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Hybrid ion capacitors (HICs) have aroused extreme interest due to their combined characteristics of energy and power densities. The performance of HICs lies hidden in the electrode materials used for the construction of battery and supercapacitor components. The hunt is always on to locate the best material in terms of cost-effectiveness and overall optimized performance characteristics. Functionalized biomass-derived porous carbons (FBPCs) possess exquisite features including easy synthesis, wide availability, high surface area, large pore volume, tunable pore size, surface functional groups, a wide range of morphologies, and high thermal and chemical stability. FBPCs have found immense use as cathode, anode and dual electrode materials for HICs in the recent literature. The current review is designed around two main concepts which include the synthesis and properties of FBPCs followed by their utilization in various types of HICs. Among monovalent HICs, lithium, sodium, and potassium, are given comprehensive attention, whereas zinc is the only multivalent HIC that is focused upon due to corresponding literature availability. Special attention is also provided to the critical factors that govern the performance of HICs. The review concludes by providing feasible directions for future research in various aspects of FBPCs and their utilization in HICs., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

In this work, the synthesis of core-shell ordered mesoporous silica nanoparticles (CSMS) with tunable particle size and shape through a dual surfactant-assisted approach is demonstrated. By varying the synthesis conditions, including the type of the solvent and the concentration of the surfactant, monodispersed and ordered mesoporous silica nanoparticles with tunable particle size (140-600 nm) and morphologies (hexagonal prism (HP), oblong, spherical, and hollow-core) can be realized. Comparative studies of the Cabazitaxel (CBZ)-loaded HP and spherical-shaped CSMS are conducted to evaluate their drug delivery efficiency to PC3 (prostate cancer) cell lines. These nanoparticles showed good biocompatibility and displayed a faster drug release at acidic pH than at basic pH. The cellular uptake of CSMS measured using confocal microscopy, flow cytometry, microplate reader, and ICP-MS (inductively coupled plasma mass spectrometry) techniques in PC3 cell lines revealed a better uptake of CSMS with HP morphology than its spherical counterparts. Cytotoxicity study showed that the anticancer activity of CBZ is improved with a higher free radical production when loaded onto CSMS. These unique materials with tunable morphology can serve as an excellent drug delivery system and will have potential applications for treating various cancers., (© 2023 The Authors. Small published by Wiley‐VCH GmbH.)

Heart failure with preserved ejection fraction (HFpEF) is an important, emerging risk factor for dementia, but it is not clear whether HFpEF contributes to a specific pattern of neuroanatomical changes in dementia. A major challenge to studying this is the relative paucity of datasets of patients with dementia, with/without HFpEF, and relevant neuroimaging. We sought to demonstrate the feasibility of using modern data mining tools to create and analyze clinical imaging datasets and identify the neuroanatomical signature of HFpEF-associated dementia. We leveraged the bioinformatics tools at Vanderbilt University Medical Center to identify patients with a diagnosis of dementia with and without comorbid HFpEF using the electronic health record. We identified high resolution, clinically-acquired neuroimaging data on 30 dementia patients with HFpEF (age 76.9 ± 8.12 years, 61% female) as well as 301 age- and sex-matched patients with dementia but without HFpEF to serve as comparators (age 76.2 ± 8.52 years, 60% female). We used automated image processing pipelines to parcellate the brain into 132 structures and quantify their volume. We found six regions with significant atrophy associated with HFpEF: accumbens area, amygdala, posterior insula, anterior orbital gyrus, angular gyrus, and cerebellar white matter. There were no regions with atrophy inversely associated with HFpEF. Patients with dementia and HFpEF have a distinct neuroimaging signature compared to patients with dementia only. Five of the six regions identified in are in the temporo-parietal region of the brain. Future studies should investigate mechanisms of injury associated with cerebrovascular disease leading to subsequent brain atrophy., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Affordable and environmentally friendly electrochemically active raw energy storage materials are in high demand to switch to mass-scale renewable energy. One particularly promising avenue is the feasibility of utilizing food waste-derived nanoporous carbon. This material holds significance due to its widespread availability, affordability, ease of processing, and, notably, its cost-free nature. Over the years, various strategies have been developed to convert different food wastes into nanoporous carbon materials with enhanced electrochemical properties. The electrochemical performance of these materials is influenced by both intrinsic factors, such as the composition of elements derived from the original food sources and recipes, and extrinsic factors, including the conditions during pyrolysis and activation. While current efforts are dedicated to optimizing process parameters to achieve superior performance in electrochemical energy storage devices, it is timely to take stock of the current state of research in this emerging field. This review provides a comprehensive overview of recent developments in the fabrication and surface characterisation of porous carbons from different food wastes. A special focus is given on the applications of these food waste derived porous carbons for energy storage applications including batteries and supercapacitors., Competing Interests: No potential conflict of interest was reported by the author(s)., (© 2024 The Author(s). Published by National Institute for Materials Science in partnership with Taylor & Francis Group.)

