Analyte Transport Research Articles

Analytical model for two-channel phonon transport engineering

The reduction of vibrational contributions to thermal transport and the search for material classes with intrinsically low lattice thermal conductivities are at the heart of thermoelectric research. Both engineering the heat transport of known thermoelectrics and searching for new material candidates is guided by understanding the physics of low thermal conduction. Spectral analytical models (e.g., the Callaway model) for propagating phonon transport have proved to be a powerful tool for interpreting experimental results and providing metrics for materials design. Now, however, it is known that another mechanism of phonon heat transport can occur in complex crystalline materials. Called diffusons, they describe the non-propagating atomic scale random-walk of thermal energy between energetically proximal phonon modes. While analytical models exist to describe both transport behaviors independently, an analytical model accounting for both transport channels simultaneously is necessary to interpret and design so-called 2-channel thermal transport. In this work, we propose an analytical 2-channel transport model that partitions the vibrational density of states into two transport regimes and subsequently accounts for both transport mechanisms. The model is then used to explain the experimental thermal conductivities of the solid solution series Ag9-xGa1-xGexSe6. In this series, substitution leads to the stabilization of a highly vacant Ag+ substructure, which is expected to induce strong point-defect phonon scattering. While the propagating phonons are strongly scattered at low temperatures, the diffuson channel is apparently unaffected. By establishing materials design metrics for 2-channel thermal transport from analytical theory, experimental investigations of materials with astonishingly low lattice thermal conductivities can now be better guided and informed.

Enhancement of Molecular Transport into Film Stacked Structures for Micro-Immunoassay by Unsteady Rotation

A film-stacked structure consisting of polyethylene terephthalate (PET) films stacked in a gap of 20 µm that can be combined with 96-well microplates used in biochemical analysis has been developed by the authors. When this structure is inserted into a well and rotated, convection flow is generated in the narrow gaps between the films to enhance the chemical/bio reaction between the molecules. However, since the main component of the flow is a swirling flow, only a part of the solution circulates into the gaps, and reaction efficiency is not achieved as designed. In this study, an unsteady rotation is applied to promote the analyte transport into the gaps using the secondary flow generated on the surface of the rotating disk. Finite element analysis is used to evaluate the changes in flow and concentration distribution for each rotation operation and to optimize the rotation conditions. In addition, the molecular binding ratio for each rotation condition is evaluated. It is shown that the unsteady rotation accelerates the binding reaction of proteins in an ELISA (Enzyme Linked Immunosorbent Assay), a type of immunoassay.

Computationally efficient Monte Carlo electron transport algorithm for nanostructured thermoelectric material configurations

Monte Carlo statistical ray-tracing methods are commonly employed to simulate carrier transport in nanostructured materials. In the case of a large degree of nanostructuring and under linear response (small driving fields), these simulations tend to be computationally overly expensive due to the difficulty in gathering the required flux statistics. Here, we present a novel Monte Carlo ray-tracing algorithm with computational efficiency of at least an order of magnitude compared to existing algorithms. Our new method, which is a hybrid of the analytical Boltzmann transport equation and Monte Carlo used a reduced number of ray-tracing particles, avoids current statistical challenges, such as the subtraction of two opposite going fluxes, the application of a driving force altogether, and the large simulation time required for low-energy carriers. We demonstrate the algorithm’s efficiency and power in accurate simulations in large domain nanostructures with multiple defects. We believe that the new method we present is indeed more robust and user friendly compared to common methods and can enable the efficient study of transport in nanostructured materials under low-field steady-state conditions.

Chemiresistive Hydrogen Sensing with Size-Limited Palladium Nanoparticles in Iptycene-Containing Poly(arylene ether)s

Metal nanoparticles have been widely employed in chemical sensing due to their high reactivity toward various gases. The size of the metal nanoparticles often dictates their reactivity and hence their performance as chemiresistive sensors. Herein, we report that iptycene-containing poly(arylene ether)s (PAEs) have been shown to limit the growth of palladium nanoparticles (Pd NPs) and stabilize the Pd NPs dispersion. These porous PAEs also facilitate the efficient transport of analytes. Single-walled carbon nanotube (SWCNT)-based chemiresistors and graphene field-effect transistors (GFETs) using these PAE-supported small Pd NPs are sensitive, selective, and robust sensory materials for hydrogen gas under ambient conditions. Generalizable strategies including presorting SWCNTs with pentiptycene-containing poly(p-phenylene ethynylene)s (PPEs) and thermal annealing demonstrated significant improvements in the chemiresistive performance. The polymer:NP colloids produced in this study are readily synthesized and solution processable, and these methods are of general utility.

