Changes In Surface Coverage Research Articles

Predicting climate driven habitat shifts for the Egyptian vulture in Punjab, Pakistan

Climate change has significantly impacted habitat loss, affecting various threatened species, including the Egyptian vulture, which is experiencing a population and habitat decline in Pakistan. This study employs MaxEnt modeling to predict the current and future distribution of the Egyptian vulture across Punjab province, Pakistan, under three Shared Socio-economic Pathways (SSPs) SSP126, SSP370, and SSP585 for the years 2040, 2070, and 2100. We used 67 occurrence records and seven environmental variables to model the vulture’s distribution. The MaxEnt model exhibited good predictive performance with an AUC value of 0.837, identifying a current suitable habitat area of 122,124.16 km2. Among the environmental factors analyzed, precipitation seasonality (Bio15), mean temperature of the warmest quarter (Bio10), and precipitation of the wettest month (Bio13) emerged as the most influential variables, contributing 39.8 %, 18 %, and 8.8 %, respectively. The model predicts a substantial shift in suitable habitats under various climate scenarios, with an overall increase of 69 % in suitable habitat by 2100 under the SSP 370–2100 scenario. Significant habitat expansions are expected across central and southern Punjab, while reductions are anticipated in the northern and western regions, with significant changes in surface coverage. Furthermore, the conservation gap analysis reveals that although 2,451.63 km2 of highly suitable and 1,648.91 km2 of very highly suitable habitat exist within protected areas, more than 90 % of these habitats remain unprotected, highlighting a critical conservation gap that threatens the species’ long term survival. The insights derived from this study are critical for informing conservation efforts and habitat management practices, offering a foundational reference for the protection of the Egyptian vulture and similar species in the face of ongoing climate change.

Surface water and geomorphological changes of the Blue Nile dynamics associated with the Grand Ethiopian Renaissance Dam (GERD): a multi-temporal analysis

ABSTRACT The Blue Nile River is an indispensable source of livelihood for over 90 million people in Ethiopia, Sudan, and Egypt. Recently, Ethiopia has constructed the Grand Ethiopian Renaissance Dam (GERD), the largest in Africa, which could potentially alter the geomorphology of the river regime, and impact downstream countries. This study offers the first comprehensive analysis of the geomorphological alterations and water surface coverage changes of the Blue Nile River due to the construction and operation of the GERD. For this, we use multi-temporal Landsat 5 and Sentinel-2 satellite imagery spanning the period from 2009 to 2022, supplemented with Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) precipitation products to discern the potential influence of climate variability on the observed changes. The findings are examined using the Before-After-Control-Impact (BACI) model, revealing significant fluctuations in the water surface coverage area across different sections of the river, particularly following the filling stages of the GERD. An analysis at every 10 km is performed to assess the extent of GERD’s influence on the Blue Nile, which indicates no notable alterations in surface area or geomorphology within the first 30 km of downstream river flow. The study also determines that changes from 2011 to 2018 are primarily associated with the upgrade activities of the Roseires Dam, while alterations along the Blue Nile from 2018 to 2019 are linked to climate variability. The observed changes between 2020 and 2022 can largely be attributed to the filling stages of the GERD. Notably, these changes are most pronounced in the water retaining areas upstream of the GERD dam. The results also show that Roseires Dam could play a crucial role as a buffer, mitigating potential impacts imposed by the GERD, and serving as a conduit between the GERD and downstream communities.

First principle based rate equation (1pRE) for reduction kinetics of Fe2O3 with syngas in chemical looping