Diffusion MRI of the spinal cord (SC) is susceptible to geometric distortion caused by field inhomogeneities, and prone to misalignment across time series and signal dropout caused by biological motion. Several modifications of image acquisition and image processing techniques have been introduced to overcome these artifacts, but their specific benefits are largely unproven and warrant further investigations. We aim to evaluate two specific aspects of image acquisition and processing that address image quality in diffusion studies of the spinal cord: susceptibility corrections to reduce geometric distortions, and cardiac triggering to minimize motion artifacts. First, we evaluate 4 distortion preprocessing strategies on 7 datasets of the cervical and lumbar SC and find that while distortion correction techniques increase geometric similarity to structural images, they are largely driven by the high-contrast cerebrospinal fluid, and do not consistently improve the geometry within the cord nor improve white-to-gray matter contrast. We recommend at a minimum to perform bulk-motion correction in preprocessing and posit that improvements/adaptations are needed for spinal cord distortion preprocessing algorithms, which are currently optimized and designed for brain imaging. Second, we design experiments to evaluate the impact of removing cardiac triggering. We show that when triggering is foregone, images are qualitatively similar to triggered sequences, do not have increased prevalence of artifacts, and result in similar diffusion tensor indices with similar reproducibility to triggered acquisitions. When triggering is removed, much shorter acquisitions are possible, which are also qualitatively and quantitatively similar to triggered sequences. We suggest that removing cardiac triggering for cervical SC diffusion can be a reasonable option to save time with minimal sacrifice to image quality., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides unique information about white matter microstructure in the brain but is susceptible to confounding effects introduced by scanner or acquisition differences. ComBat is a leading approach for addressing these site biases. However, despite its frequent use for harmonization, ComBat's robustness toward site dissimilarities and overall cohort size have not yet been evaluated in terms of DTI., Approach: As a baseline, we match N = 358 participants from two sites to create a "silver standard" that simulates a cohort for multi-site harmonization. Across sites, we harmonize mean fractional anisotropy and mean diffusivity, calculated using participant DTI data, for the regions of interest defined by the JHU EVE-Type III atlas. We bootstrap 10 iterations at 19 levels of total sample size, 10 levels of sample size imbalance between sites, and 6 levels of mean age difference between sites to quantify (i)  β AGE , the linear regression coefficient of the relationship between FA and age; (ii)  γ ^ s f * , the ComBat-estimated site-shift; and (iii)  δ ^ s f * , the ComBat-estimated site-scaling. We characterize the reliability of ComBat by evaluating the root mean squared error in these three metrics and examine if there is a correlation between the reliability of ComBat and a violation of assumptions., Results: ComBat remains well behaved for β AGE when N > 162 and when the mean age difference is less than 4 years. The assumptions of the ComBat model regarding the normality of residual distributions are not violated as the model becomes unstable., Conclusion: Prior to harmonization of DTI data with ComBat, the input cohort should be examined for size and covariate distributions of each site. Direct assessment of residual distributions is less informative on stability than bootstrap analysis. We caution use ComBat of in situations that do not conform to the above thresholds., (© 2024 The Authors.)

