 flux (F c ) and evapotranspiration (ET) above vegetation can be readily obtained from eddy‐covariance measurements, the separate quantification of their soil (respiration and evaporation) and canopy (photosynthesis and transpiration) components remains an elusive yet critical research objective. In this work, we investigate four methods to partition observed total fluxes into soil and plant sources: two new and two existing approaches that are based solely on analysis of conventional high frequency eddy‐covariance (EC) data. The physical validity of the assumptions of all four methods, as well as their performance under different scenarios, are tested with the aid of large‐eddy simulations, which are used to replicate eddy‐covariance field experiments. Our results indicate that canopies with large, exposed soil patches increase the mixing and correlation of scalars; this negatively impacts the performance of the partitioning methods, all of which require some degree of uncorrelatedness between CO 2 and water vapor. In addition, best performances for all partitioning methods were found when all four flux components are non‐negligible, and measurements are collected close to the canopy top. Methods relying on the water‐use efficiency (W) perform better whenWis known a priori, but are shown to be very sensitive to uncertainties in this input variable especially when canopy fluxes dominate. We conclude by showing how the correlation coefficient between CO 2 and water vapor can be used to infer the reliability of differentWparameterizations.

Environmental Sciences & Ecology↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