Showing papers by "ETH Zurich published in 2022"
University of Exeter1, Max Planck Institute for Biogeochemistry2, Tyndall Centre3, Atlantic Oceanographic and Meteorological Laboratory4, Bjerknes Centre for Climate Research5, University of Maryland, College Park6, CICERO Center for International Climate Research7, Leibniz Institute for Baltic Sea Research8, University of Reading9, Leibniz Institute of Marine Sciences10, Goddard Space Flight Center11, Flanders Marine Institute12, Food and Agriculture Organization13, Alfred Wegener Institute for Polar and Marine Research14, National Oceanic and Atmospheric Administration15, University of East Anglia16, Japan Meteorological Agency17, ETH Zurich18, National Institute for Environmental Studies19, Karlsruhe Institute of Technology20, Laboratoire des Sciences du Climat et de l'Environnement21, Tula Foundation22, Hertie Institute for Clinical Brain Research23, Nanjing University of Information Science and Technology24, Wageningen University and Research Centre25, Tsinghua University26, University of Western Sydney27, Cooperative Institute for Research in Environmental Sciences28, University of Florida29, Center for Neuroscience and Regenerative Medicine30, Woods Hole Research Center31, Michigan State University32, Tianjin University33, Auburn University34, Jilin Medical University35, Max Planck Institute for Meteorology36, Imperial College London37, Centre National de Recherches Météorologiques38, University of Groningen39, Tohoku University40, Ludwig Maximilian University of Munich41, Bank for International Settlements42, Institut Pierre-Simon Laplace43, Environment Canada44, North West Agriculture and Forestry University45, Northwest A&F University46, Pacific Marine Environmental Laboratory47, Stanford University48, Utrecht University49
TL;DR: Friedlingstein et al. as mentioned in this paper presented and synthesized datasets and methodology to quantify the five major components of the global carbon budget and their uncertainties, including fossil CO2 emissions, land use and land-use change data and bookkeeping models.
Abstract: Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize datasets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the first time, an approach is shown to reconcile the difference in our ELUC estimate with the one from national greenhouse gas inventories, supporting the assessment of collective countries' climate progress. For the year 2020, EFOS declined by 5.4 % relative to 2019, with fossil emissions at 9.5 ± 0.5 GtC yr−1 (9.3 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 0.9 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission of 10.2 ± 0.8 GtC yr−1 (37.4 ± 2.9 GtCO2). Also, for 2020, GATM was 5.0 ± 0.2 GtC yr−1 (2.4 ± 0.1 ppm yr−1), SOCEAN was 3.0 ± 0.4 GtC yr−1, and SLAND was 2.9 ± 1 GtC yr−1, with a BIM of −0.8 GtC yr−1. The global atmospheric CO2 concentration averaged over 2020 reached 412.45 ± 0.1 ppm. Preliminary data for 2021 suggest a rebound in EFOS relative to 2020 of +4.8 % (4.2 % to 5.4 %) globally. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2020, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and datasets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this dataset (Friedlingstein et al., 2020, 2019; Le Quéré et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2021 (Friedlingstein et al., 2021).
343 citations
University of Exeter1, Max Planck Institute for Biogeochemistry2, Tyndall Centre3, Leibniz Institute for Baltic Sea Research4, Atlantic Oceanographic and Meteorological Laboratory5, University of Maryland, College Park6, CICERO Center for International Climate Research7, University of Reading8, Leibniz Institute of Marine Sciences9, Goddard Space Flight Center10, Flanders Marine Institute11, Food and Agriculture Organization12, Alfred Wegener Institute for Polar and Marine Research13, Geophysical Institute14, University of Victoria15, National Oceanic and Atmospheric Administration16, Karlsruhe Institute of Technology17, Laboratoire des Sciences du Climat et de l'Environnement18, Japan Meteorological Agency19, Indiana University20, ETH Zurich21, National Institute for Environmental Studies22, University of East Anglia23, European Commission24, Tula Foundation25, Bjerknes Centre for Climate Research26, Hertie Institute for Clinical Brain Research27, Nanjing University of Information Science and Technology28, Ludwig Maximilian University of Munich29, Auburn University30, Wageningen University and Research Centre31, University of Western Sydney32, Cooperative Institute for Research in Environmental Sciences33, Tsinghua University34, University of Florida35, Center for Neuroscience and Regenerative Medicine36, Woods Hole Research Center37, University of Alaska Fairbanks38, Princeton University39, Michigan State University40, University of Washington41, Appalachian State University42, Sun Yat-sen University43, Imperial College London44, University of Groningen45, University of Tennessee46, Washington University in St. Louis47, Jilin Medical University48, Tohoku University49, Rutgers University50, Centre for Research on Ecology and Forestry Applications51, Institut Pierre-Simon Laplace52, North West Agriculture and Forestry University53, Northwest A&F University54, Pacific Marine Environmental Laboratory55, Xi'an Jiaotong University56, Stanford University57, National Center for Atmospheric Research58, University of Edinburgh59, Max Planck Institute for Meteorology60, Utrecht University61, Oak Ridge National Laboratory62
TL;DR: Friedlingstein et al. as mentioned in this paper presented and synthesized data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties, including fossil CO2 emissions, land use and land-use change data and bookkeeping models.
