Mapping Of Dynamics Research Articles

Producing context specific land cover and land use maps of human-modified tropical forest landscapes for infectious disease applications

Satellite-based land cover mapping plays an important role in understanding changes in ecosystems and biodiversity. There are global land cover products available, however for region specific studies of drivers of infectious disease patterns, these can lack the spatial and thematic detail or accuracy required to capture key ecological processes. To overcome this, we produced our own Landsat derived 30 m maps for three districts in India's Western Ghats (Wayanad, Shivamogga and Sindhudurg). The maps locate natural vegetation types, plantation types, agricultural areas, water bodies and settlements in the landscape, all relevant to functional resource use of species involved in infectious disease dynamics. The maps represent the mode of 50 classification iterations and include a spatial measure of class stability derived from these iterations. Overall accuracies for Wayanad, Shivamogga and Sindhudurg are 94.7 % (SE 1.2 %), 88.9 % (SE 1.2 %) and 88.8 % (SE 2 %) respectively. Class classification stability was high across all three districts and the individual classes that matter for defining key interfaces between human habitation, forests, crop, and plantation cultivation, were generally well separated. A comparison with the 300 m global ESA CCI land cover map highlights lower ESA CCI class accuracies and the importance of increased spatial resolution when dealing with complex landscape mosaics. A comparison with the 30 m Global Forest Change product reveals an accurate mapping of forest loss and different dynamics between districts (i.e., Forests lost to Built-up versus Forests lost to Plantations), demonstrating an interesting complementarity between our maps and the % tree cover Global Forest Change product. When studying infectious disease responses to land use change in tropical forest ecosystems, we recommend using bespoke land cover/use classifications reflecting functional resource use by relevant vectors, reservoirs, and people. Alternatively, global products should be carefully validated with ground reference points representing locally relevant habitats.

Post-fire assessment of recovery of montane forest composition and stand parameters using in situ measurements and remote sensing data

Tree diversity in the tropical forests face escalating threats from wildfires. This study assessed post-fire impacts and recovery patterns in Tanzanian Eastern Arc Mountains forests from 2017 to 2022 using field measurements and remote sensing techniques. Tree species diversity, composition, and forest stand parameters were compared between burned and unburned forest plots across two reserves. A predictive model utilizing 14 key variables derived from multispectral satellite data was developed to accurately map burned areas and spatial fire patterns. Results revealed significantly lower tree density, aboveground biomass, species richness, and Shannon diversity in burned areas compared to unburned forests. However, compositional analysis showed extensive species overlap between burned and unburned sites, with burned areas containing more indicative pioneer and disturbance-adapted species such as Apodytes dimidiata. Over time since fire events, tree density, basal area, aboveground biomass, species richness, evenness, and diversity increased markedly, evidencing active tree recovery. The remote sensing model effectively delineated approximately 1430 hectares of burned areas concentrated near villages, suggesting prevalent anthropogenic fire ignitions. Although wildfires substantially impacted forest structure and biodiversity, the limited compositional shifts point to resilience of these tropical montane forests. Integration of diverse spectral bands and textural metrics from multispectral satellite data can support precise mapping of fire effects and forest recovery dynamics in these ecologically vital yet threatened ecosystems, aiding conservation and management. Overall, this study provides novel insights into post-fire responses in Eastern Arc Mountain forests using a synergistic field and remote sensing approaches.

High-speed mapping of surface charge dynamics using sparse scanning Kelvin probe force microscopy

Unraveling local dynamic charge processes is vital for progress in diverse fields, from microelectronics to energy storage. This relies on the ability to map charge carrier motion across multiple length- and timescales and understanding how these processes interact with the inherent material heterogeneities. Towards addressing this challenge, we introduce high-speed sparse scanning Kelvin probe force microscopy, which combines sparse scanning and image reconstruction. This approach is shown to enable sub-second imaging (>3 frames per second) of nanoscale charge dynamics, representing several orders of magnitude improvement over traditional Kelvin probe force microscopy imaging rates. Bridging this improved spatiotemporal resolution with macroscale device measurements, we successfully visualize electrochemically mediated diffusion of mobile surface ions on a LaAlO3/SrTiO3 planar device. Such processes are known to impact band-alignment and charge-transfer dynamics at these heterointerfaces. Furthermore, we monitor the diffusion of oxygen vacancies at the single grain level in polycrystalline TiO2. Through temperature-dependent measurements, we identify a charge diffusion activation energy of 0.18 eV, in good agreement with previously reported values and confirmed by DFT calculations. Together, these findings highlight the effectiveness and versatility of our method in understanding ionic charge carrier motion in microelectronics or nanoscale material systems.