Chemical looping combustion (CLC) of solid fuel is a promising technology with inherent CO2 separation and low energy penalty for CO2 capture. Syngas is main intermediate species of solid fuel conversion in CLC, the reduction kinetics of oxygen carriers with syngas play a crucial role in CLC systems. However, the current research obtains the reaction rate constants by fitting the apparent models with the experimental data, and cannot explain the reduction kinetics behavior from a microscopic level. It remains a challenge to compute the reduction kinetics of oxygen carriers with syngas directly from first-principles density functional theory (DFT) without fitting experimental data. This study proposes a first-principle-based rate equation (1pRE) theory and integrates it into the random pore model (RPM) to predict the kinetics of Fe2O3 reduction by syngas in CLC. The developed 1pRE theory utilizes DFT calculations to search for reaction pathways and energy barriers of elementary reactions. Then the DFT data are introduced into the statistical mechanics partition function and transition state theory (TST) to calculate the reaction rate constants. Microkinetic rate equations of elementary reactions occurring at the surface scale are developed to describe the change of surface coverage of different surface species. The 1pRE theory is integrated into the RPM to account for the influence of particle-scale structural changes on the overall conversion rate during the reduction process. The theory can predict the reduction kinetics of oxygen carriers without fitting experimental data and establishes a connection between microscopic insights and macroscopic phenomena. The accuracy was validated by experimental data of Fe2O3 oxygen carriers obtained from the thermogravimetric analyzer (TGA) in the atmosphere of syngas. The developed 1pRE predicts the reduction kinetics of oxygen carriers accurately and can be used to optimize the design of oxygen carrier materials and the scale up of CLC reactors.

Time‐Resolved Product Observation for CO2 Electroreduction Using Synchronised Electrochemistry‐Mass Spectrometry with Soft Ionisation (sEC‐MS‐SI)

AbstractThe mechanistic understanding of electrochemical CO2 reduction reaction (CO2RR) requires a rapid and accurate characterisation of product distribution to unravel the activity and selectivity, which is yet hampered by the lack of advanced correlative approaches. Here, we present the time‐resolved identification of CO2RR products by using the synchronised electrochemistry‐mass spectrometry (sEC‐MS). Transients in product formation can be readily captured in relation to electrochemical conditions. Moreover, a soft ionisation (SI) strategy is developed in MS for the direct observation of CO, immune to the interference of CO2 fragments. With the sEC‐MS‐SI, the kinetic information, such as Tafel slopes and onset potentials, for a myriad of CO2RR products are revealed and we show the hysteresis seen for the evolution of some species may originate from the potential‐driven changes in surface coverage of intermediates. This work provides a real‐time picture of the dynamic formation of CO2RR products.

Time-Resolved Product Observation for CO2 Electroreduction Using Synchronised Electrochemistry-Mass Spectrometry with Soft Ionisation (sEC-MS-SI).

The mechanistic understanding of electrochemical CO2 reduction reaction (CO2 RR) requires a rapid and accurate characterisation of product distribution to unravel the activity and selectivity, which is yet hampered by the lack of advanced correlative approaches. Here, we present the time-resolved identification of CO2 RR products by using the synchronised electrochemistry-mass spectrometry (sEC-MS). Transients in product formation can be readily captured in relation to electrochemical conditions. Moreover, a soft ionisation (SI) strategy is developed in MS for the direct observation of CO, immune to the interference of CO2 fragments. With the sEC-MS-SI, the kinetic information, such as Tafel slopes and onset potentials, for a myriad of CO2 RR products are revealed and we show the hysteresis seen for the evolution of some species may originate from the potential-driven changes in surface coverage of intermediates. This work provides a real-time picture of the dynamic formation of CO2 RR products.

Molecular Dynamics Simulations on the Adsorbed Monolayers of N-Dodecyl Betaine at the Air-Water Interface.

Betaine is a kind of zwitterionic surfactant with both positive and negative charge groups on the polar head, showing good surface activity and aggregation behaviors. The interfacial adsorption, structures and properties of n-dodecyl betaine (NDB) at different surface coverages at the air-water interface are studied through molecular dynamics (MD) simulations. Interactions between the polar heads and water molecules, the distribution of water molecules around polar heads, the tilt angle of the NDB molecule, polar head and tail chain with respect to the surface normal, the conformations and lengths of the tail chain, and the interfacial thickness of the NDB monolayer are analyzed. The change of surface coverage hardly affects the locations and spatial distributions of the water molecules around the polar heads. As more NDB molecules are adsorbed at the air-water interface, the number of hydrogen bonds between polar heads and water molecules slightly decreases, while the lifetimes of hydrogen bonds become larger. With the increase in surface coverage, less gauche defects along the alkyl chain and longer NDB chain are obtained. The thickness of the NDB monolayer also increases. At large surface coverages, tilted angles of the polar head, tail chain and whole NDB molecule show little change with the increase in surface area. Surface coverages can change the tendency of polar heads and the tail chain for the surface normal.