Mapping information from photographic images to volumetric medical imaging scans is essential for linking spaces with physical environments, such as in image-guided surgery. Current methods of accurate photographic image to computed tomography (CT) image mapping can be computationally intensive and/or require specialized hardware. For general purpose 3-D mapping of bulk specimens in histological processing, a cost-effective solution is necessary. Here, we compare the integration of a commercial 3-D camera and cell phone imaging with a surface registration pipeline. Using surgical implants and chuck-eye steak as phantom tests, we obtain 3-D CT reconstruction and sets of photographic images from two sources: Canfield Imaging's H1 camera and an iPhone 14 Pro. We perform surface reconstruction from the photographic images using commercial tools and open-source code for Neural Radiance Fields (NeRF) respectively. We complete surface registration of the reconstructed surfaces with the iterative closest point (ICP) method. Manually placed landmarks were identified at three locations on each of the surfaces. Registration of the Canfield surfaces for three objects yields landmark distance errors of 1.747, 3.932, and 1.692 mm, while registration of the respective iPhone camera surfaces yields errors of 1.222, 2.061, and 5.155 mm. Photographic imaging of an organ sample prior to tissue sectioning provides a low-cost alternative to establish correspondence between histological samples and 3-D anatomical samples.

Fast production of hydrogen and oxygen in large amounts at an economic rate is the need of the hour to cater to the needs of the most awaited hydrogen energy, a futuristic renewable energy solution. Production of hydrogen through simple water splitting via visible light photocatalytic approach using sunlight is considered as one of the most promising and sustainable approaches for generating clean fuels. For this purpose, a variety of catalytic techniques and novel catalysts have been investigated. Among these catalysts, carbon nitride is presently deemed as one of the best candidates for the visible light photocatalysis due to its unique molecular structure and adequate visible-range bandgap. Its bandgap can be further engineered by structural and morphological manipulation or by doping/hybridization. Among numerous synthetic approaches for carbon nitrides, supramolecular self-assembly is one of the recently developed elegant bottom-up strategies as it is bio-inspired and provides a facile and eco-friendly route to synthesize high surface area carbon nitride with superior morphological features and other semiconducting and catalytic properties. The current review article broadly covers supramolecular self-assembly synthesis of carbon nitride nanostructures and their photocatalytic water-splitting applications and provides a comprehensive outlook on future directions., (© 2023 The Authors. Advanced Materials published by Wiley-VCH GmbH.)

Purpose: Diffusion-weighted magnetic resonance imaging (DW-MRI) is a critical imaging method for capturing and modeling tissue microarchitecture at a millimeter scale. A common practice to model the measured DW-MRI signal is via fiber orientation distribution function (fODF). This function is the essential first step for the downstream tractography and connectivity analyses. With recent advantages in data sharing, large-scale multisite DW-MRI datasets are being made available for multisite studies. However, measurement variabilities (e.g., inter- and intrasite variability, hardware performance, and sequence design) are inevitable during the acquisition of DW-MRI. Most existing model-based methods [e.g., constrained spherical deconvolution (CSD)] and learning-based methods (e.g., deep learning) do not explicitly consider such variabilities in fODF modeling, which consequently leads to inferior performance on multisite and/or longitudinal diffusion studies., Approach: In this paper, we propose a data-driven deep CSD method to explicitly constrain the scan-rescan variabilities for a more reproducible and robust estimation of brain microstructure from repeated DW-MRI scans. Specifically, the proposed method introduces a three-dimensional volumetric scanner-invariant regularization scheme during the fODF estimation. We study the Human Connectome Project (HCP) young adults test-retest group as well as the MASiVar dataset (with inter- and intrasite scan/rescan data). The Baltimore Longitudinal Study of Aging dataset is employed for external validation., Results: From the experimental results, the proposed data-driven framework outperforms the existing benchmarks in repeated fODF estimation. By introducing the contrastive loss with scan/rescan data, the proposed method achieved a higher consistency while maintaining higher angular correlation coefficients with the CSD modeling. The proposed method is assessing the downstream connectivity analysis and shows increased performance in distinguishing subjects with different biomarkers., Conclusion: We propose a deep CSD method to explicitly reduce the scan-rescan variabilities, so as to model a more reproducible and robust brain microstructure from repeated DW-MRI scans. The plug-and-play design of the proposed approach is potentially applicable to a wider range of data harmonization problems in neuroimaging., (© 2024 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Designing unique nanomaterials for the selective sensing of biomolecules is of significant interest in the field of nanobiotechnology. In this work, we demonstrated the synthesis of ordered Cu nanoparticle-functionalised mesoporous C 3 N 5 that has unique peroxidase-like nanozymatic activity for the ultrasensitive and selective detection of glucose and glutathione. A nano hard-templating technique together with the in-situ polymerisation and self-assembly of Cu and high N-containing CN precursor was adopted to introduce mesoporosity as well as high N and Cu content in mesoporous C 3 N 5 . Due to the ordered structure and highly dispersed Cu in the mesoporous C 3 N 5 , a large enhancement of the peroxidase mimetic activity in the oxidation of a redox dye in the presence of hydrogen peroxide could be obtained. Additionally, the optimised Cu-functionalised mesoporous C 3 N 5 exhibited excellent sensitivity to glutathione with a low detection limit of 2.0 ppm. The strong peroxidase activity of the Cu-functionalised mesoporous C 3 N 5 was also effectively used for the sensing of glucose with a detection limit of 0.4 mM through glucose oxidation with glucose oxidase. This unique Cu-functionalised mesoporous C 3 N 5 has the potential for detecting various molecules in the environment as well as for next-generation glucose and glutathione diagnostic devices., (© 2023 The Authors. Chemistry - A European Journal published by Wiley-VCH GmbH.)

