Showing papers by "Delft University of Technology published in 2021"

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

A Review of Domain Adaptation without Target Labels

Abstract: Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: How can a classifier learn from a source domain and generalize to a target domain? We present a categorization of approaches, divided into, what we refer to as, sample-based , feature-based, and inference-based methods. Sample-based methods focus on weighting individual observations during training based on their importance to the target domain. Feature-based methods revolve around on mapping, projecting, and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure. Additionally, we review a number of conditions that allow for formulating bounds on the cross-domain generalization error. Our categorization highlights recurring ideas and raises questions important to further research.

Review on ammonia as a potential fuel: from synthesis to economics

Abstract: Ammonia, a molecule that is gaining more interest as a fueling vector, has been considered as a candidate to power transport, produce energy, and support heating applications for decades. However, the particular characteristics of the molecule always made it a chemical with low, if any, benefit once compared to conventional fossil fuels. Still, the current need to decarbonize our economy makes the search of new methods crucial to use chemicals, such as ammonia, that can be produced and employed without incurring in the emission of carbon oxides. Therefore, current efforts in this field are leading scientists, industries, and governments to seriously invest efforts in the development of holistic solutions capable of making ammonia a viable fuel for the transition toward a clean future. On that basis, this review has approached the subject gathering inputs from scientists actively working on the topic. The review starts from the importance of ammonia as an energy vector, moving through all of the steps in the production, distribution, utilization, safety, legal considerations, and economic aspects of the use of such a molecule to support the future energy mix. Fundamentals of combustion and practical cases for the recovery of energy of ammonia are also addressed, thus providing a complete view of what potentially could become a vector of crucial importance to the mitigation of carbon emissions. Different from other works, this review seeks to provide a holistic perspective of ammonia as a chemical that presents benefits and constraints for storing energy from sustainable sources. State-of-the-art knowledge provided by academics actively engaged with the topic at various fronts also enables a clear vision of the progress in each of the branches of ammonia as an energy carrier. Further, the fundamental boundaries of the use of the molecule are expanded to real technical issues for all potential technologies capable of using it for energy purposes, legal barriers that will be faced to achieve its deployment, safety and environmental considerations that impose a critical aspect for acceptance and wellbeing, and economic implications for the use of ammonia across all aspects approached for the production and implementation of this chemical as a fueling source. Herein, this work sets the principles, research, practicalities, and future views of a transition toward a future where ammonia will be a major energy player.

More than 1000 rivers account for 80% of global riverine plastic emissions into the ocean

Abstract: Plastic waste increasingly accumulates in the marine environment, but data on the distribution and quantification of riverine sources required for development of effective mitigation are limited. Our model approach includes geographically distributed data on plastic waste, land use, wind, precipitation, and rivers and calculates the probability for plastic waste to reach a river and subsequently the ocean. This probabilistic approach highlights regions that are likely to emit plastic into the ocean. We calibrated our model using recent field observations and show that emissions are distributed over more rivers than previously thought by up to two orders of magnitude. We estimate that more than 1000 rivers account for 80% of global annual emissions, which range between 0.8 million and 2.7 million metric tons per year, with small urban rivers among the most polluting. These high-resolution data allow for the focused development of mitigation strategies and technologies to reduce riverine plastic emissions.

A four-qubit germanium quantum processor

Abstract: The prospect of building quantum circuits1,2 using advanced semiconductor manufacturing makes quantum dots an attractive platform for quantum information processing3,4. Extensive studies of various materials have led to demonstrations of two-qubit logic in gallium arsenide5, silicon6–12 and germanium13. However, interconnecting larger numbers of qubits in semiconductor devices has remained a challenge. Here we demonstrate a four-qubit quantum processor based on hole spins in germanium quantum dots. Furthermore, we define the quantum dots in a two-by-two array and obtain controllable coupling along both directions. Qubit logic is implemented all-electrically and the exchange interaction can be pulsed to freely program one-qubit, two-qubit, three-qubit and four-qubit operations, resulting in a compact and highly connected circuit. We execute a quantum logic circuit that generates a four-qubit Greenberger−Horne−Zeilinger state and we obtain coherent evolution by incorporating dynamical decoupling. These results are a step towards quantum error correction and quantum simulation using quantum dots. Using germanium quantum dots, a four-qubit processor capable of single-, two-, three-, and four-qubit gates, demonstrated by the creation of four-qubit Greenberger−Horne−Zeilinger states, is the largest yet realized with solid-state electron spins.