Spatially resolved mapping of proteome turnover dynamics with subcellular precision

Cellular activities are commonly associated with dynamic proteomic changes at the subcellular level. Although several techniques are available to quantify whole-cell protein turnover dynamics, such measurements often lack sufficient spatial resolution at the subcellular level. Herein, we report the development of prox-SILAC method that combines proximity-dependent protein labeling (APEX2/HRP) with metabolic incorporation of stable isotopes (pulse-SILAC) to map newly synthesized proteins with subcellular spatial resolution. We apply prox-SILAC to investigate proteome dynamics in the mitochondrial matrix and the endoplasmic reticulum (ER) lumen. Our analysis reveals a highly heterogeneous distribution in protein turnover dynamics within macromolecular machineries such as the mitochondrial ribosome and respiratory complexes I-V, thus shedding light on their mechanism of hierarchical assembly. Furthermore, we investigate the dynamic changes of ER proteome when cells are challenged with stress or undergoing stimulated differentiation, identifying subsets of proteins with unique patterns of turnover dynamics, which may play key regulatory roles in alleviating stress or promoting differentiation. We envision that prox-SILAC could be broadly applied to profile protein turnover at various subcellular compartments, under both physiological and pathological conditions.

Regional mapping of energetic short mesoscale ocean dynamics from altimetry: performances from real observations

Abstract. For over 25 years, satellite altimetry has provided invaluable information about the ocean dynamics at many scales. In particular, gridded sea surface height (SSH) maps allow us to estimate the mesoscale geostrophic circulation in the ocean. However, conventional interpolation techniques rely on static optimal interpolation schemes, hence limiting the estimation of non-linear dynamics at scales not well sampled by altimetry (i.e., below 150–200 km at mid-latitudes). To overcome this limitation in the resolution of small-scale SSH structures (and thus small-scale geostrophic currents), a back-and-forth nudging algorithm combined with a quasi-geostrophic model, a technique called BFN-QG, has been successfully applied on simulated SSH data in observing system simulation experiments (OSSEs). The result is a significant reduction in interpolation error and an improvement in the space–time resolutions of the experimental gridded product compared to those of operational products. In this study, we propose that the BFN-QG be applied to real altimetric SSH data in a highly turbulent region spanning a part of the Agulhas Current. The performances are evaluated within observing system experiments (OSEs) that use independent data (such as independent SSH, sea surface temperature and drifter data) as ground truth. By comparing the mapping performances to the ones obtained with operational products, we show that the BFN-QG improves the mapping of short, energetic mesoscale structures and associated geostrophic currents both in space and time. In particular, the BFN-QG improves (i) the spatial effective resolution of the SSH maps by a factor of 20 %, (ii) the zonal and (especially) the meridional geostrophic currents, and (iii) the prediction of Lagrangian transport for lead times up to 10 d. Unlike the results obtained in the OSSEs, the OSEs reveal more contrasting performances in low-variability regions, which are discussed in the paper.

Generating 60–100 m, hourly, all-weather land surface temperatures based on the Landsat, ECOSTRESS, and reanalysis temperature combination (LERC)

