Elementary Building Blocks Research Articles

Thermal emission from a single glass fiber

In this article, we study the thermal light emission from individual fibers of an industrial glass material, which are elementary building blocks of glass wool boards used for thermal insulation. Thermal emission spectra of single fibers of various diameters partially suspended on air are measured in the far field by means of infrared spatial modulation spectroscopy. These experimental spectra are compared with the theoretical absorption efficiency spectra of cylindrical shaped fibers calculated analytically in the framework of Mie theory taking as an input the measured permittivity of the industrial glass material. An excellent qualitative agreement is found between the measured thermal radiation spectra and the theoretical absorption efficiency spectra.

2D Superlattices for Efficient Energy Storage and Conversion.

2D genuine unilamellar nanosheets, that are, the elementary building blocks of their layered parent crystals, have gained increasing attention, owing to their unique physical and chemical properties, and 2D features. In parallel with the great efforts to isolate these atomic-thin crystals, a unique strategy to integrate them into 2D vertically stacked heterostuctures has enabled many functional applications. In particular, such 2D heterostructures have recently exhibited numerous exciting electrochemical performances for energy storage and conversion, especially the molecular-scale heteroassembled superlattices using diverse 2D unilamellar nanosheets as building blocks. Herein, the research progress in scalable synthesis of 2D superlattices with an emphasis on a facile solution-phase flocculation method is summarized. A particular focus is brought to the advantages of these 2D superlattices in applications of supercapacitors, rechargeable batteries, and water-splitting catalysis. The challenges and perspectives on this promising field are also outlined.

From quantitative 3D seismic stratigraphy to sequence stratigraphy: Insights into the vertical and lateral variability of shelf-margin depositional systems at different stratigraphic orders

Abstract A major challenge in sequence stratigraphy is objectively identifying stratigraphic surfaces and sequences across multiple scales of observation. Identification is commonly dependent on the resolution of the data used (i.e., seismic vs. well data), its dimension (i.e., 1D vs. 2D vs. 3D) or the criteria chosen to select sequence boundaries. Through shelf-edge trajectory analysis, the clinothem (i.e., highest order seismic sequence identified on seismic data) can constitute the elementary building block of an observation-based and data-driven quantitative workflow to develop sequence stratigraphic frameworks across different orders and ranks of hierarchy. Here, we use high-quality 3D seismic data to interpret a Late Tithonian–Early Cretaceous shelf margin, the Lower Barrow Group (LBG), developed in the Northern Carnarvon Basin on the North West Shelf of Australia. Based on full-volume seismic interpretation techniques that integrate the 3D variability of the data when identifying seismic unconformities, a high-resolution seismic stratigraphic framework was built (73 interpreted clinothems with an average time duration of ~63,000 yrs). The computation of high-frequency shelf-edge trajectory angle (Tse) curves on selected seismic cross-sections is used to objectively pick sequence stratigraphic surfaces based on the accommodation succession method, thereby highlighting small changes in trajectory and proposing a method reproducible by interpreters based on the same quantitative data. Within the D. lobispinosum interval (142.3–140.9 Ma), the definition of stratigraphic sequences and composite stratigraphic sequences through this workflow is used to discriminate the controls at high and low temporal frequency on the vertical and lateral variability (which is here quantified) of this shelf-slope-basin system. The results show that the high-frequency interplay between short-term glacio-eustasy (i.e., Milankovitch eccentricity cycles of ~100,000 yrs) and sediment supply (locus of fluvial input along the margin) impacted the three-dimensional stratigraphic architecture of the LBG. In contrast, tectonic subsidence had a significant impact on the stratigraphic architecture of the LBG within the main depocentre at lower temporal frequency by overprinting the eustatic signal and accelerating/decelerating the rates of accommodation creation. However, identification of long-term glacio-eustatic Milankovitch cycles (~400,000 yrs) outside the main depocentre, where the rates of accommodation creation due to rift-related subsidence are moderate, also suggests low-frequency eustatic control. Therefore, the vertical and lateral variability of the LBG results from variations in sediment supply and subsidence regime under local (i.e., process regime, currents), regional (i.e., tectonics) and global (i.e., eustasy, climate) forcing parameters interplaying across timescales. In contrast to standard sequence stratigraphic workflows that are based on model-dependent choices to select sequence boundaries, quantitative 3D seismic stratigraphy constitutes an improved method to interpret 3D seismic data in shelf-margin depositional systems within a sequence stratigraphic framework, which provides an observation-based and model-independent tool allowing the definition of stratigraphic sequences with results that are reproducible across multiple stratigraphers. This work highlights the need for developing new sequence stratigraphic tools and methods that integrate the 4D variability of depositional systems and moves beyond the two-dimensionality inherent to current sequence stratigraphic methods. Quantitative 3D seismic stratigraphy represents a first step towards the creation of 3D sequence stratigraphic workflows that could improve the prediction of stratigraphic patterns and facies relationships (source, reservoir, seal distribution) across shelf margins.

