Low Thermal Conductivity through Dense Particle Packings with Optimum Disorder

Heat transport plays a critical role in modern batteries, electrodes, and capacitors. This is caused by the ongoing miniaturization of such nanotechnological devices, which increases the local power density and hence temperature. Even worse, the introduction of heterostructures and interfaces is often accompanied by a reduction in thermal conductivity, which can ultimately lead to the failure of the entire device. Surprisingly, a fundamental understanding of the governing heat transport processes even in simple systems, such as binary particle mixtures is still missing. This contribution closes this gap and elucidates how strongly the polydispersity of a model particulate system influences the effective thermal conductivity across such a heterogeneous system. In a combined experimental and modeling approach, well‐defined mixtures of monodisperse particles with varying size ratios are investigated. The transition from order to disorder can reduce the effective thermal conductivity by as much as ≈50%. This is caused by an increase in the thermal transport path length and is governed by the number of interparticle contact points. These results are of general importance for many particulate and heterostructured materials and will help to conceive improved device layouts with more reliable heat dissipation or conservation properties in the future.     

Insights into Electrolytic Pre-Lithiation: A Thorough Analysis Using Silicon Thin Film Anodes

Pre-lithiation via electrolysis, herein defined as electrolytic pre-lithiation, using cost-efficient electrolytes based on lithium chloride (LiCl), is successfully demonstrated as a proof-of-concept for enabling lithium-ion battery full-cells with high silicon content negative electrodes. An electrolyte for pre-lithiation based on γ-butyrolactone and LiCl is optimized using boron-containing additives (lithium bis(oxalato)borate, lithium difluoro(oxalate)borate) and CO₂ with respect to the formation of a protective solid electrolyte interphase (SEI) on silicon thin films as model electrodes. Reversible lithiation in Si||Li metal cells is demonstrated with Coulombic efficiencies (C_Eff) of 95–96% for optimized electrolytes comparable to 1 m LiPF₆/EC:EMC 3:7. Formation of an effective SEI is shown by cyclic voltammetry and X-ray photoelectron spectroscopy (XPS). electrolytic pre-lithiation experiments show that notable amounts of the gaseous product Cl₂ dissolve in the electrolyte leading to a self-discharge Cl₂/Cl⁻ shuttle mechanism between the electrodes lowering pre-lithiation efficiency and causing current collector corrosion. However, no significant degradation of the Si active material and the SEI due to contact with elemental chlorine is found by SEM, impedance, and XPS. In NCM111||Si full-cells, the capacity retention in the 100th cycle can be significantly increased from 54% to 78% by electrolytic pre-lithiation, compared to reference cells without pre-lithiation of Si.     

Remotely Light‐Powered Soft Fluidic Actuators Based on Plasmonic‐Driven Phase Transitions in Elastic Constraint

Materials capable of actuation through remote stimuli are crucial for untethering soft robotic systems from hardware for powering and control. Fluidic actuation is one of the most applied and versatile actuation strategies in soft robotics. Here, the first macroscale soft fluidic actuator is derived that operates remotely powered and controlled by light through a plasmonically induced phase transition in an elastomeric constraint. A multiphase assembly of a liquid layer of concentrated gold nanoparticles in a silicone or styrene–ethylene–butylene–styrene elastic pocket forms the actuator. Upon laser excitation, the nanoparticles convert light of specific wavelength into heat and initiate a liquid‐to‐gas phase transition. The related pressure increase inflates the elastomers in response to laser wavelength, intensity, direction, and on–off pulses. During laser‐off periods, heating halts and condensation of the gas phase renders the actuation reversible. The versatile multiphase materials actuate—like soft “steam engines”—a variety of soft robotic structures (soft valve, pnue‐net structure, crawling robot, pump) and are capable of operating in different environments (air, water, biological tissue) in a single configuration. Tailored toward the near‐infrared window of biological tissue, the structures actuate also through animal tissue for potential medical soft robotic applications.     

Flexible data anonymization using ARX—Current status and challenges ahead

The race for innovation has turned into a race for data. Rapid developments of new technologies, especially in the field of artificial intelligence, are accompanied by new ways of accessing, integrating, and analyzing sensitive personal data. Examples include financial transactions, social network activities, location traces, and medical records. As a consequence, adequate and careful privacy management has become a significant challenge. New data protection regulations, for example in the EU and China, are direct responses to these developments. Data anonymization is an important building block of data protection concepts, as it allows to reduce privacy risks by altering data. The development of anonymization tools involves significant challenges, however. For instance, the effectiveness of different anonymization techniques depends on context, and thus tools need to support a large set of methods to ensure that the usefulness of data is not overly affected by risk-reducing transformations. In spite of these requirements, existing solutions typically only support a small set of methods. In this work, we describe how we have extended an open source data anonymization tool to support almost arbitrary combinations of a wide range of techniques in a scalable manner. We then review the spectrum of methods supported and discuss their compatibility within the novel framework. The results of an extensive experimental comparison show that our approach outperforms related solutions in terms of scalability and output data quality—while supporting a much broader range of techniques. Finally, we discuss practical experiences with ARX and present remaining issues and challenges ahead.     