Satellite-derived land surface temperatures (LSTs) often encounter a tradeoff between spatial and temporal resolutions, as well as severe cloud contamination. While extensive efforts have focused on resolution enhancement and under-cloud reconstruction, generating fine-resolution (≤100 m) diurnal LSTs under all-weather conditions remains a challenge, which hampers fine-scale monitoring of climatological, hydrological, and ecological processes. The latest 70-m ECOSTRESS observations at varying times of day provide an unprecedented opportunity for detailed mapping of diurnal LST dynamics, and reanalysis products with spatiotemporal continuity offer promising references for all-weather thermal dynamics. However, these advantages have rarely been integrated to concurrently achieve high spatiotemporal resolution and completeness. Here, we present a simple yet effective framework for reconstructing 60–100 m, hourly, all-weather LSTs based on the Landsat, ECOSTRESS and Reanalysis temperature Combination (termed LERC). The framework involves three steps: (i) preliminary under-cloud estimations within annual cycles of several times by fitting an enhanced annual temperature cycle (EATC) model to clear Landsat/ECOSTRESS scenes and China Land Data Assimilation System (CLDAS) LST fluctuations; (ii) optimized daily estimations at each selected time by correcting biases of the preliminary under-cloud estimations and re-modeling the EATC with temporally densified samples; and (iii) hourly seamless estimations by interpolating the two nearest daily estimations with reference to the weighted diurnal changes in CLDAS. LERC was evaluated in an urban-dominated region throughout 2020, resulting in an average root-mean-squared-error of 2.0 K (3.0 K) against 50 Landsat and ECOSTRESS images (hourly ground measurements at 13 sites). Compared to the enhanced spatial and temporal adaptive reflectance fusion model, classic ATC model, diurnal temperature cycle model, and three sophisticated all-weather products, LERC demonstrates outperformance in terms of general accuracy, spatiotemporal variability, and robustness against sparse input. LERC has great potentials for generating long-term reliable all-weather LST records with a high spatiotemporal resolution to promote broad applications.

All-optical spatiotemporal mapping of ROS dynamics across mitochondrial microdomains in situ

Hydrogen peroxide (H2O2) functions as a second messenger to signal metabolic distress through highly compartmentalized production in mitochondria. The dynamics of reactive oxygen species (ROS) generation and diffusion between mitochondrial compartments and into the cytosol govern oxidative stress responses and pathology, though these processes remain poorly understood. Here, we couple the H2O2 biosensor, HyPer7, with optogenetic stimulation of the ROS-generating protein KillerRed targeted into multiple mitochondrial microdomains. Single mitochondrial photogeneration of H2O2 demonstrates the spatiotemporal dynamics of ROS diffusion and transient hyperfusion of mitochondria due to ROS. This transient hyperfusion phenotype required mitochondrial fusion but not fission machinery. Measurement of microdomain-specific H2O2 diffusion kinetics reveals directionally selective diffusion through mitochondrial microdomains. All-optical generation and detection of physiologically-relevant concentrations of H2O2 between mitochondrial compartments provide a map of mitochondrial H2O2 diffusion dynamics in situ as a framework to understand the role of ROS in health and disease.

Ultrafast, high resolution spatiotemporal mapping of energy transport dynamics for determination of energy transport properties in silicon

Ultrafast, high resolution spatiotemporal mapping of energy transport dynamics for determination of energy transport properties in silicon

Normative brain mapping using scalp EEG and potential clinical application

A normative electrographic activity map could be a powerful resource to understand normal brain function and identify abnormal activity. Here, we present a normative brain map using scalp EEG in terms of relative band power. In this exploratory study we investigate its temporal stability, its similarity to other imaging modalities, and explore a potential clinical application. We constructed scalp EEG normative maps of brain dynamics from 17 healthy controls using source-localised resting-state scalp recordings. We then correlated these maps with those acquired from MEG and intracranial EEG to investigate their similarity. Lastly, we use the normative maps to lateralise abnormal regions in epilepsy. Spatial patterns of band powers were broadly consistent with previous literature and stable across recordings. Scalp EEG normative maps were most similar to other modalities in the alpha band, and relatively similar across most bands. Towards a clinical application in epilepsy, we found abnormal temporal regions ipsilateral to the epileptogenic hemisphere. Scalp EEG relative band power normative maps are spatially stable across time, in keeping with MEG and intracranial EEG results. Normative mapping is feasible and may be potentially clinically useful in epilepsy. Future studies with larger sample sizes and high-density EEG are now required for validation.