Comparative cellular analysis of motor cortex in human, marmoset and mouse

Abstract: The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.

A Review on Soft Sensors for Monitoring, Control, and Optimization of Industrial Processes

Abstract: Over the past twenty years, numerous research outcomes have been published, related to the design and implementation of soft sensors. In modern industrial processes, various types of soft sensors are used, which play essential roles in process monitoring, control and optimization. Emerging new theories, advanced techniques and the information infrastructure have enabled the elevation of the performance of soft sensing. However, novel opportunities are accompanied by novel challenges. This work is motivated by these observations and aims to present a comprehensive review of the developments since the start of the millennium. While a few books and review articles are published on the related topics, more focus on the most up-to-the-date advancement is put in this work, from the perspective of systems and control.

Realization of a multinode quantum network of remote solid-state qubits.

Abstract: The distribution of entangled states across the nodes of a future quantum internet will unlock fundamentally new technologies. Here, we report on the realization of a three-node entanglement-based quantum network. We combine remote quantum nodes based on diamond communication qubits into a scalable phase-stabilized architecture, supplemented with a robust memory qubit and local quantum logic. In addition, we achieve real-time communication and feed-forward gate operations across the network. We demonstrate two quantum network protocols without postselection: the distribution of genuine multipartite entangled states across the three nodes and entanglement swapping through an intermediary node. Our work establishes a key platform for exploring, testing, and developing multinode quantum network protocols and a quantum network control stack.

The MLIP package: moment tensor potentials with MPI and active learning

Abstract: The subject of this paper is the technology (the "how") of constructing machine-learning interatomic potentials, rather than science (the "what" and "why") of atomistic simulations using machine-learning potentials. Namely, we illustrate how to construct moment tensor potentials using active learning as implemented in the MLIP package, focusing on the efficient ways to sample configurations for the training set, how expanding the training set changes the error of predictions, how to set up ab initio calculations in a cost-effective manner, etc. The MLIP package (short for Machine-Learning Interatomic Potentials) is available at this https URL.

The 2021 Magnonics Roadmap.

Abstract: Magnonics is a rather young physics research field in nanomagnetism and nanoscience that addresses the use of spin waves (magnons) to transmit, store, and process information. After several papers and review articles published in the last decade, with a steadily increase in the number of citations, we are presenting the first Roadmap on Magnonics. This a collection of 22 sections written by leading experts in this field who review and discuss the current status but also present their vision of future perspectives. Today, the principal challenges in applied magnonics are the excitation of sub-100 nm wavelength magnons, their manipulation on the nanoscale and the creation of sub-micrometre devices using low-Gilbert damping magnetic materials and the interconnections to standard electronics. In this respect, magnonics offers lower energy consumption, easier integrability and compatibility with CMOS structure, reprogrammability, shorter wavelength, smaller device features, anisotropic properties, negative group velocity, non-reciprocity and efficient tunability by various external stimuli to name a few. Hence, despite being a young research field, magnonics has come a long way since its early inception. This Roadmap represents a milestone for future emerging research directions in magnonics and hopefully it will be followed by a series of articles on the same topic.

The trans-ancestral genomic architecture of glycemic traits

Abstract: Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10-8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.

Attribution of the Australian bushfire risk to anthropogenic climate change

Abstract: . Disastrous bushfires during the last months of 2019 and January 2020 affected Australia, raising the question to what extent the risk of these fires was exacerbated by anthropogenic climate change. To answer the question for southeastern Australia, where fires were particularly severe, affecting people and ecosystems, we use a physically based index of fire weather, the Fire Weather Index; long-term observations of heat and drought; and 11 large ensembles of state-of-the-art climate models. We find large trends in the Fire Weather Index in the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5) since 1979 and a smaller but significant increase by at least 30 % in the models. Therefore, we find that climate change has induced a higher weather-induced risk of such an extreme fire season. This trend is mainly driven by the increase of temperature extremes. In agreement with previous analyses we find that heat extremes have become more likely by at least a factor of 2 due to the long-term warming trend. However, current climate models overestimate variability and tend to underestimate the long-term trend in these extremes, so the true change in the likelihood of extreme heat could be larger, suggesting that the attribution of the increased fire weather risk is a conservative estimate. We do not find an attributable trend in either extreme annual drought or the driest month of the fire season, September–February. The observations, however, show a weak drying trend in the annual mean. For the 2019/20 season more than half of the July–December drought was driven by record excursions of the Indian Ocean Dipole and Southern Annular Mode, factors which are included in the analysis here. The study reveals the complexity of the 2019/20 bushfire event, with some but not all drivers showing an imprint of anthropogenic climate change. Finally, the study concludes with a qualitative review of various vulnerability and exposure factors that each play a role, along with the hazard in increasing or decreasing the overall impact of the bushfires.