The upside of data privacy – delighting customers by implementing data privacy measures

The targeted analysis of customer data becomes increasingly important for data-driven business models. At the same time, the customers’ concerns regarding data privacy have to be addressed properly. Existing research mostly describes data privacy as a necessary evil for compliance and risk management and does not propose specific data privacy measures which address the customers’ concerns. We therefore aim to shed light on the upside of data privacy. In this paper, we derive specific measures to deal with customers’ data privacy concerns based on academic literature, legislative texts, corporate privacy statements, and expert interviews. Next, we leverage the Kano model and data from two internet-based surveys to analyze the measures’ evaluation by customers. From a customer perspective, the implementation of the majority of measures is obligatory as those measures are considered as basic needs of must-be quality. However, delighting measures of attractive quality do exist and have the potential to create a competitive advantage. In this, we find some variation across different industries suggesting that corporations aiming to improve customer satisfaction by superior privacy protection should elicit the demands of their specific target customers.     

Visualizing Group Structures in Graphs: A Survey

Graph visualizations encode relationships between objects. Abstracting the objects into group structures provides an overview of the data. Groups can be disjoint or overlapping, and might be organized hierarchically. However, the underlying graph still needs to be represented for analyzing the data in more depth. This work surveys research in visualizing group structures as part of graph diagrams. A particular focus is the explicit visual encoding of groups, rather than only using graph layout to indicate groups implicitly. We introduce a taxonomy of visualization techniques structuring the field into four main categories: visual node attributes vary properties of the node representation to encode the grouping, juxtaposed approaches use two separate visualizations, superimposed techniques work with two aligned visual layers, and embedded visualizations tightly integrate group and graph representation. The derived taxonomies for group structure and visualization types are also applied to group visualizations of edges. We survey group‐only, group–node, group–edge and group–network tasks that are described in the literature as use cases of group visualizations. We discuss results from evaluations of existing visualization techniques as well as main areas of application. Finally, we report future challenges based on interviews we conducted with leading researchers of the field.     

Comparison of Olefin Polymerization Processes with Metallocene Catalysts

Summary: Solution and gas phase processes of the polymerization of ethene are compared using new types of pentalenyl bridged ansa‐metallocenes such as [Me₃Pen(Flu)]ZrCl₂. As of the bridge, the catalyst system is remarkable thermostable up to 105 °C and a deactivation of the metallocene on the silica support can be suppressed. Compared to the non‐supported catalyst in a solution process, the application of the heterogenized system in a gas phase process leads to a decrease in activities while molar masses of the polyethenes are similar. Due to a higher degree of short chain branches of 20–30 per 1 000 carbon atoms instead of 10–17, the melting temperatures are 10 °C lower than those for polymers obtained in the solution process.

Basic structure of the here used pentalenyl bridged ansa‐metallocenes.     

Automated reference-free detection of motion artifacts in magnetic resonance images

Objectives

Our objectives were to provide an automated method for spatially resolved detection and quantification of motion artifacts in MR images of the head and abdomen as well as a quality control of the trained architecture.

Materials and methods

T1-weighted MR images of the head and the upper abdomen were acquired in 16 healthy volunteers under rest and under motion. Images were divided into overlapping patches of different sizes achieving spatial separation. Using these patches as input data, a convolutional neural network (CNN) was trained to derive probability maps for the presence of motion artifacts. A deep visualization offers a human-interpretable quality control of the trained CNN. Results were visually assessed on probability maps and as classification accuracy on a per-patch, per-slice and per-volunteer basis.

Results

On visual assessment, a clear difference of probability maps was observed between data sets with and without motion. The overall accuracy of motion detection on a per-patch/per-volunteer basis reached 97%/100% in the head and 75%/100% in the abdomen, respectively.

Conclusion

Automated detection of motion artifacts in MRI is feasible with good accuracy in the head and abdomen. The proposed method provides quantification and localization of artifacts as well as a visualization of the learned content. It may be extended to other anatomic areas and used for quality assurance of MR images.

     

Partial-norm-constrained sparse recovery algorithm and its application on single carrier underwater-acoustic-data telemetry

To solve the problem of single carrier underwater-acoustic-data telemetry,compressive sensing (CS) provides competitive performance of compression and recovery with low energy consumption.The primary objective of CS is to minimize the l₀norm,which is an NP hard problem.Hence,the common methods were transferred to minimize l₁norm.However,l₁norm minimization provided a limited accuracy.A partial-norm-constraint (PNC) based sparse signal recovery method was derived,which adopted PNC as a zero attraction in a Lagrange method,to distribute the soft threshold for the non-zero taps.The proposed method is used for at-sea data telemetry.Combining with DCT,the proposed method improves the recovery accuracy.