Modelling agricultural innovations as a social-ecological phenomenon

Agricultural innovations involve both social and social-ecological dynamics where outcomes emerge from interactions of innovation actors embedded within their ecological environments. Neglecting the interconnected nature of social-ecological innovations can lead to a flawed understanding and assessment of innovations. In this paper, we present an empirically informed, stylized agent-based model of agricultural innovation systems in Mali, West Africa. The study aimed to understand the emergence of food security and income inequality outcomes through two distinct model structures: top-down, aid-driven (exogenous) innovation and bottom-up, community-driven (endogenous) innovation. Our research questions were: i) How does the inclusion of social-ecological interactions in the model affect food security and income inequality outcomes? ii) How do exogenous and endogenous mechanisms influence food insecurity and income inequality? iii) What are the conditions under which exogenous and endogenous mechanisms would improve food security? The structural design of the model was based on a combination of theory, empirics, and mapping of social-ecological dynamics within innovation systems. Using the Social-Ecological Action Situation framework, we mapped the social, social-ecological, and ecological interactions that jointly produce food security outcomes. The exploratory model analysis reveals three key insights: i) Incorporation of social-ecological interactions influences model outcomes. Scenarios with social-ecological interactions showed a stronger relationship between income inequality and food security, lower levels of food security, and higher levels of income inequality than scenarios with social interactions. ii) Endogenous mechanism leads to higher food security and income inequality than the exogenous mechanism. iii) Bidirectional outreach is more effective than unidirectional outreach in improving food security. Inclusion of social-ecological dynamics and interactions such as the role of climate risk perception, social learning and formation of innovation beliefs and desires is key for modelling and analysis of agricultural innovations.

Survey of maps of dynamics for mobile robots

Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area.

Spectral Relaxation Imaging Microscopy II: Complex Dynamics.

The dynamics of condensed matter can be measured by the time-dependent Stokes shift of a suitable fluorescent probe. The time-dependent spectral correlation function is typically described by one or more spectral relaxation correlation times, which, in liquid solvents, characterize the timescales of the dipolar relaxation processes around the excited-state probe. The phasor plot provides a powerful approach to represent and analyze time and frequency-domain data acquired as images, thus providing a spatial map of spectral dynamics in a complex structure such as a living cell. Measurements of the phase and modulation at two emission wavelength channels were shown to be sufficient to extract a single excited-state lifetime and a single spectral relaxation correlation time, supplying estimates of the mean rate of excited-state depopulation and the mean rate of spectral shift. In the present contribution, two more issues were addressed. First, the provision of analytic formulae allowing extraction of the initial generalized polarization and the relaxed generalized polarization, which characterize the fluorescence spectrum of the unrelaxed state and the fully relaxed state. Second, improved methods of model discrimination and model parameter extraction for more complex spectral relaxation phenomena. The analysis workflow was illustrated with examples from the literature.

Real-time microscopy of the relaxation of a glass

The understanding of the dynamics of a glass above its devitrification temperature remains incomplete. Here, we build a spatio-temporal map of the relaxation dynamics of a highly stable glass into its supercooled liquid using real-time atomic force microscopy imaging. This methodology enables direct visualization of the progression of the liquid phase and clarifies and quantifies the presence of localized fast mobility regions separated by giant length scales. Our data establish a clear correlation between dynamic length and time scales in glasses. This approach may also be applicable to unveil the microscopic structure and dynamics of other glass-forming systems with much shorter length and time scales, including liquid-cooled glasses.

Temperature-Dependent Intensity Modulated Two-Photon Excited Fluorescence Microscopy for High Resolution Mapping of Charge Carrier Dynamics.

We present a temperature-dependent intensity modulated two-photon excited fluorescence microscopy technique that enables high-resolution quantitative mapping of charge carrier dynamics in perovskite microcrystal film. By disentangling the emission into harmonics of the excitation modulation frequency, we analyze the first and second order charge carrier recombination processes, including potential accumulation effects. Our approach allows for a quantitative comparison of different emission channels at a micrometer resolution. To demonstrate the effectiveness of the method, we applied it to a methylammonium lead bromide perovskite microcrystal film. We investigated the temperature-dependent modulated imaging, encompassing the exciton dissociation-association and charge carrier trapping-detrapping equilibrium. Additionally, we explored the potential freezing out of traps and the phase transition occurring at low temperatures.