A compact and highly collimated atomic/molecular beam source

We describe the design, characterization, and application of a simple, highly collimated, and compact atomic/molecular beam source. This source is based on a segmented capillary design, constructed using a syringe needle. Angular width measurements and free molecular flow simulations show that the segmented structure effectively suppresses atoms traveling in off-axis directions, resulting in a narrow beam of Helium atoms having a width of 7 mrad (full width half maximum). We demonstrate an application of this source by using it for monitoring real-time changes in surface coverage on a clean Cu(110) surface exposed to oxygen by measuring the specular reflectivity of the Helium beam generated using this source.

Sensitive and selective DNA detecting electrochemical sensor via double cleaving CRISPR Cas12a and dual polymerization on hyperbranched rolling circle amplification

Electrochemical sensors are widely used for nucleic acid detection. However, they exhibit low sensitivity and specificity. To overcome these limitations, DNA amplification method is necessary. In this study, we introduced CRISPR (Clustered regularly interspaced short palindromic repeats) Cas12a-dependent hyperbranched rolling circle amplification (HRCA) into an electrochemical sensor platform. By resolving the existing false-positive issue of HRCA, CRISPR Cas12a determines the real positive amplification that able to enhance its sensitivity for extremely low concentrations of nucleic acids and specificity for single-point mutations. In detail, CRISPR Cas12a, which activates the nucleic acid amplification reaction, was used for both trans and cis cleavage for the first time. Finally, selectively amplified DNA was detected using a screen-printed electrode. Using the change in surface coverage by DNA, the electrochemical sensor detected a decrease in the redox signal. In summary, combining a novel DNA amplification method and electrochemical sensor platform, our proposed method compensates for the shortcomings of existing RCA and hyperbranched RCA, secures a high sensitivity of 10 aM, and overcomes false-positivity problems. Moreover, such creative applications of CRISPR Cas12a may lead to the expansion of its applications to other nucleic acid amplification methods.

On the Impacts of Ice Cover on Flow Profiles in a Bend

AbstractWe investigate the impact of ice coverage on flow and bed shear stress profiles in a river bend. We perform field measurements using Acoustic Doppler Current Profiler in a bend of the Red River, North Dakota, the United States. Field campaigns were carried out under both open‐surface and ice‐covered conditions in 2020 and 2021. Our results show that the time‐averaged velocity profile follows closely the quartic solution (Guo et al., 2017, under full ice coverage. While the flow profile under open‐surface condition follows closely the logarithmic law near the bed, it is challenging to identify the logarithmic layers in our measured data under ice‐covered condition. Our results also show that the impact of ice coverage is most significant near both banks where the vertical velocity profile is modified significantly due to the interaction of turbulent flows with the ice cover. Our results suggest that the bend curvature and ice coverage both have significant impacts on the velocity profile as well as the distribution of the bed shear stresses. Our findings provide new insights on sediment transport processes of ice‐covered rivers, especially during the break‐up period when the surface coverage changes rapidly.

A Siamese Network Based U-Net for Change Detection in High Resolution Remote Sensing Images

Remote sensing image change detection (RSICD) is a technique that explores the change of surface coverage in a certain time series by studying the difference between multiple remote sensing images (RSIs) collected over the same area. Traditional RSICD algorithms exhibit poor performance on complex change detection (CD) tasks. In recent years, deep learning (DL) techniques have achieved outstanding results in the fields of RSI segmentation and target recognition. In CD research, most of the methods treat multitemporal remote sensing data as one input and directly apply DL-based image segmentation theory on it while ignoring the spatio-temporal information in these images. In this article, a new siamese neural network is designed by combing an attention mechanism (Siamese_AUNet) with UNet to solve the problems of RSICD algorithms. SiameseNet encodes the feature extraction of RSIs by two branches in the siamese network, respectively. The weights are shared between these two branches in siamese networks. Subsequently, an attention mechanism is added to the model in order to improve its detection ability for changed objects. The models are then compared with conventional neural networks using three benchmark datasets. The results show that the Siamese_AUNet newly proposed in this article exhibits better performance than other standard methods when solving problems related to weak CD and noise suppression.