Achieving Fixed-Frequency Beam Scanning With a Microstrip Leaky-Wave Antenna Using Double-Gap Capacitor Technique

This study presents a novel half-width microstrip leaky-wave antenna (HW-MLWA) that can electronically control the main beam at a fixed frequency by using a double-gap capacitor with diodes, resulting in good impedance matching with small variation gain while scanning. The elementary building blocks of this antenna are periodic HW-MLWA and seven control unit cells (CUCs). A reconfigurable CUC is created by combining two triangle patches as double-gap capacitor with two diodes as switches to connect the patches with the ground plane. A gap capacitor in each patch cell is independently disconnected or connected by using a PIN diode switch. The reactance profile at the free edge of the microstrip is modified when the state of the patch cell changes, which in turn shifts the main beam direction. A single-pole double-throw switch is used to achieve backward-to-forward beam scanning at a certain frequency. An HW-MLWA is designed, fabricated, and validated. The proposed antenna prototype can scan the main beam forward between +28° and +67° and backward between –27° and –66° at 4.2 GHz. The measured peak gain of the antenna is 11.12 dBi at 4.2 GHz, and the gain variation is 1.3 dB while scanning.

Ordered Adlayers of a Combined Lateral Switch and Rotor

Rotors and switches are elementary building blocks of molecular machines. To achieve more advanced functions, these units have to be integrated into solid-state devices, which triggered interest in mounting these functional units in well-defined geometries onto surfaces. While vertically oriented switches and rotors have been obtained by various strategies, the design of surface-parallel switches and of altitudinal rotors with an in-plane oriented rotation axis has proven to be more difficult. We here demonstrate a molecular adlayer system with highly defined geometry and laterally oriented functional groups that combines facile photoswitching and rotation. We employ a custom-designed molecule with two platforms and pillars that span an azobenzene unit between them. The molecules form well-ordered monolayers on Au(111) with the azobenzene units parallel to and above the surface. Spectroscopic data and density functional calculations suggest that in the trans configuration, at room temperature, the azo uni...

The Link between ZSM-5 Zeolite Crystallization and Mesopore Formation by Leaching.

Acid or base leaching are well-established tools in the synthesis of mesoporous zeolites. Previous studies suggest an inherent link between the structure-property relationship of mesoporous zeolites, that is, chemical resistance, pore diameter and distribution, with zeolite synthesis or crystallization conditions. The exact nature and/or origin of this link is currently however, poorly established. Here, we provide evidence how zeolite crystallization conditions influence the leaching behavior and thus structure of mesoporous zeolites. Electron microscopy and in situ small angle X-ray scattering both confirmed the crystallization of ZSM-5 to utilize both nanoparticles and oligomers as elemental building blocks. Utilization of these species is highly depended on supersaturation. The precursor solution of decreased water content favored a faster consumption of nanoparticles compared to its oligomers at the early stage of crystallization. Then the addition of oligomers can heal the surface imperfections and thus the resulting zeolite showed a higher resistance against acid leaching. In contrast, within the precursor solution of increased water content the slower consumption of nanoparticles led to crystals with a less robust rim. Defects existed in the rim due to limited healing by oligomers and, as such, mesopores can be created by the following post-treatment. Precise control over selected crystallization conditions can therefore further aid the design of optimized mesoporous zeolites.