Soft, bioresorbable, transparent microelectrode arrays for multimodal spatiotemporal mapping and modulation of cardiac physiology

Transparent microelectrode arrays (MEAs) that allow multimodal investigation of the spatiotemporal cardiac characteristics are important in studying and treating heart disease. Existing implantable devices, however, are designed to support chronic operational lifetimes and require surgical extraction when they malfunction or are no longer needed. Meanwhile, bioresorbable systems that can self-eliminate after performing temporary functions are increasingly attractive because they avoid the costs/risks of surgical extraction. We report the design, fabrication, characterization, and validation of a soft, fully bioresorbable, and transparent MEA platform for bidirectional cardiac interfacing over a clinically relevant period. The MEA provides multiparametric electrical/optical mapping of cardiac dynamics and on-demand site-specific pacing to investigate and treat cardiac dysfunctions in rat and human heart models. The bioresorption dynamics and biocompatibility are investigated. The device designs serve as the basis for bioresorbable cardiac technologies for potential postsurgical monitoring and treating temporary patient pathological conditions in certain clinical scenarios, such as myocardial infarction, ischemia, and transcatheter aortic valve replacement.

Human-Flow-Aware Long-Term Mobile Robot Task Planning Based on Hierarchical Reinforcement Learning

The difficulty in finding long-term planning policies for a mobile robot increases when operating in crowded and dynamic environments. State-of-the-art approaches do not consider cues of human-robot-shared dynamic environments. Aiming to fill this gap, we present a novel Human-Flow-Aware Guided Hierarchical Dyna-Q (HA-GHDQ) algorithm, which solves long-term robot task planning problems by using human motion patterns encoded in Maps of Dynamics (MoDs). To tackle the complexity of long-term robot operation in dynamic environments, we propose a combination of symbolic planning and Hierarchical Reinforcement Learning (HRL) that generates robot policies considering cost information derived from MoDs. We evaluated HA-GHDQ in a factory environment with two simulation and one real-world datasets to complete a transportation-and-assembly task. Our approach outperforms the baselines with respect to sample efficiency and final plan quality. Moreover, we show that it is more adaptable and robust against environmental changes than the baselines.

Towards complex applications of active remote sensing for ecology and conservation

Abstract Remote sensing (RS) and geospatial sciences already amount to a long history of fostering research in topics related to ecology. Data and methods have mainly been subject to research and experiments, but trends are now emerging that suggest the use of RS in practical applications like nationwide monitoring programs and assisting global conservation goals. However, use of active remote sensing for ecological and conservation is in its infancy, and the implications of active sensor data, including light detection and ranging and radio detection and ranging that mostly deliver three‐dimensional (3D) information, are still relatively primitive and have largely been limited to indirect use of their extracted proxies for ecological modelling. This cross‐journal special feature between Methods in Ecology and Evolution, Journal of Animal Ecology, Journal of Applied Ecology and Journal of Ecology includes 18 papers that include full research papers, reviews and technical applications. They are mostly novel in either or both their interpretation of proxies derived from active RS data and the direct usage of 3D RS techniques (terrestrial, airborne, UAV borne and spaceborne) to address ecological topics. We categorized the published contributions into the following thematic groups, with some degree of overlap: (i) ecosystem structural analysis by active data (nine studies); (ii) response of animal populations to climate dynamics as shown by active data; (iii) interactive effects of forest structure and wildlife monitoring (five studies); (iv) forest inventories assisted by active data (one study) and (v) tree type classification by active data (one study). Synthesis. The studies in this Special Feature and trends shown by other recent works at the interface of ecology and active RS confirm the ongoing shift from indirect and solely proxy‐based approaches to direct and more data‐science driven methods in approaching ecology and conservation problems by means of active sensors. Relatively affordable and accessible drone and citizen science‐based on‐demand active RS data acquisition are becoming common practice, and the future of sensor development is hypothesized to go beyond the current domination of very high spatial resolution data and towards multiple spaceborne platforms. These tools and methods will support spatial upscaling, uncertainty analysis, large‐scale mapping and monitoring of wildlife dynamics, among other topics that can take advantage of multitemporal/time series data. Nevertheless, access to demanding and costly very high‐resolution data sources may still be maintained and optimized by establishing international and public–private partnered data pools.

