Architecture Properties Research Articles

Neuroanatomical and Functional Dissociations between Variably Present Anterior Lateral Prefrontal Sulci.

The lateral prefrontal cortex (LPFC) is an evolutionarily expanded region in humans that is critical for numerous complex functions, many of which are largely hominoid specific. Although recent work shows that the presence or absence of specific sulci in anterior LPFC is associated with cognitive performance across age groups, it is unknown whether the presence of these structures relates to individual differences in the functional organization of LPFC. To fill this gap in knowledge, we leveraged multimodal neuroimaging data from two samples encompassing 82 young adult humans (aged 22-36 years) and show that the dorsal and ventral components of the paraintermediate frontal sulcus, or pimfs, present distinct morphological (surface area), architectural (thickness and myelination), and functional (resting-state connectivity networks) properties. We further contextualize the pimfs components within classic and modern cortical parcellations. Taken together, the dorsal and ventral pimfs components mark transitions in LPFC anatomy and function, across metrics and parcellations. These results emphasize that the pimfs is a critical structure to consider when examining individual differences in the anatomical and functional organization of LPFC and suggest that future individual-level parcellations could benefit from incorporating sulcal anatomy when delineating LPFC cortical regions.

Reduction of wing area affects estimated stress in the primary flight muscles of chickens.

In flying birds, the pectoralis (PECT) and supracoracoideus (SUPRA) generate most of the power required for flight, while the wing feathers create the aerodynamic forces. However, in domestic laying hens, little is known about the architectural properties of these muscles and the forces the wings produce. As housing space increases for commercial laying hens, understanding these properties is important for assuring safe locomotion. We tested the effects of wing area loss on mass, physiological cross-sectional area (PCSA), and estimated muscle stress (EMS) of the PECT and SUPRA in white-feathered laying hens. Treatments included Unclipped (N = 18), Half-Clipped with primaries removed (N = 18) and Fully-Clipped with the primaries and secondaries removed (N = 18). The mass and PCSA of the PECT and SUPRA did not vary significantly with treatment. Thus, laying hen muscle anatomy may be relatively resistant to changes in external wing morphology. We observed significant differences in EMS among treatments, as Unclipped birds exhibited the greatest EMS. This suggests that intact wings provide the greatest stimulus of external force for the primary flight muscles.

Towards a Methodology to Design Provably Secure Cyber-physical Systems

The inordinate financial cost of mitigating post-production cybersecurity vulnerabilities in cyber-physical systems (CPS) is forcing the industry to rethink systems design cycles: greater attention is being given to the design phase - with the goal of reducing the attack surface of systems at an early stage (i.e., before silicon tape out). Fortunately, formal methods have advanced to the point that they can address such needs and contribute towards achieving security certification. However, new methods and tools focusing on industrial scalability and usability for systems engineers are required. In this ongoing research paper, we describe a framework that will help systems engineers to: a) design cyber-assured CPS using a Model Based Engineering (MBE) approach; b) formally map security requirements to different hardware and software blocks in the model; and c) formally verify security requirements. Based on the nature of each requirement, our framework collects formal correctness evidence from different tools: while high-level architectural properties are suitable for a contract- or ontology-based reasoning, more complex properties with rich semantics require the use of model checking or theorem proving techniques.

Artificial Visual Synaptic Architecture with High-Linearity Light-Modulated Weight for Optoelectronic Neuromorphic Computing.

A brain-like neuromorphic computing system, as compared with traditional Von Neumann architecture, has broad application prospects in the fields of emerging artificial intelligence (AI) due to its high fault tolerance, excellent plasticity, and parallel computing capability. A neuromorphic visuosensory and memory system, an important branch of neuromorphic computing, is the basis for AI to perceive, process, and memorize optical information, now still suffering from nonlinearity of synaptic weight, crosstalk issues, and integration incompatibility, hindering the high-level training and inference accuracy. In this work, we propose a new optoelectronic neuromorphic architecture by integrating an electrochromic device and a perovskite photodetector. Ascribing to the superior memory characteristics of the electrochromic device and sensitive light response of the perovskite photodetector, the neuromorphic device shows typical visual synaptic functionalities such as light triggering, neural facilitation, long-term potentiation, and depression (LTP and LTD). Furthermore, by adjusting the intensity and wavelength of external light signals, the visual synaptic function of the device can be modulated, enabling the device to exhibit high weight linearity in all current output ranges and improve information processing capability and image recognition accuracy. Moreover, both the electrochromic and perovskite layers possess the advantage of large area fabrication and integration, which enables the fabrication of large device arrays with high integration compatibility and scalability. In this study, 10 × 10 device arrays are demonstrated and each device shows uniform light responses, memory behaviors, and synaptic performances. MNIST and CIFAR-10 algorithms are used to simulate the image recognition properties of the synaptic architecture, and the calculated recognition accuracy is 97.94 and 91.04%, respectively, with an error less than 2.5%. The proposed artificial visual neuromorphic architecture will provide a potential device prototype for efficient visual neuromorphic systems.