Solid-State Nanopores for Biomolecular Analysis and Detection.

Advances in nanopore technology and data processing have rendered DNA sequencing highly accessible, unlocking a new realm of biotechnological opportunities. Commercially available nanopores for DNA sequencing are of biological origin and have certain disadvantages such as having specific environmental requirements to retain functionality. Solid-state nanopores have received increased attention as modular systems with controllable characteristics that enable deployment in non-physiological milieu. Thus, we focus our review on summarizing recent innovations in the field of solid-state nanopores to envision the future of this technology for biomolecular analysis and detection. We begin by introducing the physical aspects of nanopore measurements ranging from interfacial interactions at pore and electrode surfaces to mass transport of analytes and data analysis of recorded signals. Then, developments in nanopore fabrication and post-processing techniques with the pros and cons of different methodologies are examined. Subsequently, progress to facilitate DNA sequencing using solid-state nanopores is described to assess how this platform is evolving to tackle the more complex challenge of protein sequencing. Beyond sequencing, we highlight the recent developments in biosensing of nucleic acids, proteins, and sugars and conclude with an outlook on the frontiers of nanopore technologies.

A photonic biosensor-integrated tissue chip platform for real-time sensing of lung epithelial inflammatory markers.

Tissue chip (TC) devices, also known as microphysiological systems (MPS) or organ chips (OCs or OoCs), seek to mimic human physiology on a small scale. They are intended to improve upon animal models in terms of reproducibility and human relevance, at a lower monetary and ethical cost. Virtually all TC systems are analyzed at an endpoint, leading to widespread recognition that new methods are needed to enable sensing of specific biomolecules in real time, as they are being produced by the cells. To address this need, we incorporated photonic biosensors for inflammatory cytokines into a model TC. Human bronchial epithelial cells seeded in a microfluidic device were stimulated with lipopolysaccharide, and the cytokines secreted in response sensed in real time. Sensing analyte transport through the TC in response to disruption of tissue barrier was also demonstrated. This work demonstrates the first application of photonic sensors to a human TC device, and will enable new applications in drug development and disease modeling.

Microfabricated acoustofluidic membrane acoustic waveguide actuator for highly localized in-droplet dynamic particle manipulation.

Precision manipulation techniques in microfluidics often rely on ultrasonic actuators to generate displacement and pressure fields in a liquid. However, strategies to enhance and confine the acoustofluidic forces often work against miniaturization and reproducibility in fabrication. This study presents microfabricated piezoelectric thin film membranes made via silicon diffusion for guided flexural wave generation as promising acoustofluidic actuators with low frequency, voltage, and power requirements. The guided wave propagation can be dynamically controlled to tune and confine the induced acoustofluidic radiation force and streaming. This provides for highly localized dynamic particle manipulation functionalities such as multidirectional transport, patterning, and trapping. The device combines the advantages of microfabrication and advanced acoustofluidic capabilities into a miniature "drop-and-actuate" chip that is mechanically robust and features a high degree of reproducibility for large-scale production. The membrane acoustic waveguide actuators offer a promising pathway for acoustofluidic applications such as biosensing, organoid production, and in situ analyte transport.

Analytical multispecies chemical mixture transport model comprising degradable byproducts subject to scale-dependent dispersion

Analytical multispecies chemical mixture transport model comprising degradable byproducts subject to scale-dependent dispersion

Ordered Macro/Microporous Ionic Organic Framework for Efficient Separation of Toxic Pollutants from Water.

In case of pollutant segregation, fast mass diffusion is a fundamental criterion in order to achieve improved performance. The rapid mass transport through porous materials can be achieved by availing large open pores followed by easy and complete accessibility of functional sites. Inducing macroporosity into such materials could serve as ideal solution providing access to large macropores that offer unhindered transport of analyte and full exposure to interactive sites. Moreover, the challenge to configure the ionic-functionality with macroporosity could emerge as an unparalleled avenue toward pollutants separation. Herein, we strategized a synthetic protocol for construction of a positively charged hierarchically-porous ordered interconnected macro-structure of organic framework where the size and number of macropores can easily be tuned. The ordered macropores with strong electrostatic interaction synergistically exhibited ultrafast removal efficiency towards various toxic pollutants.