Machine learning pipeline for battery state-of-health estimation

Abstract: Lithium-ion batteries are ubiquitous in applications ranging from portable electronics to electric vehicles Irrespective of the application, reliable real-time estimation of battery state of health (SOH) by on-board computers is crucial to the safe operation of the battery, ultimately safeguarding asset integrity In this Article, we design and evaluate a machine learning pipeline for estimation of battery capacity fade—a metric of battery health—on 179 cells cycled under various conditions The pipeline estimates battery SOH with an associated confidence interval by using two parametric and two non-parametric algorithms Using segments of charge voltage and current curves, the pipeline engineers 30 features, performs automatic feature selection and calibrates the algorithms When deployed on cells operated under the fast-charging protocol, the best model achieves a root-mean-squared error of 045% This work provides insights into the design of scalable data-driven models for battery SOH estimation, emphasizing the value of confidence bounds around the prediction The pipeline methodology combines experimental data with machine learning modelling and could be applied to other critical components that require real-time estimation of SOH Rechargeable lithium-ion batteries play a crucial role in many modern-day applications, including portable electronics and electric vehicles, but they degrade over time To ensure safe operation, a battery’s ‘state of health’ should be monitored in real time, and this machine learning pipeline, tested on a variety of charging conditions, can provide such an online estimation of battery state of health

Offshore wind turbine operations and maintenance: A state-of-the-art review

Abstract: Operations and maintenance of offshore wind turbines (OWTs) play an important role in the development of offshore wind farms. Compared with operations, maintenance is a critical element in the levelized cost of energy, given the practical constraints imposed by offshore operations and the relatively high costs. The effects of maintenance on the life cycle of an offshore wind farm are highly complex and uncertain. The selection of maintenance strategies influences the overall efficiency, profit margin, safety, and sustainability of offshore wind farms. For an offshore wind project, after a maintenance strategy is selected, schedule planning will be considered, which is an optimization problem. Onsite maintenance will involve complex marine operations whose efficiency and safety depend on practical factors. Moreover, negative environmental impacts due to offshore maintenance deserve attention. To address these issues, this paper reviews the state-of-the-art research on OWT maintenance, covering strategy selection, schedule optimization, onsite operations, repair, assessment criteria, recycling, and environmental concerns. Many methods are summarized and compared. Limitations in the research and shortcomings in industrial development of OWT operations and maintenance are described. Finally, promising areas are identified with regard to future studies of maintenance strategies.

Multiple rereads of single proteins at single–amino acid resolution using nanopores

Abstract: A proteomics tool capable of identifying single proteins would be important for cell biology research and applications. Here, we demonstrate a nanopore-based single-molecule peptide reader sensitiv...

An omics-based framework for assessing the health risk of antimicrobial resistance genes

Abstract: Antibiotic resistance genes (ARGs) are widespread among bacteria. However, not all ARGs pose serious threats to public health, highlighting the importance of identifying those that are high-risk. Here, we developed an ‘omics-based’ framework to evaluate ARG risk considering human-associated-enrichment, gene mobility, and host pathogenicity. Our framework classifies human-associated, mobile ARGs (3.6% of all ARGs) as the highest risk, which we further differentiate as ‘current threats’ (Rank I; 3%) - already present among pathogens - and ‘future threats’ (Rank II; 0.6%) - novel resistance emerging from non-pathogens. Our framework identified 73 ‘current threat’ ARG families. Of these, 35 were among the 37 high-risk ARGs proposed by the World Health Organization and other literature; the remaining 38 were significantly enriched in hospital plasmids. By evaluating all pathogen genomes released since framework construction, we confirmed that ARGs that recently transferred into pathogens were significantly enriched in Rank II (‘future threats’). Lastly, we applied the framework to gut microbiome genomes from fecal microbiota transplantation donors. We found that although ARGs were widespread (73% of genomes), only 8.9% of genomes contained high-risk ARGs. Our framework provides an easy-to-implement approach to identify current and future antimicrobial resistance threats, with potential clinical applications including reducing risk of microbiome-based interventions. Antibiotic resistance genes are common but not all are of high risk to human health. Here, the authors develop an omics-based framework for ranking genes by risk that incorporates level of enrichment in human associated environments, gene mobility, and host pathogenicity.