KmcEx: memory-frugal and retrieval-efficient encoding of counted k-mers.

K-mers along with their frequency have served as an elementary building block for error correction, repeat detection, multiple sequence alignment, genome assembly, etc., attracting intensive studies in k-mer counting. However, the output of k-mer counters itself is large; very often, it is too large to fit into main memory, leading to highly narrowed usability. We introduce a novel idea of encoding k-mers as well as their frequency, achieving good memory saving and retrieval efficiency. Specifically, we propose a Bloom filter-like data structure to encode counted k-mers by coupled-bit arrays-one for k-mer representation and the other for frequency encoding. Experiments on five real datasets show that the average memory-saving ratio on all 31-mers is as high as 13.81 as compared with raw input, with 7 hash functions. At the same time, the retrieval time complexity is well controlled (effectively constant), and the false-positive rate is decreased by two orders of magnitude. The source codes of our algorithm are available at github.com/lzhLab/kmcEx. Supplementary data are available at Bioinformatics online.

CST: A new semi-empirical tool for simulating spacecraft collisions in orbit

This paper provides a general description of a new Collision Simulation Tool (CST) to model the consequences of orbital impacts involving large debris and satellites, with the aim to predict fragments distributions in case of sub-catastrophic and catastrophic impacts. The new tool is being developed in the framework of the ESA contract “Numerical simulations for spacecraft catastrophic disruption analysis”, carried out by the Center of Studies and Activities for Space CISAS “G. Colombo” of the University of Padova (prime contractor) and etamax space GmbH.The CST makes possible to model a large variety of collision scenarios involving complex systems, such as entire satellites with many design details included, and provides statistically accurate results with a computational effort orders of magnitude lower than hydrocodes. To this end, the simulation approach is based on a hybrid modelling strategy, in which every colliding object is described as a gross net of Macroscopic Elements (ME) representing spacecraft elementary building blocks. On one hand, individual fragmentation of Macroscopic Elements is addressed through the use of semi-empirical breakup models applied, at element level, only to those spacecraft parts which are involved in the collision. On the other hand, structural distortion, fracture, and separation of satellite broken parts are modelled with a discrete-element approach through the simulation of momentum transfer to Macroscopic Elements through the net, taking into account energy dissipation inside elements and across links.This paper provides a description of the CST simulation methodology and framework; preliminary results are also shown with respect to the tool validation process. Validation is done by comparing the tool predictions with empirical data from ground-based impact tests on simple targets (simple plates and Whipple Shields) as well as sub-scale spacecraft models. In the first case, the tool capability of predicting fragments distributions from plates and ballistic limit equations of shields is verified. In the second case, fragments distributions calculated by the software are compared with those derived from published experiments on a micro-satellite.

Metal Oxide Nanoarrays for Chemical Sensing: A Review of Fabrication Methods, Sensing Modes, and Their Inter-correlations

In recent years, engineered nanostructure assemblies such as nanowire arrays have attracted much research attention due to their unique chemical and functional characteristics collectively. The engineered nano-assemblies usually carry the characteristics distinct from bulk as a result of a size effect in their comprised elemental building blocks. The nanoscale size induced high surface-to-volume ratio is a fundamental attribute responsible for various chemical and physical properties required in various technologically important applications such as catalysts and sensors. This review article surveys the latest progress in engineered metal oxide nanostructure arrays, i.e., nanoarrays, for advanced chemical sensors’ design and application. It starts with an overview of gaseous chemical sensors followed by surveys of various fabrication methods and routes for metal oxide nanoarrays. Different sensing modes and corresponding applications have been highlighted in the mixed gaseous chemical sensing, which provides new approaches and perspectives to meet the challenges of selective gas sensing, such as the cross-sensitivity and inter-correlation of multiple sensing signals.