Development of a semi-anthropomorphic photoacoustic calcaneus phantom based on nano computed tomography and stereolithography 3D printing.

Osteoporosis is a major public health threat with significant physical, psychosocial, and financial consequences. The calcaneus bone has been used as a measurement site for risk prediction of osteoporosis by noninvasive quantitative ultrasound (QUS). By adding optical contrast to QUS, our previous studies indicate that a combination of photoacoustic (PA) and QUS, that is, PAQUS, provides a novel opportunity to assess the health of human calcaneus. Calibration of the PAQUS system is crucial to realize quantitative and repeatable measurements of the calcaneus. Therefore, a phantom which simulates the optical, ultrasound, and architectural properties of the human calcaneus, for PAQUS system calibration, is required. Additionally, a controllable phantom offers researchers a versatile framework for developing versatile structures, allowing more controlled assessment of how varying bone structures cause defined alterations in PA and QUS signals. In this work, we present the first semi-anthropomorphic calcaneus phantom for PAQUS. The phantom was developed based on nano computed-tomography (nano-CT) and stereolithography 3D printing, aiming to maximize accuracy in the approximation of both trabecular and cortical bone microstructures. Compared with the original digital input calcaneus model from a human cadaveric donor, the printed model achieved accuracies of 71.15% in total structure and 87.21% in bone volume fraction. Inorganic materials including synthetic blood, mineral oil, intralipid, and agar gel were used to model the substitutes of bone marrow and soft tissue, filling and covering the calcaneus phantom. The ultrasound and optical properties of this phantom were measured, and the results were consistent with those measured by a commercialized device and from previous in vivo studies. In addition, a short-term stability test was conducted for this phantom, demonstrating that the optical and ultrasound properties of the phantom were stable without significant variation over 1 month. This semi-anthropomorphic calcaneus phantom shows structural, ultrasound, and optical properties similar to those from a human calcaneus in vivo and, thereby, can serve as an effective source for equipment calibration and the comprehensive study of human patients.

Exceptional dynamic compressive properties of bio-inspired three-dimensional interlocking graphene network reinforced copper matrix composites

Insights into the reinforcement spatial architectures and their fundamental effects on the dynamic mechanical behaviors are of great importance for designing shock-resistant metallic structures. In this study, we report copper matrix composites (CMCs) reinforced by three-dimensional interlocking graphene network (3D-IGN) with a unique bio-inspired “brick-bridge-mortar” structure. Our results demonstrate that the 3D-IGN/Cu shows simultaneously enhanced dynamic strength and ductility as compared to the uniformly-distributed RGO/Cu and pure Cu at the specific strain rate from 1000 s−1 to 8000 s−1. The interlocking network structure not only blocks dislocation movement and restricts grain boundary sliding, but also alleviates the heat-induced softening by improving the thermal conductivity in the horizontal direction. The finite element simulation results further confirm the important role of the graphene network on strain delocalization. This work offers a promising bottom-up tactic to fabricate CMCs with network architecture and superior dynamic properties for high-rate applications.