Using GEDI as training data for an ongoing mapping of landscape-scale dynamics of the plant area index

Leaf or plant area index (LAI, PAI) information is frequently used to describe vegetation structure in environmental science. While field measurements are time-consuming and do not scale to landscapes, model-based air- or space-borne remote-sensing methods have been used for many years for area-wide monitoring. As of 2019, NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission delivers a point-based LAI product with 25 m footprints and periodical repetition. This opens up new possibilities in integrating GEDI as frequently generated training samples with high resolution (spectral) sensors. However, the foreseeable duration of the system installed on the ISS is limited. In this study we want to test the potential of GEDI for regional comprehensive LAI estimations throughout the year with a focus on its usability beyond the lifespan of the GEDI mission. We study the landscape of Hesse, Germany, with its pronounced seasonal changes. Assuming a relationship between GEDI’s PAI and Sentinel-1 and -2 data, we used a Random Forest approach together with spatial variable selection to make predictions for new Sentinel scenes. The model was trained with two years of GEDI PAI data and validated against a third year to provide a robust and temporally independent model validation. This ensures the applicability of the validation for years outside the training period, reaching a total RMSE of 1.12. Predictions for the test year showed the expected seasonal and spatial patterns indicated by RMSE values ranging between 0.75 and 1.44, depending on the land cover class. The overall prediction performance shows good agreement with the test data set of the independent year which supports our assumption that the usage of GEDI’s PAI beyond the mission lifespan is feasible for regional studies.

Robustness of dynamics in games: A contraction mapping decomposition approach

A systematic framework for analyzing dynamical attributes of games has not been well-studied except for the special class of potential or near-potential games. In particular, the existing results have shortcomings in determining the asymptotic behavior of a given dynamics in a designated game. Although there is a large body of literature on developing convergent dynamics to the Nash equilibrium (NE) of a game, in general, the asymptotic behavior of an underlying dynamics may not be even close to a NE. In this paper, we initiate a new direction toward game dynamics by studying the contraction properties of the map of dynamics in games. To this aim, we first decompose the map of a given dynamics into contractive and non-contractive parts and then explore the asymptotic behavior of those dynamics using the proximity of such decomposition to contraction mappings. In particular, we analyze the non-contractive behavior for better/best response dynamics in discrete-action space sequential/repeated games and show that the non-contractive part of those dynamics is well-behaved in a certain sense. That allows us to estimate the asymptotic behavior of such dynamics using a neighborhood around the fixed point of their contractive part proxy. Finally, we demonstrate the practicality of our framework via an example from the duopoly Cournot games.

Morphology and Dynamics in Hydrated Graphene Oxide/Branched Poly(ethyleneimine) Nanocomposites: An In Silico Investigation.

Graphene oxide (GO)-branched poly(ethyleneimine) (BPEI) hydrated mixtures were studied by means of fully atomistic molecular dynamics simulations to assess the effects of the size of polymers and the composition on the morphology of the complexes, the energetics of the systems and the dynamics of water and ions within composites. The presence of cationic polymers of both generations hindered the formation of stacked GO conformations, leading to a disordered porous structure. The smaller polymer was found to be more efficient at separating the GO flakes due to its more efficient packing. The variation in the relative content of the polymeric and the GO moieties provided indications for the existence of an optimal composition in which interaction between the two components was more favorable, implying more stable structures. The large number of hydrogen-bonding donors afforded by the branched molecules resulted in a preferential association with water and hindered its access to the surface of the GO flakes, particularly in polymer-rich systems. The mapping of water translational dynamics revealed the existence of populations with distinctly different mobilities, depending upon the state of their association. The average rate of water transport was found to depend sensitively on the mobility of the freely to move molecules, which was varied strongly with composition. The rate of ionic transport was found to be very limited below a threshold in terms of polymer content. Both, water diffusivity and ionic transport were enhanced in the systems with the larger branched polymers, particularly with a lower polymer content, due to the higher availability of free volume for the respective moieties. The detail afforded in the present work provides a new insight for the fabrication of BPEI/GO composites with a controlled microstructure, enhanced stability and adjustable water transport and ionic mobility.