Continuous evaluation of the spatial representativeness of land surface temperature validation sites

A reliable accuracy is essential for the application of land surface temperature (LST) products. Current satellite retrieved LSTs are mainly validated over a few homogeneous sites. However, most of the existing ground sites are located in inhomogeneous areas: thus, their spatial representativeness on satellite pixel scales is unknown. In this situation, how to evaluate the spatial representativeness of these inhomogeneous sites, quantify the influence introduced by the spatial representativeness and describe the variation of the site's spatial representativeness are critical questions for satellite LST validation. In an attempt to answer those questions, a so-called temporal variation method (TVM) is proposed for evaluating a ground site's spatial representativeness. The method defines a spatial representativeness indicator (SRI), which is the LST difference between a ground radiometer's field-of-view (FOV) and a satellite pixel, and describes a site's spatial representativeness. Based on the temporal variation of LST, the SRI time series consists of three temporal components: ∆ATC, ∆DTCF-P, and ∆USC, which describe the annual, diurnal, and instantaneous variations of SRI, respectively. Associated with the Landsat TM/ETM+ data and weather parameters, the method is implemented and tested at 16 Chinese ground sites for the validation of MODIS and AATSR LST products. Results show that the temporally continuous SRI (SRITPR) shows high correlations with the original SRI (SRIORI). The variation of SRITPR is mainly determined by changes in surface coverage (i.e. NDVI difference on the two scales) and affected by weather conditions (e.g. near-surface air temperature, accumulative downward solar radiation, and wind speed). Since the SRI is defined as the LST difference between the two scales, it can be used as a bridge to convert the in-situ LST to pixel scale to address the spatial scale mismatch in LST validation. With this idea, the in-situ LST at daytime was converted to pixel scale associated with the SRITPR, and the corresponding MODIS and AATSR LST were validated at the 16 ground sites. Results for MODIS and AATSR LST show that the effect of spatial representativeness on the validation results over the sites is large, with mean biases between −1.95 K and 5.60 K and standard deviations between 0.07 K and 3.72 K. Since the TVM method does not rely on a specific satellite or land surface product, it is readily applied to other LST products (e.g. Sentinel-3 SLSTR LST, NOAA VIIRS LST) and surface parameters (e.g. surface longwave radiation).

Characterization of reliability of anti-soiling coatings using tapping mode-AFM phase imaging

Power loss due to dust deposition on photovoltaic (PV) modules (also referred to as soiling) is a severe challenge to the economic viability of PV deployment in sunbelt countries like India and the Middle East. Anti-soiling coatings are nano/micrometer thick transparent coatings, which mitigate dust deposition on PV module. In this study, Tapping Mode Atomic Force Microscopy (TM-AFM) phase imaging was used to characterize the surface changes of four different commercial hydrophobic anti-soiling coatings as they were subjected to outdoor field exposure tests and three different indoor accelerated stress tests. The anti-soiling coatings were applied via manual spray coating method on solar glass substrates. The phase angle for not-coated glass shows a tight distribution around zero. The phase angle distribution of coated surface showed a larger spread. When the coatings were subjected to different environmental stressors, the phase angle distributions became tighter and, in some cases, collapsed to tight distributions around zero, indicating that the coatings were partly or completely removed from the glass surface. We demonstrate a correlation of the changes in surface coverage identified from phase angle distributions to contact angle and surface roughness, thus establishing TM-AFM phase imaging as a promising approach to characterize anti-soiling coatings subjected to environmental stress. While the analysis of the phase angle distribution is able to resolve the removal of the coating, contact angle and surface roughness cannot do so unambiguously.