An efficient dual-response luminescent metal-organic framework sensor constructed by new photochromic ligand

Photochromic materials have shown very promising applications. However, developing new photochromic materials remains a huge challenge. In this work, a new photochromic organic molecule, namely 6,13-bis(di(pyridin-4-yl)methylene)-6,13-dihydropentacene (L), has been successfully synthesized. Its photochromic mechanism has been elucidated. The dihydropentacene-based photochromic organic molecule was firstly applied as functional ligands to construct LMOF-based sensor {Zn(L)(TPA)·H2O}n (1; LMOF = luminescent metal-organic framework; H2TPA = terephthalic acid). 1 exhibits a three-dimensional (3-D) two-fold interpenetrated architecture, excellent stabilities, luminescent and photochromic properties. 1 can highly sensitively detect Cu2+ and MnO4− in aqueous medium by luminescent quenching effect. Notably, the quenching constants of 1′ (1 after photochromism) towards Cu2+ and MnO4− are 6.4 and 6.5 times higher than those of 1, respectively, which demonstrates significantly enhanced sensing sensitivities for 1′. Furthermore, 1′ can effectively identify MnO4− from similar strong oxidizing anions CrO42− and Cr2O72−. This dual-response luminescent sensor exhibits good recyclability, and its quenching sensing mechanisms have been discussed in detail. This work will contribute to the development of efficient photochromic luminescent sensors.

Workshop Summary: Exoplanet Orbits and Dynamics

Abstract Exoplanetary systems show a wide variety of architectures, which can be explained by different formation and dynamical evolution processes. Precise orbital monitoring is mandatory to accurately constrain their orbital and dynamical parameters. Although major observational and theoretical advances have been made in understanding the architecture and dynamical properties of exoplanetary systems, many outstanding questions remain. This paper aims to give a brief review of a few current challenges in orbital and dynamical studies of exoplanetary systems and a few future prospects for improving our knowledge. Joint data analyses from several techniques are providing precise measurements of orbits and masses for a growing sample of exoplanetary systems, both with close-in orbits and with wide orbits, as well as different evolutionary stages. The sample of young planets detected around stars with circumstellar disks is also growing, allowing for simultaneous studies of planets and their birthplace environments. These analyses will expand with ongoing and future facilities from both ground and space, allowing for detailed tests of formation, evolution, and atmospheric models of exoplanets. Moreover, these detailed analyses may offer the possibility of finding missing components of exoplanetary systems, such as exomoons, or even finding new exotic configurations such as co-orbital planets. In addition to unveiling the architecture of planetary systems, precise measurements of orbital parameters and stellar properties—in combination with more realistic models for tidal interactions and the integration of such models in N-body codes—will improve the inference of the past history of mature exoplanetary systems in close-in orbits. These improvements will allow a better understanding of planetary formation and evolution, placing the solar system in context.

Tracing past extreme floods on an alluvial fan using geophysical surveying

SummarySedimentary units in alluvial fans may record gradual transport and deposition during multiple floods or sediment‐laden flows or, conversely, during few catastrophic events. While outcrops are a valuable source of direct information to constrain past geomorphic and hydrologic processes, such exposures are scarce, especially along aggrading rivers or those that have been subject to recent catastrophic sedimentation. In this context, near‐surface geophysical techniques can constrain the dimensions, internal architecture, composition, and petrophysical properties of different sedimentary units. We consider the Grimmbach alluvial fan in the cuesta landscape of southwestern Germany, which was heavily impacted by sediment and wood loads during a flash flood in 2016; published radiocarbon dates indicate that at least three floods similar to the one in 2016 may have occurred since the 17th century. To test whether and to which detail near‐surface geophysics might reveal the sedimentary legacy of these floods, we survey the Grimmbach alluvial fan using detailed topographic data and geophysical imaging based on electromagnetic induction, electrical resistivity tomography (ERT), and ground‐penetrating radar. Our geophysical results indicate former channel courses and two coarse bar deposits up to 3 m below the surface, which are comparable with the more extensive bar deposits of the 2016 flood. From the ERT models, we interpret coarse, up to 5 m thick, gravel lag overlying bedrock at a maximum depth of 10 m. Our geophysical results also highlight patches of finer materials derived from gradual sedimentation and soil development. Overall, our results show that the Grimmbach alluvial fan may have formed and reshaped during catastrophic flows, which likely caused channel avulsions. Our findings point to the need to reconsider flash flood and debris‐flow hazards in similar headwaters and fans of this seemingly quiescent cuesta landscape in southern Germany.

Multifunctional Nanofibrous Scaffolds Capable of Localized Delivery of Theranostic Nanoparticles for Postoperative Cancer Management.