Computational Modeling of Nanoparticle Coalescence

AbstractThe coalescence of nanoclusters fabricated in the gas phase is a fundamental growth mechanism determining cluster shapes, sizes, compositions, and structures, with resultant effects on practically all of their physical and chemical properties. Furthermore, coalescence can affect properties of larger structures that consist of nanoparticles as their elementary building blocks, such as the fractal dimension of cluster aggregates and the porosity and conductance of thin films. Therefore, it comes as no surprise that a great body of research, both experimental and theoretical, has focused on nanoparticle coalescence over the course of the past few decades. This review attempts to summarize the most important recent results from computational studies on nanoparticle coalescence and draw parallels between theoretical and experimental findings. The approach used here aspires to explain nanoparticle coalescence within the framework of a single intuitive narrative by integrating previous results obtained using various methods by the authors and others. Simultaneously, it is discussed where understanding and controlling (i.e., enhancing or inhibiting) nanoparticle coalescence can have great technological interest.

Multipartite state generation in quantum networks with optimal scaling

We introduce a repeater scheme to efficiently distribute multipartite entangled states in a quantum network with optimal scaling. The scheme allows to generate graph states such as 2D and 3D cluster states of growing size or GHZ states over arbitrary distances, with a constant overhead per node/channel that is independent of the distance. The approach is genuine multipartite, and is based on the measurement-based implementation of multipartite hashing, an entanglement purification protocol that operates on a large ensemble together with local merging/connection of elementary building blocks. We analyze the performance of the scheme in a setting where local or global storage is limited, and compare it to bipartite and hybrid approaches that are based on the distribution of entangled pairs. We find that the multipartite approach offers a storage advantage, which results in higher efficiency and better performance in certain parameter regimes. We generalize our approach to arbitrary network topologies and different target graph states.

Big Data—Building Software: Some Thoughts on the Future of Building Sciences

Building and sustaining our communities is the most significant challenge of our day. This means seeing and understanding the interrelatedness of economies, technologies, physical and natural systems. One way of achieving this objective is visualizing our options using big-data decision spaces where unique systems arerepresented by dynamic flows of data create patterns, relationships and context. As seminal computer scientist Jim Gray articulated in 2003, “big data describes on the one hand extremely large data sets that through analysis generate patterns and associations; on the other hand, it can be understood to encompass all potential data generated by any dynamic process, in short life on Earth as we know it”. This kind of ecological “data literacy” let us see beyond a mechanistic view of the phenomenal world in which the imagined universe is a machine composed of elementary building blocks, to a system view where the dominant model of the material world is a network of systems with interrelated and interdependent patterns of behavior. Adopting the systems’ approach the challenge in designing resilient buildings is: 1) educating architects and engineers capable of using multiple and varied data to create multipart decision spaces, 2) assessing the impact visualizing data will have on all aspects of the building design, construction and fabrication process and 3) making increasingly large data-sets available to the AEC professionals and researchers to asses building performance and its impact on our design, delivery and maintenance of the built environment available.

Evaporation-induced assembly of colloidal clusters into superclusters with nonconvex deltahedral geometry

Using small colloidal clusters with dumbbell, triplet, tetrahedral, octahedral geometry as elementary building blocks, we investigate the templated-assisted cluster-cluster aggregation into hierarchical clusters, namely superclusters, by means of Monte Carlo simulations. We find that the colloidal superclusters made of dumbbells and triplets produces a unique structural motif that is similar to that of clusters of single colloids, whereas the superclusters composed of tetrahedral and octahedral clusters possess a more complex structure. However, despite their complexity, such structures can be predicted based on a decomposition using triplets as a core and the number of initial “building block” clusters. In particular, a majority of the observable superclusters are members of a particular category of nonconvex deltahedra (Cundy deltahedra). These findings could be useful in preparation of complex colloidal molecules.