Role of the Carbon-Based Gas Diffusion Layer on Flooding in a Gas Diffusion Electrode Cell for Electrochemical CO 2 Reduction

Abstract: The deployment of gas diffusion electrodes (GDEs) for the electrochemical CO2 reduction reaction (CO2RR) has enabled current densities an order of magnitude greater than those of aqueous H cells. The gains in production, however, have come with stability challenges due to rapid flooding of GDEs, which frustrate both laboratory experiments and scale-up prospects. Here, we investigate the role of carbon gas diffusion layers (GDLs) in the advent of flooding during CO2RR, finding that applied potential plays a central role in the observed instabilities. Electrochemical characterization of carbon GDLs with and without catalysts suggests that the high overpotential required during electrochemical CO2RR initiates hydrogen evolution on the carbon GDL support. These potentials impact the wetting characteristics of the hydrophobic GDL, resulting in flooding that is independent of CO2RR. Findings from this work can be extended to any electrochemical reduction reaction using carbon-based GDEs (CORR or N2RR) with cathodic overpotentials of less than -0.65 V versus a reversible hydrogen electrode.

Coupling electrochemical CO2 conversion with CO2 capture

Abstract: Electrochemical CO2 conversion into fuels or chemicals and CO2 capture from point or dilute sources are two important processes to address the gigaton challenges in reducing greenhouse gas emissions. Both CO2 capture and electrochemical CO2 conversion are energy intensive, and synergistic coupling between the two processes can improve the energy efficiency of the system and reduce the cost of the reduced products, via eliminating the CO2 transport and storage or eliminating the capture media regeneration and molecular CO2 release. We consider three different levels to couple electrochemical CO2 reduction with CO2 capture: independent (Type-I), subsequent (Type-II) and fully integrated (Type-III) capture and conversion processes. We focus on Type-II and Type-III configurations and illustrate potential coupling routes of different capture media, which include amine-based solutions and direct carbamate reduction, redox active carriers, aqueous carbonate and bicarbonate solutions, ionic liquids CO2 capture and conversion mediated by covalent organic frameworks. The practical implementation of CO2 electrocatalysis is premised on the availability of captured CO2—a consideration that is often overlooked. This Perspective presents several concepts for integrating CO2 capture with electrochemical CO2 conversion for the enhancement of overall efficiency.

Electrochemical carbon dioxide capture to close the carbon cycle

Abstract: Electrochemical CO2 capture technologies are gaining attention due to their flexibility, their ability to address decentralized emissions (e.g., ocean and atmosphere) and their fit in an electrified industry. In the present work, recent progress made in electrochemical CO2 capture is reviewed. The majority of these methods rely on the concept of “pH-swing” and the effect it has on the CO2 hydration/dehydration equilibrium. Through a pH-swing, CO2 can be captured and recovered by shifting the pH of a working fluid between acidic and basic pH. Such swing can be applied electrochemically through electrolysis, bipolar membrane electrodialysis, reversible redox reactions and capacitive deionization. In this review, we summarize main parameters governing these electrochemical pH-swing processes and put the concept in the framework of available worldwide capture technologies. We analyse the energy efficiency and consumption of such systems, and provide recommendations for further improvements. Although electrochemical CO2 capture technologies are rather costly compared to the amine based capture, they can be particularly interesting if more affordable renewable electricity and materials (e.g., electrode and membranes) become widely available. Furthermore, electrochemical methods have the ability to (directly) convert the captured CO2 to value added chemicals and fuels, and hence prepare for a fully electrified circular carbon economy.