High-Resolution Remote Sensing Image Information Extraction and Target Recognition Based on Multiple Information Fusion

The current research on multiple information fusion of remote sensing images is mainly aimed at remote sensing images of specific satellite sensors, and cannot be extended to other types of data source images. For high-resolution remote sensing images, when its surface coverage changes significantly, most of the mainstream algorithms are difficult to restore satisfactorily. The algorithm proposed in this paper combines the sparse representation and the spectral, spatial, and temporal features of remote sensing images for the first time to solve the above problems. The algorithm proposed in this paper first simulates the human visual mechanism, and obtains the spatial, spectral, and temporal features of the remote sensing image through the spatial spectral dictionary learning and the time-varying weight learning model. Secondly, local constraints are added to the extraction of temporal features to obtain temporal and geographical change information of heterogeneous remote sensing images. Then, a sparse representation model combining space-spectrum-time features is proposed to extract features of high-resolution remote sensing images. Finally, based on the VGG-16 network, this paper proposes a target recognition network with deep fully convolutional network, and uses the extracted feature map as the input of the target recognition network to realize the target recognition of the remote sensing image. Experimental results show that the method proposed in this paper can improve the accuracy of target recognition and improve the accuracy of recognition.

Combined Effects of the Surface Urban Heat Island with Landscape Composition and Configuration Based on Remote Sensing: A Case Study of Shanghai, China

Rapid urbanization leads to changes in surface coverage and landscape patterns. This results in urban heat island (UHI) effects and a series of negative ecological consequences. Considering this concern and taking Shanghai as an example, this paper concentrates on the effects of surface coverage and landscape patterns on urban land surface temperature (LST). The research is based on quantitative retrieval of remote sensing data with consideration of methods in multiple disciplines, including landscape ecology, geographic information systems, and statistical analysis. It concludes that, over time, the thermal environment of Shanghai is becoming critical. The average LST ranking of different surface coverage is as follows: Construction land (CL) > bare land (BL) > green land (GL) > agricultural land (AL) > water body (WB). LST varies significantly with the type of surface coverage. CL contributes the most to the UHI, while WB and GL have obvious mitigation effects on the UHI. The large area, low degree of landscape fragmentation, and complex outlines lead to low LST rankings for GL, WB, and AL and a high LST ranking for CL. The conclusions indicate that CL should be broken down by GL and WB into discrete pieces to effectively mitigate UHI effects. The research reveals UHI features and changes in Shanghai over the years and provides practical advice that can be used by urban planning authorities to mitigate UHI.

About the penetration of the diurnal and annual temperature variation into the subsurface

In order to evaluate the effect of the penetration of diurnal and annual wave temperature into the subsurface, the temperature has been monitored at an hourly recording frequency at depths of 40, 60 and 78 m between summer 2016 and summer 2018, at the geothermal experimental test site “Neutra” of the Georg-August-University of Göttingen, Germany. It has been asserted that the mean temperature gradient between 40 and 78 m continuously increases, because the temperature decreases at 40 m. The decrease can be explained by an increase in vegetation cover (trees, shrubs, etc.) in the perimeter of the test area, increasing the absorption of solar energy by the leaves. During the phenological growth season the diurnal temperature variation at the surface can be recorded in phase with opposite sign, even at a depth of 40 m, and the drop of the temperature at 40 m, when surface temperature reaches a value of nearly 9 °C, can be observed during small events of eco-dormancy during winter. The annual surface temperature variation of ±10 K induce the same effect with an amplitude of ±2mK at 40 m. It is stated that the dormant state of the vegetation cells is the reason of the annual variation of the residual temperature. At greater depths groundwater flows prevail and influence the temperature according to the structural properties of the encountered lithologies and the precipitation. The vegetation can transfer the daily and seasonal temperature variation to larger depths than expected based on the theory of heat conduction. This timely variation of the temperature gradient demonstrates that the determination of the terrestrial heat flow density is subject to several impacts induced from the surface as well as from the Earth’s interior. As a conclusion, temperature gradients determined at shallow depths may be influenced by changes in surface coverage.