Assortative exchange processes

In exchange processes clusters composed of elementary building blocks, monomers, undergo binary exchange in which a monomer is transferred from one cluster to another. In assortative exchange only clusters with comparable masses participate in exchange events. We study maximally assortative exchange processes in which only clusters of equal masses can exchange monomers. A mean-field framework based on rate equations is appropriate for spatially homogeneous systems in sufficiently high spatial dimension. For diffusion-controlled exchange processes, the mean-field approach is erroneous when the spatial dimension is smaller than critical; we analyze such systems using scaling and heuristic arguments. Apart from infinite-cluster systems we explore the fate of finite systems and study maximally assortative exchange processes driven by a localized input.

All carbon materials pn diode

Semiconductor pn junctions are elementary building blocks of many electronic devices such as transistors, solar cells, photodetectors, and integrated circuits. Due to the absence of an energy bandgap and massless Dirac-like behaviour of charge carriers, graphene pn junction with electrical current rectification characteristics is hardly achieved. Here we show a graphene pn junction diode can be made exclusively from carbon materials by laminating two layers of positively and negatively charged graphene oxides. As the interdiffusion of oppositely charged mobile counterions, a built-in potential is created to rectify the current by changing the tunnelling probability of electrons across the junction. This graphene diode is semi-transparent, can perform simple logic operations, and since it has carbon nanotubes electrodes, we demonstrate an all carbon materials pn diode. We expect this graphene diode will expand material choices and provide functionalities (e.g. grafting recognition units on graphene oxides) beyond that of traditional semiconductor pn junctions.

Epidemiological Dimensions of Social Anhedonia

Social anhedonia (SA)—reduced drive for and pleasure from social interaction—is associated with social/emotional dysfunction and risk for psychopathology. However, our understanding of the factors that contribute to variation in SA remains limited. Here, we investigate the epidemiology of SA in an international population-based sample of more than 19,000 individuals who completed the Revised Social Anhedonia Scale through TestMyBrain.org . We find that SA exhibits considerable variation over the life span and is higher in males versus females, people of lower socioeconomic status, those of African ethnicity, nonmigrants, and people living in ethnically dense locations and less urban environments. Gender, socioeconomic status, and urbanicity were the only factors that captured unique variance in SA. These findings provide a framework for understanding how variation in epidemiological factors contribute to variation in elemental building blocks of psychopathology, and demonstrate the utility of using big data approaches toward the study of risk and Research Domain Criteria dimensions.

Scientific iconoclasm and active imagination: synthetic cells as techno-scientific mandalas

Metaphors allow us to come to terms with abstract and complex information, by comparing it to something which is structured, familiar and concrete. Although modern science is “iconoclastic”, as Gaston Bachelard phrases it (i.e. bent on replacing living entities by symbolic data: e.g. biochemical and mathematical symbols and codes), scientists are at the same time prolific producers of metaphoric images themselves. Synthetic biology is an outstanding example of a technoscientific discourse replete with metaphors, including textual metaphors such as the “Morse code” of life, the “barcode” of life and the “book” of life. This paper focuses on a different type of metaphor, however, namely on the archetypal metaphor of the mandala as a symbol of restored unity and wholeness. Notably, mandala images emerge in textual materials (papers, posters, PowerPoints, etc.) related to one of the new “frontiers” of contemporary technoscience, namely the building of a synthetic cell: a laboratory artefact that functions like a cell and is even able to replicate itself. The mandala symbol suggests that, after living systems have been successfully reduced to the elementary building blocks and barcodes of life, the time has now come to put these fragments together again. We can only claim to understand life, synthetic cell experts argue, if we are able to technically reproduce a fully functioning cell. This holistic turn towards the cell as a meaningful whole (a total work of techno-art) also requires convergence at the “subject pole”: the building of a synthetic cell as a practice of the self, representing a turn towards integration, of multiple perspectives and various forms of expertise.