MFEM: a modular finite element methods library

Abstract: MFEM is an open-source, lightweight, flexible and scalable C++ library for modular finite element methods that features arbitrary high-order finite element meshes and spaces, support for a wide variety of discretization approaches and emphasis on usability, portability, and high-performance computing efficiency. MFEM’s goal is to provide application scientists with access to cutting-edge algorithms for high-order finite element meshing, discretizations and linear solvers, while enabling researchers to quickly and easily develop and test new algorithms in very general, fully unstructured, high-order, parallel and GPU-accelerated settings. In this paper we describe the underlying algorithms and finite element abstractions provided by MFEM, discuss the software implementation, and illustrate various applications of the library.

Observation of flat bands in twisted bilayer graphene

Abstract: Transport experiments in twisted bilayer graphene have revealed multiple superconducting domes separated by correlated insulating states1–5. These properties are generally associated with strongly correlated states in a flat mini-band of the hexagonal moire superlattice as was predicted by band structure calculations6–8. Evidence for the existence of a flat band comes from local tunnelling spectroscopy9–13 and electronic compressibility measurements14, which report two or more sharp peaks in the density of states that may be associated with closely spaced Van Hove singularities. However, direct momentum-resolved measurements have proved to be challenging15. Here, we combine different imaging techniques and angle-resolved photoemission with simultaneous real- and momentum-space resolution (nano-ARPES) to directly map the band dispersion in twisted bilayer graphene devices near charge neutrality. Our experiments reveal large areas with a homogeneous twist angle that support a flat band with a spectral weight that is highly localized in momentum space. The flat band is separated from the dispersive Dirac bands, which show multiple moire hybridization gaps. These data establish the salient features of the twisted bilayer graphene band structure. Spectroscopic measurements using nano-ARPES on twisted bilayer graphene directly highlight the presence of the flat bands.

The emerging landscape of single-molecule protein sequencing technologies

Abstract: Single-cell profiling methods have had a profound impact on the understanding of cellular heterogeneity. While genomes and transcriptomes can be explored at the single-cell level, single-cell profiling of proteomes is not yet established. Here we describe new single-molecule protein sequencing and identification technologies alongside innovations in mass spectrometry that will eventually enable broad sequence coverage in single-cell profiling. These technologies will in turn facilitate biological discovery and open new avenues for ultrasensitive disease diagnostics. This Perspective describes new single-molecule protein sequencing and identification technologies alongside innovations in mass spectrometry that will eventually enable broad sequence coverage in single-cell proteomics.

Grid Impact of Electric Vehicle Fast Charging Stations: Trends, Standards, Issues and Mitigation Measures - An Overview

Abstract: With growing concern on climate change, widespread adoption of electric vehicles (EVs) is important. One of the main barriers to EV acceptance is range anxiety, which can be alleviated by fast charging (FC). The main technology constraints for enabling FC consist of high-charging-rate batteries, high-power-charging infrastructure, and grid impacts. Although these technical aspects have been studied in literature individually, there is no comprehensive review on FC involving all the perspectives. Moreover, the power quality (PQ) problems of fast charging stations (FCSs) and the mitigation of these problems are not clearly summarized in the literature. In this paper, the state-of-the-art technology, standards for FC (CHAdeMO, GB/T, CCS, and Tesla), power quality issues, IEEE and IEC PQ standards, and mitigation measures of FCSs are systematically reviewed.

Use of fine recycled concrete aggregates in concrete: A critical review

Abstract: This paper discusses the state-of-the-art of the fine recycled concrete aggregates (fRCA), focusing on their physical and chemical properties, engineering properties and durability of concretes with fRCA. Based on the systematic review of the published literature, it is impossible to deduce without any further research the guidelines and tools to introduce the widespread application of the fRCA in new concrete whilst keeping the cement contents at least the same or preferably lower. Namely, what is still missing is knowledge on key physico-chemical properties and their relation to the quality of the concrete mix and the concrete performance. This paper sets the foundations for better understanding the quality of fRCA obtained either from parent concrete specifically produced in the laboratory, with controlled crushing and sieving of the recycled aggregates or from field structures. By comparing properties of fRCA with properties of fine natural aggregates, the key limiting properties of fRCA are identified as the high water absorption of fRCA, moisture state of fRCA, agglomeration of particles and adhered mortar. As such, continuous quality of fRCA is hard to be obtained, even though they may be more continuous in terms of chemistry. Advanced characterization techniques and concrete technology tools are needed to account for limiting properties of fRCA in concrete mix design.