Ordered Macro/Microporous Ionic Organic Framework for Efficient Separation of Toxic Pollutants from Water

AbstractIn case of pollutant segregation, fast mass diffusion is a fundamental criterion in order to achieve improved performance. The rapid mass transport through porous materials can be achieved by availing large open pores followed by easy and complete accessibility of functional sites. Inducing macroporosity into such materials could serve as ideal solution providing access to large macropores that offer unhindered transport of analyte and full exposure to interactive sites. Moreover, the challenge to configure the ionic‐functionality with macroporosity could emerge as an unparalleled avenue toward pollutants separation. Herein, we strategized a synthetic protocol for construction of a positively charged hierarchically‐porous ordered interconnected macro‐structure of organic framework where the size and number of macropores can easily be tuned. The ordered macropores with strong electrostatic interaction synergistically exhibited ultrafast removal efficiency towards various toxic pollutants.

Photothermomechanical Nanopump: A Flow-Through Plasmonic Sensor at the Fiber Tip.

Optical fibers equipped with plasmonic flow sensors at their tips are fabricated and investigated as photothermomechanical nanopumps for the active transport of target analytes to the sensor surface. The nanopumps are prepared using a bottom-up strategy: i.e., by sequentially stacking a monolayer of a thermoresponsive polymer and a plasmonic nanohole array on an optical fiber tip. The temperature-dependent collapse and swelling of the polymer is used to create a flow-through pumping mechanism. The heat required for pumping is generated by exploiting the photothermal effect in the plasmonic nanohole array upon irradiation with laser light (405 nm). Simultaneous detection of analytes by the plasmonic sensor is achieved by monitoring changes in its optical response at longer wavelengths (∼500-800 nm). Active mass transport by pumping through the holes of the plasmonic nanohole array is visualized by particle imaging velocimetry. Finally, the performance of the photothermomechanical nanopumps is investigated for two types of analytes, namely nanoscale objects (gold nanoparticles) and molecules (11-mercaptoundecanoic acid). In the presence of the pumping mechanism, a 4-fold increase in sensitivity was observed compared to the purely photothermal effect, demonstrating the potential of the presented photothermomechanical nanopumps for sensing applications.

THEORETICAL APPROACHES TO DETERMINING THE ESSENCE OF THE CONCEPT OF "EXPORT AND LOGISTICS POTENTIAL"

The work is devoted to topical issues of theoretical research of scientific approaches to interpreting the concepts of "export potential" and "logistics potential". The purpose of the article is the research and systematization of theoretical approaches to the content of the above concepts, as well as the scientific substantiation of the essence of the "export-logistics potential" category. The research uses general scientific and unique methods: inductive and deductive - to justify approaches to determining the essence of export and logistics potential concepts; comparative - to outline the typical characteristics of the studied images within the framework of resource and system approaches; graphic - for a visual representation of the "export and logistics potential" definition; abstract and logical - for generalization and formation of conclusions. The author's interpretation of the essence of "export and logistics potential" is proposed, under which it is appropriate to understand the component of the production and economic potential of the state, which affects the formation of the national economy structure, determining its profile and degree of involvement in the system of foreign economic relations and global logistics networks. It has been studied that the export-logistics potential ensures efficient trade, which meets international standards and minimizes emissions into the environment. It has been established that the formation of the export-logistics potential is a complex, dynamic process aimed at interacting with the external environment and ensuring high quality of export products selling. It was established that the export-logistics potential provides efficient export goods traffic, associated with minimal expenditure of time and financial resources, as well as analytical transport support that meets international standards and minimizes harmful environmental emissions. It is substantiated that the export-logistics potential is characterized by the presence of hierarchical levels: micro- (enterprises), meso- (region), macro- (state), and mega-levels (international level).

Impact of transversal vortices on the performance of open-tubular liquid chromatography

Open-Tubular Liquid Chromatography (OTLC) is currently limited by two shortcomings, namely the low ratio of adsorbing area to the channel volume and the large values of the Height Equivalent of the Theoretical Plate (HETP) due to Taylor-Aris dispersion. Previous work focusing on axial dispersion of nonadsorbing solutes showed how it is possible to tame the Taylor-Aris effect by inducing transversal velocity components acting alongside the main pressure-driven axial flow. We here analyze the impact of transversal flow on the separation resolution in OTLC, where simultaneous equilibrium adsorption at the channel walls is superimposed to the analyte transport in the mobile phase. A three-dimensional steady flow generated by the combination of a pressure-driven flow and an electroosmotically-induced transversal flow is used as case study. Flows geometries possessing regular and chaotic streamlines are created by axially-invariant and periodically-alternate arrangements of the electrodes along the channel walls, respectively. By enforcing Brenner’s macrotransport approach, we predict the column length achieving a prescribed level of resolution as a function of the Péclet number and of the species affinity towards the stationary adsorbing phase. Results show that the presence of transversal flows can lower sensitively the dependence of the column length on the Péclet number. Flows possessing chaotic streamlines prove the most efficient choice at large eluent velocities and low values of the column adsorption constant.