Abstract: Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1 (40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9 ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with a BIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).
98 citations
TL;DR: A review of the physics of particle-laden turbulence in the last decade can be found in this paper, which is motivated by the fast progress in our understanding of particleladen turbulence and the tremendous advances of measurement and simulatio...
Abstract: This review is motivated by the fast progress in our understanding of the physics of particle-laden turbulence in the last decade, partly due to the tremendous advances of measurement and simulatio...
98 citations
TL;DR: POCLib as discussed by the authors proposes orthogonal processing on compression (orthogonal POC), which means that text analytics can be efficiently supported directly on compressed data, regardless of the type of the data processing.
Abstract: Parallel technology boosts data processing in recent years, and parallel direct data processing on hierarchically compressed documents exhibits great promise. The high-performance direct data processing technique brings large savings in both time and space by removing the need for decompressing data. However, its benefits have been limited to data traversal operations; for random accesses, direct data processing is several times slower than the state-of-the-art baselines. This article proposes a novel concept, orthogonal processing on compression (orthogonal POC), which means that text analytics can be efficiently supported directly on compressed data, regardless of the type of the data processing – that is, the type of data processing is orthogonal to its capability of conducting POC. Previous proposals, such as TADOC, are not orthogonal POC. This article presents a set of techniques that successfully eliminate the limitation, and for the first time, establishes the near orthogonal POC feasibility of effectively handling both data traversal operations and random data accesses on hierarchically-compressed data. The work focuses on text data and yields a unified high-performance library, called POCLib. In a ten-node distributed Spark cluster on Amazon EC2, POCLib achieves 3.1× speedup over the state-of-the-art on random data accesses to compressed data, while preserving the capability of supporting traversal operations efficiently and providing large (3.9×) space savings.
83 citations
TL;DR: In this article , the authors used 17 physical qubits in a superconducting circuit to encode quantum information in a distance-three logical qubit and demonstrated the preservation of four cardinal states of the qubit.
Abstract: Quantum computers hold the promise of solving computational problems that are intractable using conventional methods1. For fault-tolerant operation, quantum computers must correct errors occurring owing to unavoidable decoherence and limited control accuracy2. Here we demonstrate quantum error correction using the surface code, which is known for its exceptionally high tolerance to errors3-6. Using 17 physical qubits in a superconducting circuit, we encode quantum information in a distance-three logical qubit, building on recent distance-two error-detection experiments7-9. In an error-correction cycle taking only 1.1 μs, we demonstrate the preservation of four cardinal states of the logical qubit. Repeatedly executing the cycle, we measure and decode both bit-flip and phase-flip error syndromes using a minimum-weight perfect-matching algorithm in an error-model-free approach and apply corrections in post-processing. We find a low logical error probability of 3% per cycle when rejecting experimental runs in which leakage is detected. The measured characteristics of our device agree well with a numerical model. Our demonstration of repeated, fast and high-performance quantum error-correction cycles, together with recent advances in ion traps10, support our understanding that fault-tolerant quantum computation will be practically realizable.
81 citations
TL;DR: Pore-scale modeling is an efficient tool for the simulation of porescale transport and reactions in porous media because of its ability to accurately characterize these processes and to provide the distribution details of important variables which are challenging for current experimental techniques to provide either due to lack of in-situ measurement capability or due to limited spatial and temporal resolution as discussed by the authors.
72 citations
Paul Scherrer Institute1, ETH Zurich2, University of Calgary3, Utrecht University4, Massachusetts Institute of Technology5, University of Sheffield6, University of Aberdeen7, Potsdam Institute for Climate Impact Research8, University of Texas at Austin9, Polytechnic University of Milan10, Heriot-Watt University11
TL;DR: In this paper, the authors provide a balanced perspective on the impacts on climate change associated with blue hydrogen and show that such impacts may indeed vary over large ranges and depend on only a few key parameters: the methane emission rate of the natural gas supply chain, the CO2 removal rate at the hydrogen production plant, and the global warming metric applied.