Postoperative recovery of cancer patients can be affected by complications, such as tissue dysfunction or disability caused by tissue resection, and also cancer recurrence resulting from residual cancer cells. Despite impressive progress made for tissue engineering scaffolds that assist tissue regeneration for postoperative cancer patients, the majority of existing tissue engineering scaffolds still lack functions for monitoring and killing residual cancer cells, if there are any, upon their detection. In this study, multifunctional scaffolds that comprise biodegradable nanofibers and core-shell structured microspheres encapsulated with theranostic nanoparticles (NPs) are developed. The multifunctional scaffolds possess an extracellular matrix-like nanofibrous architecture and soft tissue-like mechanical properties, making them excellent tissue engineering patch candidates for assisting in the repair and regeneration of tissues at the cancerous sites after surgery. Furthermore, they are capable of localized delivery of theranostic NPs upon quick degradation of core-shell structured microspheres that contain theranostic NPs. Leveraging on folic acid-mediated ligand-receptor binding, surface-enhanced Raman scattering activity, and near-infrared-responsive photothermal effect of the theranostic gold NPs (AuNPs) delivered locally, the multifunctional scaffolds display excellent active targeting, diagnosis, and photothermal therapy functions for cancer cells, showing great promise for adaptive postoperative cancer management.

Impact of various detergent-based immersion and perfusion decellularization strategies on the novel caprine pancreas derived extracellular matrix scaffold.

Limited availability of the organs donors has facilitated the establishment of xenogeneic organ sources for transplantation. Numerous studies have decellularized several organs and assessed their implantability in order to provide such organs. Among all the decellularized organs studies for xenotransplantation, the pancreas has garnered very limited amount of research. The presently offered alternatives for pancreas transplantation are unable to liberate patients from donor dependence. The rat and mice pancreas are not of an accurate size for transplantation but can only be used for in-vitro studies mimicking in-vivo immune response in humans, while the porcine pancreas can cause zoonotic diseases as it carries porcine endogenous retrovirus (PERV- A/B/C). Therefore, we propose caprine pancreas as a substitute for these organs, which not only reduces donor dependence but also poses no risk of zoonosis. Upon decellularization the extracellular matrix (ECM) of different tissues responds differently to the detergents used for decellularization at physical and physiological level; this necessitates a comprehensive analysis of each tissue independently. This study investigates the impact of decellularization by ionic (SDS and SDC), non-ionic (Triton X-100 and Tween-20), and zwitterionic detergents (CHAPS). All these five detergents have been used to decellularize caprine pancreas via immersion (ID) and perfusion (PD) set-up. In this study, an extensive comparison of these two configurations (ID and PD) with regard to each detergent has been conducted. The final obtained scaffold with each set-up has been evaluated for the left-over cytosolic content, ECM components like sGAG, collagen, and fibronectin were estimated via Prussian blue and Immunohistochemical staining respectively, and finally for the tensile strength and antimicrobial activity. All the detergents performed consistently superior in PD than in ID. Conclusively, PD with SDS, SDC, and TX-100 successfully decellularizes caprine pancreatic tissue while retaining ECM architecture and mechanical properties. This research demonstrates the viability of caprine pancreatic tissue as a substitute scaffold for porcine organs and provides optimal decellularization protocol for this xenogeneic tissue. This research aims to establish a foundation for further investigations into potential regenerative strategies using this ECM in combination with other factors.

Correctness of IoT-based systems: From a DSL to a mechanised analysis

Systems based on the Internet of Things are being widely used in industrial areas such as smart manufacturing, in smart health monitoring, in entertainment and in home automation application. They impact more and more every day life. Ensuring their correct construction, their well functioning and their reliability is an important issue for some of these systems which can be critical in case of dysfunction. The main requirements on physical architectures and control software are common to most of IoT-based systems. We propose, on the basis of the common architectural properties and the behaviour of IoT-based systems, the rigorous analysis of their intrinsic consistency properties; specific properties may also be considered and analysed.We propose a model-based approach to help in systematically modelling and analysing these systems. Our approach focuses both on the formalisation of the model of the targeted systems for their rigorous analysis purpose, and on the design of a modelling language as a domain specific language dedicated to describe IoT-based systems. We build a generic formal model which captures the common features and the properties required for any IoT-based system. This formal model is then a parametrised model where the parameters are the specific data and properties of a given system. The proposed DSL, named τila , is used to describe an IoT system which will be submitted to formal analysis of correctness. τila is a tiny declarative language, extensible, based on a library of building component blocks available as types.In fact, a description in τila helps to model a system, by collecting the parameters necessary to instantiate the generic formal model. Therefore the analysis requirements are based on the generic model. We experimented our approach using the proposed τila DSL for modelling and the Event-B framework for the formal analysis. The overall proposed approach is accompanied by a model editor generated to assist users in modelling an application and a prototype tool we have developed to assist the users in analysing their systems. Examples are provided.The generic formal model is extensible; it may be profitably adapted to more general hybrid or cyber–physical systems. Moreover, our generic model is independent of the target formal modelling tools; it may be implemented in various other formal analysis environments.