Numerical Caseology by Lagrange Interpolation for the 1D Neutron Transport Equation in a Slab

Here, we are concerned with a new, highly precise, numerical solution to the one-dimensional neutron transport equation based on Case’s analytical, singular eigenfunction expansion (SEE). While a considerable number of numerical solutions currently exist, understandably, because of its complexity even in one dimension, there are only a few truly analytical solutions to the neutron transport equation. In 1960, Case introduced a consistent theory of the SEE for a variety of idealized transport problems and forever changed the landscape of analytical transport theory. Several numerical methods, including the Fn method, were based on the theory. What is presented is yet another, called the Lagrange order N method (LNM) featuring the simplicity and precision of the Fn method, but for a more convenient and natural Lagrangian polynomial basis.

Streambed water flux characterization through a Deep-Learning-Based approach considering data worth analysis: Numerical modeling and sandbox experiments

• An approach named CNN-DWA is proposed for estimating streambed water fluxes. • High-utility data are chosen through data worth analysis for CNN model training. • A few chosen data points provide similar information content to entire dataset. • CNN-DWA achieves more accurate flux estimations than the previously used IES. Quantifying streambed water fluxes (SWFs) is a prerequisite for studying the transport and fate of contaminants in the hyporheic zone. One approach, the heat tracing technique combined with analytical or numerical heat transport models has been widely used to quantify SWFs. However, both analytical and numerical methods are limited. For example, analytical methods are built upon rarely met assumptions, while the inversion performance of numerical methods can be impeded by uncertainties from different sources. To overcome these limitations, we introduce a new approach that combines convolutional neural networks (CNNs) with the Bayesian data worth analysis (DWA) framework for estimating SWFs. In this approach, a synthetic dataset is first generated through numerical simulations of groundwater flow and heat transport in streambeds. Then high-utility temperatures are chosen from the synthetic dataset through Bayesian DWA and further used to train the CNN model. We assess the capability of Bayesian DWA in improving the SWF estimation accuracy of CNN models through both numerical and sandbox experimental cases. Results show that the CNN model trained on temperatures with higher utility generally gives more accurate SWF estimations; use of a few high-utility temperature measurements can provide near similar constraint on the CNN model training and SWF estimation as compared to the full time series. Furthermore, a comparison to the iterative ensemble smoother algorithm shows the proposed inversion approach can estimate the flux field in the sandbox more accurately. The proposed CNN-based inversion approach considering DWA (CNN-DWA) would be beneficial more broadly for other hydrological problems.

A Simple Two-Dimensional Convective-Diffusive Based Modelling Study of a Biosensor for Glucose Detection

Over the past decade, novel technologies for biomolecular detection of target molecules of interest have been reported. The chemical reactions that govern the kinetics of the transport of target molecules to the sensor are also of significant importance. Here we analyze how the diffusion, convection, and reaction kinetics can drive the experimental design of a simple glucose biosensor. In this work, a physically intuitive and practical understanding of analyte transport is presented by modelling and simulating a two-dimensional convective-diffusive transport phenomenon in COMSOL Multiphysics to understand the mechanism of surface capture dynamics of a biosensor under the assumption of perfect binding kinetics. The target molecule of interest is glucose. In the model, 1 mol/m3 of the glucose solution is introduced at the inlet at 1 mm/s, and the process is simulated as a stationary study. It was found that by decreasing the flow rate, a threshold exists where the detection of target molecules by the sensor is solely controlled by diffusion. Comparison of convective and diffusive fluxes of the system as well as the concentration distribution reveal that a decrease in flow rate increases diffusive transport, thus increasing the fraction of target molecules that are captured by the sensor.

Convection Driven Ultrarapid Protein Detection via Nanobody-Functionalized Organic Electrochemical Transistors.