Abstract: Natural gas based hydrogen production with carbon capture and storage is referred to as blue hydrogen. If substantial amounts of CO2 from natural gas reforming are captured and permanently stored, such hydrogen could be a low-carbon energy carrier. However, recent research raises questions about the effective climate impacts of blue hydrogen from a life cycle perspective. Our analysis sheds light on the relevant issues and provides a balanced perspective on the impacts on climate change associated with blue hydrogen. We show that such impacts may indeed vary over large ranges and depend on only a few key parameters: the methane emission rate of the natural gas supply chain, the CO2 removal rate at the hydrogen production plant, and the global warming metric applied. State-of-the-art reforming with high CO2 capture rates combined with natural gas supply featuring low methane emissions does indeed allow for substantial reduction of greenhouse gas emissions compared to both conventional natural gas reforming and direct combustion of natural gas. Under such conditions, blue hydrogen is compatible with low-carbon economies and exhibits climate change impacts at the upper end of the range of those caused by hydrogen production from renewable-based electricity. However, neither current blue nor green hydrogen production pathways render fully “net-zero” hydrogen without additional CO2 removal.
72 citations
TL;DR: In this paper, the micro laser lap welding method formed a lap joint of NiTiNb shape memory alloys (SMAs) to Ti6Al4V alloy with a Ni interlayer.
61 citations
TL;DR: In this article , the micro laser lap welding method formed a lap joint of NiTiNb shape memory alloys (SMAs) to Ti6Al4V alloy with a Ni interlayer.
54 citations
TL;DR: In this paper, a hybrid framework for fusing the information from physics-based performance models with deep learning algorithms for prognostics of complex safety-critical systems is proposed, which is based on a calibration problem to infer unobservable model parameters related to a system's components health.
50 citations
TL;DR: Li et al. as discussed by the authors developed a durable hollow core-shell TiO2@LaFeO3 (TLFO) nanosphere via facile carbon-sphere-templated method and sol-gel process, and applied it as heterojunction photocatalyst coupled with peroxymonosulfate (PMS) for efficient ATZ removal via enhanced electron-transfer.
Abstract: Insufficient charge-carriers separation and deteriorated recycling are still bottlenecks limiting practical photocatalytic water purification. Herein, we developed a durable hollow core-shell TiO2@LaFeO3 (TLFO) nanosphere via facile carbon-sphere-templated method and sol-gel process, and applied it as heterojunction photocatalyst coupled with peroxymonosulfate (PMS) for efficient atrazine (ATZ) removal via enhanced electron-transfer. The built-in electric field originated from the three-dimensional heterojunction between TiO2 and LaFeO3, acting as charge transfer driving force, enhanced the charge separation rate. Meanwhile, PMS could function as electron acceptor to boost photogenerated charge separation and maximize reactive oxidant species (e.g., •OH, SO4•−, O2•− and 1O2) production. Therefore, the fabricated TLFO heterojunction exhibited outstanding reusability, and superior ATZ removal efficiency without detectable metal ion leaching. This work successfully demonstrates the synergistic effect and superior hollow structure of TLFO heterojunction with promoted light utilization and PMS activation, which offers potential application for efficient abating environmental pollution using solar energy.
TL;DR: In this paper, a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks (CNN) is proposed to regress canopy top height globally, and the model learns to extract robust features that generalize to unseen geographical regions and yields reliable estimates of predictive uncertainty.
TL;DR: In this article, a two-dimensional MXene was first functionalized with phosphorylated chitosan (PCS) to obtain the PCS-MXene nanohybrid.
TL;DR: In this article , the authors assess the impact of COVID-19 on world scientific production in the life sciences and find indications that the usage of medical subject headings (MeSH) has changed following the outbreak.
Abstract: The COVID-19 outbreak has posed an unprecedented challenge to humanity and science. On the one side, public and private incentives have been put in place to promptly allocate resources toward research areas strictly related to the COVID-19 emergency. However, research in many fields not directly related to the pandemic has been displaced. In this paper, we assess the impact of COVID-19 on world scientific production in the life sciences and find indications that the usage of medical subject headings (MeSH) has changed following the outbreak. We estimate through a difference-in-differences approach the impact of the start of the COVID-19 pandemic on scientific production using the PubMed database (3.6 Million research papers). We find that COVID-19-related MeSH terms have experienced a 6.5 fold increase in output on average, while publications on unrelated MeSH terms dropped by 10 to 12%. The publication weighted impact has an even more pronounced negative effect (-16% to -19%). Moreover, COVID-19 has displaced clinical trial publications (-24%) and diverted grants from research areas not closely related to COVID-19. Note that since COVID-19 publications may have been fast-tracked, the sudden surge in COVID-19 publications might be driven by editorial policy.