Friction and adhesion control between adsorbed layers of polyelectrolyte brush-grafted nanoparticles via pH-triggered bridging interactions

HypothesisAdsorption of polyelectrolyte brush-grafted nanoparticles (BGNPs) produces a heterogeneous interface with sub-monolayer surface coverage resulting from lateral electrostatic repulsions that limit packing. As a result, the interaction forces between opposing BGNP layers include an adhesive cross-surface BGNP-substrate bridging force that depends on the interparticle spacing, particle size, and strength of electrostatic interactions. We hypothesize that BGNPs with pH-responsive, annealed polyelectrolyte brushes can undergo controlled changes in surface area coverage through post-adsorption swelling or de-swelling into non-equilibrium layer conformations and that such changes in surface coverage can switch off or switch on particle intercalation, bridging attractions, and enhanced energy dissipation upon sliding. This work aims to characterize the nature of surface forces in heterogeneous BGNP adsorbed layers and to utilize pH-sensitive bridging forces as a mechanism to tune friction and adhesion. ExperimentsColloidal probe atomic force microscopy (CP-AFM) is used to measure normal and lateral forces between negatively charged silica surfaces with adsorbed pH-responsive cationic BGNPs. The BGNPs are poly(2-dimethylaminoethyl methacrylate) brush-grafted silica nanoparticles. Adhesion force and friction analysis is complemented by simultaneous quartz-crystal microbalance and ellipsometry measurements under conditions that render the particles strongly charged and swollen (acidic) or weakly charged and de-swollen (basic). FindingsAdsorbed BGNPs can be swollen or de-swollen via pH rinses, enabling direct control of surface coverage and bridging interactions. Transitions from adhesive bridging contacts with high friction to non-adhesive contacts with low friction forces occur when adsorbed BGNP layers are switched from a de-swollen/weakly charged state to a swollen/highly charged state. The ability to controllably shift the character of normal and lateral forces via coverage-mediated bridging interactions is a unique feature of adsorbed nanoparticulate brush constructs and highlights their potential to condition surfaces with additional functionality compared to dense, planar homopolymer brushes.

The impact of fire on sand dune stability: Surface coverage and biomass recovery after fires on Western Australian coastal dune systems from 1988 to 2016

This study aims to determine the common response of coastal sand dunes in Western Australia (WA) to fire on decadal time-scales, in terms of ecological-geomorphic-climatic interactions to test the hypothesis that fire plays a role in coastal dune destabilisation. Fires are commonly suggested to have contributed to widespread dune reactivation in Australia and globally, a hypothesis that is relatively untested. We used data from the Landsat Thematic Mapper, Enhanced Thematic Mapper Plus, and Operational Land Imager missions to monitor changes in surface coverage on coastal sand dunes in south-west WA after fires. We analysed 31 fire scars from 1988 to 2016 in two Landsat scenes on the west and south coast of WA. Recovery ratios derived from the Normalised Difference Vegetation Index (NDVI) were used to monitor patterns in post-fire biomass and surface cover. Recovery ratios are correlated with indices of burn severity, and meteorological data to investigate relationships. We also used Maximum Likelihood Classification to monitor changes in bare sand area. Results suggest that recovery followed a strongly consistent pattern, and is characterised by rapid vegetation cover re-establishment within six to twelve months. Prior to this, some aeolian activity may have occurred but without substantial surface changes. Initial germination and/or resprouting were followed by steady growth up to seven years, where NDVI typically neared pre-fire values. Some variation in early recovery occurred between the west and south coast, possibly owing to relative proportions of reseeding and resprouting plants. A log regression explained 75% of the recovery pattern (79% on the south coast). Precipitation had some ability to explain recovery up to nine months post-fire (r2=0.29 to 0.54). No relationships were observed between estimates of burn severity and recovery. After nine months, the biggest cause of spatial variation in recovery was the pre-fire community composition and related seedbank or resprouting density. Image classification did not identify any new blowout features except where fires were not the primary cause. Results suggest that fires are not presently contributing to the destabilisation of coastal dunes in south-west WA.