Conventional biosensors rely on the diffusion-dominated transport of the target analyte to the sensor surface. Consequently, they require an incubation step that may take several hours to allow for the capture of analyte molecules by sensor biorecognition sites. This incubation step is a primary cause of long sample-to-result times. Here, alternating current electrothermal flow (ACET) is integrated in an organic electrochemical transistor (OECT)-based sensor to accelerate the device operation. ACET is applied to the gate electrode functionalized with nanobody-SpyCatcher fusion proteins. Using the SARS-CoV-2 spike protein in human saliva as an example target, it is shown that ACET enables protein recognition within only 2 min of sample exposure, supporting its use in clinical practice. The ACET integrated sensor exhibits better selectivity, higher sensitivity, and lower limit of detection than the equivalent sensor with diffusion-dominated operation. The performance of ACET integrated sensors is compared with two types of organic semiconductors in the channel and grounds for device-to-device variations are investigated. The results provide guidelines for the channel material choice in OECT-based biochemical sensors, and demonstrate that ACET integration substantially decreases the detection speed while increasing the sensitivity and selectivity of transistor-based sensors.

Transport resilience to weather and climate: an interdisciplinary view from Rio de Janeiro

Weather causes damage and disruption to public transport, especially in developing megacities where transport demand is high, trip-lengths can be long, and poor socio-economic conditions exacerbate impacts. Here, an analytical framework overviews urban transport resilience to current weather and future climate in Rio de Janeiro. It describes how heavy rainfall and high temperatures impact on rail, metro and Bus Rapid Transit networks, and characterises the triggers, actors and linkages that combine to create barriers or pathways to transport resilience. There are three improvements to weather and climate resilience, namely, (a) the creation of Centre of Operations Rio (Centro de Operações Rio; COR) to co-ordinate daily operations and disaster response, (b) a series of innovations in operational integration enabled by co-locating services within COR and (c) infrastructure investment prior to the Olympic Games, which increased transport provision. The results highlight the need for integration and leadership across the private transport sector and demonstrate how resilience to current weather and future climate is intrinsically linked to sustainable urban mobility and should be considered in state and municipal planning strategies for housing, public services, and commercial and industrial development. Without adaptation, climate change will exacerbate existing systemic problems identified by the framework.

Mapping Thermoelectric Transport in a Multicomponent Alloy Space

AbstractInterest in high entropy alloy thermoelectric materials is predicated on achieving ultralow lattice thermal conductivity κL through large compositional disorder. However, here it is shown that for a given mechanism, such as mass contrast phonon scattering, κL will be minimized along the binary alloy with highest mass contrast, such that adding an intermediate mass atom to increase atomic disorder can increase thermal conductivity. Only when each component adds an independent scattering mechanism (such as adding strain fluctuation to an existing mass fluctuation) is there a benefit. In addition, both charge carriers and heat‐carrying phonons are known to experience scattering due to alloying effects, leading to a trade‐off in thermoelectric performance. Analytic transport models are applied, based on perturbation and effective medium theories, to predict how alloy scattering will affect the thermal and electronic transport across the full compositional range of several pseudo‐ternary and pseudo‐quaternary alloy systems. To do so, a multicomponent extension is demonstrated to both thermal and electronic binary alloy scattering models based on the virtual crystal approximation. Finally, it is shown that common functional forms used in computational thermodynamics can be applied to this problem to further generalize the scattering behavior that is modeled.

Directed Particle Transport via Reconfigurable Fiber Networks

AbstractMass transport limitations of particulates within conventional microanalytical systems are often cited as the root cause for low sensitivity but can be overcome by directed analyte transport, such as via biomolecular motors or gradient surfaces. An ongoing challenge is the development of materials that are passive in nature (i.e., no external power source required), but can reconfigure to perform work, such as transporting particle‐based analytes. Mimicking biology's concepts of autonomous and reconfigurable materials systems, like the Drosera capensis leaf, reconfigurable fiber networks that effectively concentrate particulates within a localized spot that can act as a detection patch are developed. These networks, prepared by electrohydrodynamic co‐jetting, draw their reconfigurability from a bicompartmental fiber architecture. Upon exposure to neutral pH, a differential swelling of both fiber compartments gives rise to interfacial tension and ultimately results in shape reconfiguration of the fiber network. Compared to free particles, the reconfigurable fiber networks display a 57‐fold increase in analyte detectability, average transport efficiencies of 91.9 ± 2.4%, and separation selectivity between different surface properties of 95 ± 3%. The integration of biomimetic materials into microanalytical systems, exemplified in this study, offers ample opportunities to design novel and effective detection schemes that circumvent mass transport limitations.