TL;DR: The numerical study and the experimental data both demonstrate that the proposed automated tuning method is efficient in terms of required tuning iterations, robust to disturbances, and results in improved tracking.
Abstract: In this article, we propose a performance-based autotuning method for cascade control systems, where the parameters of a linear axis drive motion controller from two control loops are tuned jointly. Using Bayesian optimization as all parameters are tuned simultaneously, the method is guaranteed to converge asymptotically to the global optimum of the cost. The data-efficiency and performance of the method are studied numerically for several training configurations and compared numerically to those achieved with classical tuning methods and to the exhaustive evaluation of the cost. On the real system, the tracking performance and robustness against disturbances are compared experimentally to nominal tuning. The numerical study and the experimental data both demonstrate that the proposed automated tuning method is efficient in terms of required tuning iterations, robust to disturbances, and results in improved tracking.
TL;DR: In this article, the authors review methods and assumptions for analysing geographical, technical, economic and, finally, feasible onshore wind potentials, including aspects related to land eligibility criteria, energy meteorology, and technical developments relating to wind turbine characteristics such as power density, specific rotor power and spacing aspects.
TL;DR: In this article , a comprehensive review of recent advances in the biomass-based aerogels field is provided, which ranges from their structural design to routes for functional tailoring, and encompasses resource-efficient applications.
TL;DR: In this paper , the authors report a nearquantitative (up to 92%) and catalyst-free depolymerization of various linear, bulky, cross-linked, and functional polymethacrylates made by reversible addition-fragmentation chain-transfer (RAFT) polymerization.
Abstract: The ability to reverse controlled radical polymerization and regenerate the monomer would be highly beneficial for both fundamental research and applications, yet this has remained very challenging to achieve. Herein, we report a near-quantitative (up to 92%) and catalyst-free depolymerization of various linear, bulky, cross-linked, and functional polymethacrylates made by reversible addition-fragmentation chain-transfer (RAFT) polymerization. Key to our approach is to exploit the high end-group fidelity of RAFT polymers to generate chain-end radicals at 120 °C. These radicals trigger a rapid unzipping of both conventional (e.g., poly(methyl methacrylate)) and bulky (e.g., poly(oligo(ethylene glycol) methyl ether methacrylate)) polymers. Importantly, the depolymerization product can be utilized to either reconstruct the linear polymer or create an entirely new insoluble gel that can also be subjected to depolymerization. This work expands the potential of polymers made by controlled radical polymerization, pushes the boundaries of depolymerization, offers intriguing mechanistic aspects, and enables new applications.
TL;DR: In this article, a hierarchical graph neural network is proposed to operate on the hierarchical entity-graph and map the tissue structure to tissue functionality, treating the tissue as a hierarchical composition of multiple types of histological entities from fine to coarse level.
TL;DR: Following a taxonomy development approach, 22 empirically and conceptually grounded design dimensions contingent on chatbots’ temporal profiles are compiled, and three time-dependent chatbot design archetypes are abstracted: Ad-hoc Supporters, Temporary Assistants, and Persistent Companions.
TL;DR: In this article , the authors collected a large dataset with matching GPS tracks, booking data and survey data for more than 500 travellers, and by estimating a first choice model between eight transport modes, including shared e-scooters, e-bikes, shared scooters, personal scooters and personal scooter, they found that trip distance, precipitation and access distance are fundamental to micro-mobility mode choice.
Abstract: Shared micro-mobility services are rapidly expanding yet little is known about travel behaviour. Understanding mode choice, in particular, is quintessential for incorporating micro-mobility into transport simulations in order to enable effective transport planning. We contribute by collecting a large dataset with matching GPS tracks, booking data and survey data for more than 500 travellers, and by estimating a first choice model between eight transport modes, including shared e-scooters, shared e-bikes, personal e-scooters and personal e-bikes. We find that trip distance, precipitation and access distance are fundamental to micro-mobility mode choice. Substitution patterns reveal that personal e-scooters and e-bikes emit less CO2 than the transport modes they replace, while shared e-scooters and e-bikes emit more CO2 than the transport modes they replace. Our results enable researchers and planners to test the effectiveness of policy interventions through transport simulations. Service providers can use our findings on access distances to optimize vehicle repositioning.