Short-Term Natural Gas and Carbon Price Forecasting Using Artificial Neural Networks

Methods of computational intelligence show a high potential for short-term price forecasting of the energy market as they offer the possibility to cope with the complexity, multi-parameter dependency, and non-linearity of pricing mechanisms. While there is a large number of publications applying neural networks to the prediction of electricity prices, the analysis of natural gas and carbon prices remains scarce. Identifying a best practice from the literature, this study presents an iterative approach to optimize both the input values and network configuration of neural networks. We apply the approach to the natural gas and carbon market, sequentially testing autoregressive and exogenous explanatory variables as well as different neural network architectures. We subsequently discuss the influence of architectural properties, input parameters, data preparation, and the models’ resilience to singular events. Results show that the selection of appropriate lags of gas and carbon prices to account for autoregressive properties of the respective time series leads to a high degree of forecasting accuracy. Additionally, including ambient temperature data can slightly reduce errors of natural gas price forecasting whereas carbon price predictions benefit from electricity prices as a further explanatory input. The best configurations presented in this contribution achieve a root mean square error (RMSE) of 0.64 EUR/MWh (natural gas prices) corresponding to a normalized RMSE of 0.037 and 0.33 EUR/tCO2 (carbon prices) corresponding to a normalized RMSE of 0.023.

From ancient lime mortar recipes to organic laboratory replicas: Interpreting forced carbonation through 3D microstructural architecture

The characterisation of lime mortars is a relevant task for the conservation of ancient buildings and the correct design of repair materials; among the relevant properties to be studied, the pore structure stand out, which is influenced by the manufacturing processes, receipts and technical expedients and is related to the strength and durability of the mortars. Among other things, the ancient practice of fermenting organic matter in water and adding it to the lime mixture has shown great potential for improving workability and mechanical performance of the building environment. In fact, this practice ensures an internal source of forced carbonation, which in turn determines the precipitation of new mineral phases and the efficient modification of the pore structure. However, a full understanding of the contribution of organically fermented water in lime mortars requires a detailed visual inspection. In this respect, the use of high-resolution synchrotron radiation computed microtomography (SR-μ-CT) proves to be an effective tool for the validation of volumetric data and the three-dimensional representation of studied objects. Under these premises, SR-μ-CT has been used to study different mortars prepared by fermenting various organics in the mixing water. The combination of 3D imaging and traditional physical-mechanical characterisation tests supported the modelling of forced carbonation induced by organically fermented water, suggesting that the different proportions in carbohydrates, fats and proteins of selected organics influence the final pore architecture and mechanical properties of the mixtures since the first phases of carbonation. In particular, carbohydrate-rich organics promote the development of a uniform microstructure and improved mechanical strength, whereas fat-rich organics determine a different and less efficient microstructure due to the low carbonation reactions, the major contribution of larger pores and the lower connectivity between them.

(Invited) Accelerating Battery Manufacturing Optimization by Combining Process Modeling with Machine Learning