Impact of helium ion energy modulation on tungsten surface morphology and nano-tendril growth

Time-modulated helium (He) ion energy (e.g. VBias = −50 + 25·sin(2πfRF · t), fRF = 13.56 MHz) is demonstrated to strongly affect the development of tungsten (W) surface morphology that results from He plasma irradiation in the DIONISOS linear plasma experiment. Nano-tendril bundles (NTBs), which appear as isolated ‘islands’ of nano-tendrils, can rapidly grow on an otherwise smooth W surface. This is in contrast to previously seen full-surface coverage of nano-tendril growth known as ‘fuzz’. When tall NTBs form, less than 15% of the surface contains nano-tendrils. The NTB surface coverage changes with growth conditions and the total volume of nano-tendrils in the NTBs is observed to be up to a factor of 16 larger than when fuzz is grown. This indicates that long-range W surface transport underlies nano-tendril formation.Surface temperature 870–1220 K, the DC bias potential −30 to −70 V, and the ion flux density 4.4 × 1021–1.1 × 1022 He · m−2 · s−1 are varied in the experiments. NTBs form at similar conditions as fuzz with the critical difference being the RF modulation of the ion energy bombarding the W, another indication of the importance of W surface transport. Mass loss measurements indicate net erosion with a yield of 1–8 × 10−4 W/He when NTBs form; erosion that is not attributable to chemical or physical sputtering by He or impurities in the plasma. The erosion is correlated to the NTB growth, based on post-exposure inspection by electron microscopy indicating that NTBs are prone to loss from the surface. NTB growth is compared to the empirical growth-erosion model of fuzz, showing NTBs grow up to a factor of 100 times taller than the expected fuzz layer depth under DC bias conditions. Insights into nano-tendril growth provided by this new growth regime are discussed. Strategies to mitigate W fuzz growth may inadvertently result in rapid localized nano-tendril bundle growth with a higher probability of dust production.

Adsorption of silica colloids onto like-charged silica surfaces of different roughness

Particle adsorption was explored in a model optical polishing system, consisting of silica colloids and like-charged silica surfaces. The adsorption was monitored in situ under various suspension conditions, in the absence of surfactants or organic modifiers, using a quartz crystal microbalance with dissipation monitoring (QCM-D). Changes in surface coverage with particle concentration, particle size, pH, ionic strength and ionic composition were quantified by QCM-D and further characterized ex situ by atomic force microscopy (AFM). A Monte Carlo model was used to describe the kinetics of particle deposition and provide insights on scaling with particle concentration. Transitions from near-zero adsorption to measurable adsorption were compared with equilibrium predictions made using the Deraguin-Verwey-Landau-Overbeek (DLVO) theory. In addition, the impact of silica surface roughness on the propensity for particle adsorption was studied on various spatial scale lengths by intentionally roughening the QCM sensor surface using polishing methods. It was found that a change in silica surface roughness at the AFM scale from 1.3nm root-mean-square (rms) to 2.7nmrms resulted in an increase in silica particle adsorption of 3-fold for 50-nm diameter particles and 1.3-fold for 100-nm diameter particles—far exceeding adsorption observed by altering suspension conditions alone, potentially because roughness at the proper scale reduces the total separation distance between particle and surface.

Dynamic analysis of platelet deposition to resolve platelet adhesion receptor activity in whole blood at arterial shear rate

Platelet activation is traditionally quantified using turbidimetric aggregometry, which reflects integrin αIIbβ3 activity, an important determinant of platelet function during pathophysiological thrombus formation. However, aggregometry does not recreate the shear conditions prevailing during thrombosis in vivo. Here we describe novel whole-frame analysis of real-time video microscopy to quantify platelet adhesion receptor activity under shear in parallel-plate flow chambers. We demonstrate that the rate of change of surface coverage (designated Platelet Population Mobility, PM) quantifies platelet mobility. On collagen, PM falls exponentially to a low level, corresponding to firm platelet adhesion, while on other substrates, PM remains high. Different receptor-specific thrombogenic surfaces reveal that the PM time constant reflects real-time changes in integrins αIIbβ3 and α2β1 activity. This ensemble kinetic analysis has the potential to provide valuable diagnostic information about platelet thrombus formation with both academic and clinical applications.