Probabilistic multiscale characterization and modeling of organic-rich shale poroelastic properties

A probabilistic modeling and validation for multiscale poroelastic behavior of organic-rich shale is presented. The methodology is based on the integration of microporomechanics and effective medium theory with uncertainty quantification and propagation from nano- to macroscale. The multiscale model utilizes and improves an existing structural thought model of organic-rich shale material consisting of three levels that span from the nanoscale elementary building blocks of material to the scale of the macroscopic inorganic/organic hard/soft inclusion composite. Major model parameters related to the composition and mechanical properties of shale subscale features are modeled as random variables. Probabilistic description of model input parameters are constructed using the maximum entropy principle (MaxEnt) based on available information. Special care is given to the development of probabilistic description of random elasticity tensor of clay phase exhibiting transversely isotropic symmetry properties by employing the theory of random matrix combined with MaxEnt principle. Having the probabilistic descriptions of uncertain model parameters in hand, a Monte Carlo simulation is carried out to propagate the uncertainty across different scales, which allows us to construct a prior probabilistic descriptions of model outputs. The model predictions at different scales are validated against the available experimental measurements from samples of different formations and relevant information in the literature. Furthermore, a global sensitivity analysis is performed to characterize the contribution of each source of uncertainty in the variation of model predictions with respect to different quantities of interest related to macroscale mechanical properties of shale rocks. The integration of experimental characterization, microporomechanical modeling, and uncertainty quantification and propagation proposed in this work provides valuable insights into understanding the effect of subscale compositions and their variation in the upscaled poroelastic properties of shale rocks and improves the robustness and reliability of predictive models for the shale multiscale behavior.

Ab-initio process synthesis using evolutionary programming

Process synthesis methods enable the determination of unit operations and their interconnection into a process flowsheet, with associated design and operating parameters, and responding to given objectives. Modern methods are optimization-based, using for example Mixed Integer Non-Linear Programming (MINLP) formulation to optimize a process superstructure. Finding an adequate definition of the search space is a non-trivial problem in such approaches, especially when the number of possible combinations is high due to the process complexity, and is mostly driven by expertise (e.g. heuristics). Consequently, an inductive bias is intrinsically introduced due to restriction of a limited search space, such as the choice of a superstructure representing a limited set of process alternatives.In this work, an evolutionary method is proposed to generate several process architectures based on a set of available unit operations (and associated models) as elementary building blocks. The procedure is here called ab initio process synthesis since it does not require any pre-defined process structure. The developed method relies on the use of an Evolutionary Programming (EP), mimicking natural evolution at species-level, for the automatic construction of a process by using mutation operators to choose, assemble and connect elementary building blocks (i.e. unit operations). A Non-Linear Programming (NLP) is used for process evaluation, by simultaneously solving balances and optimizing process degrees of freedom.The method is implemented in a newly developed tool called PSEvo (Process Synthesis by Evolution). An application to a typical reaction-separation problem is presented, using various problem definitions and evolution control parameters, which demonstrates the method capability to generate optimal processes. The possible uses and the challenges of ab initio process synthesis are finally discussed.

Liquid-Crystalline Tris[60]fullerodendrimers.

Liquid-crystalline tris[60]fullerodendrimers based on first- and second-generation poly(arylester)dendrons carrying cyanobiphenyl mesogens were synthesized for the first time by the olefin cross-metathesis reaction between type I (terminal) and type II (α,β-unsaturated carbonyl) olefinic precursors, using a second-generation Grubbs or Hoveyda-Grubbs catalyst. The modular synthetic approach developed here also allowed the selective preparation of the [60]fullerene-free, mono[60]fullerodendrimer, and bis[60]fullerodendrimer derivatives from the appropriate precursors. As revealed by polarized optical microscopy, differential scanning calorimetry, and small-angle X-ray scattering, all of the materials displayed liquid-crystalline properties. In agreement with the nature of the dendritic building blocks, the emergence of lamellar mesophases (smectic C and/or smectic A phases), with the segregation of the various constitutive parts, was systematically observed. The small variation of the mesomorphic temperature range and of the mesophase stability suggested that the mesomorphism is essentially dominated by the dendrimer itself and is regulated by a subtle adaptive mechanism, in which the proportion of monolayering and bilayering arrangements of the multisegregated lamellar mesophases is modified in order to compensate the space requirements of each of the elementary building blocks, namely, the [60]fullerene units, the cyanobiphenyl mesogens, and the dendritic matrix.