The manufacturing process of lithium ion battery (LIB) electrodes impact their architecture and practical properties, such as their energy and power densities, their durability and safety. Therefore, it is very important to optimize this manufacturing process in a proper manner. Such an optimization is highly complex because of the very significant amount of parameters and numerous interdependencies between the process steps, such as the slurry mixing, the casting, the drying, the calendering, the assembly, the electrolyte filling and the solid electrolyte interphase formation. As a consequence, traditional trial and error approaches can lead to high scrap rates in LIB cell manufacturing (as high as 30 % in prototyping activities).1 In this lecture I present a computational infrastructure able to optimize the LIB manufacturing process. Such infrastructure, called ARTISTIC,2 is supported on multiscale physics-based modeling and machine learning. Mixing, casting and drying stages are simulated by using a 3D-resolved Coarse Grain Molecular Dynamics approach, while the calendering is simulated by using a 3D-resolved Discrete Element Method. The electrolyte filling is simulated by using a 3D-resolved Lattice Boltzmann Method and the formation stage is simulated by using a 3D-resolved Finite Element Method (FEM)-based model. These physics-based models are sequentially linked in a way that the outputs of one simulated process step goes as inputs for the following one. In such a way, the interdependencies between process steps can be captured. At the end of this workflow, the 3D-FEM-based model is used to simulate the electrochemical and transport processes in the electrodes and capture the influence of manufacturing parameters on the spatiotemporal heterogeneities of lithiation/delithiation. Machine learning models, particle swarm optimization and Bayesian optimization are used to speed up the parameterization of these different models. Machine learning is also used to derive surrogate models implemented in a Bayesian optimizer predicting which manufacturing parameters to adopt in order to maximize/minimize different electrode properties.All this modeling work is supported in a 1-to-1 comparison with experimental data acquired in our battery manufacturing pilot line. The optimization capabilities of our computational infrastructure is demonstrated for LIBs with electrodes with different active material chemistries (NMC111, NMC622, LFP, graphite, silicon-graphite blends), for Sodium Ion and Solid State Batteries. Illustrative examples are provided during my lecture.3 I also discuss the ARTISTIC Online Calculator,4 a free service offered by the project to perform battery manufacturing simulations from an internet browser. Finally, I demonstrate our Virtual Reality-based digital twin of our battery manufacturing pilot line5 and how it is being used by us to train the next generation researchers and pilot line operators.

Layer adaptive node selection in Bayesian neural networks: Statistical guarantees and implementation details

Sparse deep neural networks have proven to be efficient for predictive model building in large-scale studies. Although several works have studied theoretical and numerical properties of sparse neural architectures, they have primarily focused on the edge selection. Sparsity through edge selection might be intuitively appealing; however, it does not necessarily reduce the structural complexity of a network. Instead pruning excessive nodes leads to a structurally sparse network with significant computational speedup during inference. To this end, we propose a Bayesian sparse solution using spike-and-slab Gaussian priors to allow for automatic node selection during training. The use of spike-and-slab prior alleviates the need of an ad-hoc thresholding rule for pruning. In addition, we adopt a variational Bayes approach to circumvent the computational challenges of traditional Markov Chain Monte Carlo (MCMC) implementation. In the context of node selection, we establish the fundamental result of variational posterior consistency together with the characterization of prior parameters. In contrast to the previous works, our theoretical development relaxes the assumptions of the equal number of nodes and uniform bounds on all network weights, thereby accommodating sparse networks with layer-dependent node structures or coefficient bounds. With a layer-wise characterization of prior inclusion probabilities, we discuss the optimal contraction rates of the variational posterior. We empirically demonstrate that our proposed approach outperforms the edge selection method in computational complexity with similar or better predictive performance. Our experimental evidence further substantiates that our theoretical work facilitates layer-wise optimal node recovery.

Multifunctional and ultrastrong MXene modified aramid fibers

Two dimensional (2D) materials have been extensively used in applications such as energy storage, solar steam generation, power generation, and water purification. Among these 2D materials, Ti3C2Tx MXene is one of the most popular candidates for energy storage applications because of its outstanding electrical and electrochemical properties. However, its applications are restricted due to the insufficient mechanical properties of pure MXene architectures caused by weak inter-sheet assembly. One potential supporting substrate used to impart mechanical integrity to MXene architectures is aramid yarn, however, one drawback of this lightweight and strong material is its lack of surface functionality, inhibiting MXene adhesion. To solve this issue, air plasma was utilized to impart hydroxyl groups to the fiber, enabling MXene to be deposited through a simple dip coating method. Upon 35 layers of MXene coating, the resulting composite fiber becomes a promising electrode material for supercapacitors demonstrating outstanding energy density (18.51 μWh cm−2) and low electrical resistance (0.4 kΩ cm−1) while retaining excellent tensile strength (3.28 GPa). Furthermore, the composite fiber can be also integrated into wearable Joule heating devices as it provides human body temperature heating with 7 V voltage applied.