TL;DR: In this article , the authors studied how the COVID-19 pandemic affected travel behavior of public transport users, focusing on route choice and recurrent trips, in terms of travel distance, mode share and location during the day.
TL;DR: In this paper, the authors model the entire process of data generation to processing, model updating and reliability calculation, and investigate it on a deteriorating bridge system, assuming that dynamic response data are obtained in a sequential fashion from deployed accelerometers, subsequently processed by an output-only operational modal analysis scheme for identifying the system's modal characteristics.
TL;DR: In this paper, the authors provide a comprehensive insight into additive manufacturing process mechanics, which explore how thermodynamics give rise to characteristic microstructure and property, and how solidification micro-structure is modified using processing conditions and utilisation of traditional casting methods.
TL;DR: In this article, the authors proposed the connection of the digital building twin with blockchain-based smart contracts to execute performance-based digital payments, and demonstrated the feasibility of both the concept and technical architecture by integrating the Ethereum blockchain with digital building models and sensors via the Siemens building twin platform.
TL;DR: In this article, a Canny edge-based crack detector was proposed to detect cracks in large-scale homogeneous concrete element experiments, and two approaches for the statistical consolidation of the large amount of gathered data into characteristic crack properties from the full-field DIC displacements were proposed.
TL;DR: In this article , the effect of vacuum field fluctuations in subwavelength split-ring resonators on the quantum Hall electron transport in high-mobility two-dimensional electron gases is investigated.
Abstract: The prospect of controlling the electronic properties of materials via the vacuum fields of cavity electromagnetic resonators is emerging as one of the frontiers of condensed matter physics. We found that the enhancement of vacuum field fluctuations in subwavelength split-ring resonators strongly affects one of the most paradigmatic quantum protectorates, the quantum Hall electron transport in high-mobility two-dimensional electron gases. The observed breakdown of the topological protection of the integer quantum Hall effect is interpreted in terms of a long-range cavity-mediated electron hopping where the anti-resonant terms of the light-matter coupling Hamiltonian develop into a finite resistivity induced by the vacuum fluctuations. Our experimental platform can be used for any two-dimensional material and provides a route to manipulate electron phases in matter by means of vacuum-field engineering.
TL;DR: In this paper , an atomic Pd-promoted ZnZrOx solid solution catalyst (Pd-Zn-Zr Oxox) was proposed for CO2 conversion to methanol.
Abstract: The development of efficient CO2 conversion catalysts is a long-lasting desire. Herein, we introduce an atomic Pd-promoted ZnZrOx solid solution catalyst (Pd-ZnZrOx), which shows markedly enhanced rate of methanol production compared to bare ZnZrOx, as well as excellent stability over 100 h on stream. Up to 0.8 at% (i.e. 0.6 wt%), Pd can be atomically dispersed in ZnZrOx, leading to more oxygen vacancies on the mixed oxide that foster methanol production. Kinetic analysis and in situ DRIFTS reveal that hydrogen activation is limited on ZnZrOx, but Pd doping facilitates H2 dissociation as well as the consequent formation of HCOO*, thus boosting CO2 conversion to methanol. DFT analyses suggest that the presence of atomic Pd enables a more exothermic H2 dissociation, which increases the availability of surface H and facilitates CO2 hydrogenation on adjacent Zn sites, providing rationale on the high activity and robustness of Pd-ZnZrOx in CO2 hydrogenation.
TL;DR: MSNovelist as mentioned in this paper combines fingerprint prediction with an encoder-decoder neural network to generate structures de novo solely from tandem mass spectrometry (MS 2 ) spectra.
Abstract: Abstract Current methods for structure elucidation of small molecules rely on finding similarity with spectra of known compounds, but do not predict structures de novo for unknown compound classes. We present MSNovelist, which combines fingerprint prediction with an encoder–decoder neural network to generate structures de novo solely from tandem mass spectrometry (MS 2 ) spectra. In an evaluation with 3,863 MS 2 spectra from the Global Natural Product Social Molecular Networking site, MSNovelist predicted 25% of structures correctly on first rank, retrieved 45% of structures overall and reproduced 61% of correct database annotations, without having ever seen the structure in the training phase. Similarly, for the CASMI 2016 challenge, MSNovelist correctly predicted 26% and retrieved 57% of structures, recovering 64% of correct database annotations. Finally, we illustrate the application of MSNovelist in a bryophyte MS 2 dataset, in which de novo structure prediction substantially outscored the best database candidate for seven spectra. MSNovelist is ideally suited to complement library-based annotation in the case of poorly represented analyte classes and novel compounds.