Hybrid nanostructures based on vertically graphenes decorated with tungsten oxide nanoparticles for enhanced capacitive performance

Herewith, we investigated the capacitive properties of hybrid nanometric architectures consisting of tungsten oxide nanoparticles deposited on graphite (WOx/G) and graphite coated with highly-porous carbon nanowall layers (WOx/CNW/G). The deposition of the nanostructured oxide was performed by a plasma-assisted sublimation, vapor transport and condensation technique. The samples containing the CNW interlayer show superior areal capacitances making this architecture and its fabrication route promising approaches for supercapacitor applications. Thus, areal capacitances of WOx/CNW/G and WOx/G, estimated from galvanostatic charge-discharge tests (GCD) at an applied density current of 1 mA cm−2, are 1247.2 mF cm−2 and 527.3 mF cm−2, respectively. From SEM and XPS investigations, it was observed that CNW promotes the formation of a high amount of small tungsten oxide particles, homogeneously distributed on electrode surface, and facilitates the formation of highly conductive tungsten nitride species, resulting in electrodes with remarkable charge-storage capacity.

In silico assessment of the bone regeneration potential of complex porous scaffolds

Mechanical environment plays a crucial role in regulating bone regeneration in bone defects. Assessing the mechanobiological behavior of patient-specific orthopedic scaffolds in-silico could help guide optimal scaffold designs, as well as intra- and post-operative strategies to enhance bone regeneration and improve implant longevity. Additively manufactured porous scaffolds, and specifically triply periodic minimal surfaces (TPMS), have shown promising structural properties to act as bone substitutes, yet their ability to induce mechanobiologially-driven bone regeneration has not been elucidated. The aim of this study is to i) explore the bone regeneration potential of TPMS scaffolds made of different stiffness biocompatible materials, to ii) analyze the influence of pre-seeding the scaffolds and increasing the post-operative resting period, and to iii) assess the influence of patient-specific parameters, such as age and mechanosensitivity, on outcomes. To perform this study, an in silico model of a goat tibia is used. The bone ingrowth within the scaffold pores was simulated with a mechano-driven model of bone regeneration. Results showed that the scaffold's architectural properties affect cellular diffusion and strain distribution, resulting in variations in the regenerated bone volume and distribution. The softer material improved the bone ingrowth. An initial resting period improved the bone ingrowth but not enough to reach the scaffold's core. However, this was achieved with the implantation of a pre-seeded scaffold. Physiological parameters like age and health of the patient also influence the bone regeneration outcome, though to a lesser extent than the scaffold design. This analysis demonstrates the importance of the scaffold's geometry and its material, and highlights the potential of using mechanobiological patient-specific models in the design process for bone substitutes.

Fe3O4@SiO2 functionalized PEG-PPG-PEG triblock copolymer-grafted graphene oxide as novel magnetic nanodemulsifier for water-in-crude oil emulsion separation

The formation of water-in-crude oil emulsions during oil production and refining is a serious problem in oil industry. Chemical demulsification is a crucial and urgent treatment step for such emulsions. In this research, a novel magnetic nanodemulsifier (MND) has been synthesized by grafting graphene oxide nanosheets with poly(ethylene glycol)-block-poly(propylene glycol)-block-poly(ethylene glycol) triblock copolymer functionalized Fe3O4@SiO2, and characterized by a range of spectroscopic techniques. The prepared MND has an SBET of 534 m2/g, Ms value of 25.05 emu/g, and a positive surface charge of 23.1 mV. Our novel MND has a water contact angle of 58° and oil contact angle of 76.6°, which classifies it as an amphiphilic nanodemulsifier. The measured dynamic IFT for blank water/oil emulsion is 60.5 mN/m, while it is reduced to 13.25 mN/m 600 s after adding MND. The MND can quickly demulsify water/oil emulsions with 100% demulsification rate at 1900 ppm dose, 80 ℃ demulsification temperature and 5 min settling time. The demulsifier is reusable for at least four cycles, can effectively demulsify water/oil emulsions at pH 2–6 with demulsification efficiency between 82.14 % and 99.3 %, and salt water/oil emulsions with 83.21 % and 94.46 % DE for 2000 and 4000 ppm salt content, respectively. A demulsification mechanism was proposed, which showed that the prepared MND could efficiently adsorb at the oil-water interface via hydrogen-bonding with water droplets along the triblock copolymer chains, while the high density and high surface area oleophilic graphene oxide nanosheets contribute to efficient dispersibility of the MND in the oil phase. The MND’s spider-like architecture, nano-size, amphiphilic and magnetic properties, high density and high surface area work synergistically to facilitate dispersibility of the MND and promote fast demulsification.