Functional magnetic resonance images (fMRI) acquired using echo planar sequences typically suffer from spatial distortions due to susceptibility induced off-resonance fields, which may cause geometric mismatch with structural images and affect subsequent quantification and localization of brain function. State-of-the art distortion correction methods (for example, using FSL's topup or AFNI's 3dQwarp algorithms) require the collection of additional scans - either field maps or images with reverse phase encoding directions (i.e., blip-up/blip-down acquisitions) - to estimate and correct distortions. However, not all imaging protocols acquire these additional data and thus cannot take advantage of these post-acquisition corrections. In this study, we aim to enable state-of-the art processing of historical or limited datasets that do not include specific sequences for distortion correction by using only the acquired functional data and a single commonly acquired structural image. To achieve this, we synthesize an undistorted image with contrast similar to the fMRI data and use the non-distorted synthetic image as an anatomical target for distortion correction. We evaluate the efficacy of this approach, named SynBOLD-DisCo (Synthetic BOLD contrast for Distortion Correction), and show that this distortion correction process yields fMRI data that are geometrically similar to non-distorted structural images, with distortion correction virtually equivalent to acquisitions that do contain both blip-up/blip-down images. Our method is available as a Singularity container, source code, and an executable trained model to facilitate evaluation and integration into existing fMRI preprocessing pipelines., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Catalysis is at the heart of modern-day chemical and pharmaceutical industries, and there is an urgent demand to develop metal-free, high surface area, and efficient catalysts in a scalable, reproducible and economic manner. Amongst the ever-expanding two-dimensional materials family, carbon nitride (CN) has emerged as the most researched material for catalytic applications due to its unique molecular structure with tunable visible range band gap, surface defects, basic sites, and nitrogen functionalities. These properties also endow it with anchoring capability with a large number of catalytically active sites and provide opportunities for doping, hybridization, sensitization, etc. To make considerable progress in the use of CN as a highly effective catalyst for various applications, it is critical to have an in-depth understanding of its synthesis, structure and surface sites. The present review provides an overview of the recent advances in synthetic approaches of CN, its physicochemical properties, and band gap engineering, with a focus on its exclusive usage in a variety of catalytic reactions, including hydrogen evolution reactions, overall water splitting, water oxidation, CO 2 reduction, nitrogen reduction reactions, pollutant degradation, and organocatalysis. While the structural design and band gap engineering of catalysts are elaborated, the surface chemistry is dealt with in detail to demonstrate efficient catalytic performances. Burning challenges in catalytic design and future outlook are elucidated.

Carbon-based nanomaterials, including graphene, fullerenes, and carbon nanotubes, are attracting significant attention as promising materials for next-generation energy storage and conversion applications. They possess unique physicochemical properties, such as structural stability and flexibility, high porosity, and tunable physicochemical features, which render them well suited in these hot research fields. Technological advances at atomic and electronic levels are crucial for developing more efficient and durable devices. This comprehensive review provides a state-of-the-art overview of these advanced carbon-based nanomaterials for various energy storage and conversion applications, focusing on supercapacitors, lithium as well as sodium-ion batteries, and hydrogen evolution reactions. Particular emphasis is placed on the strategies employed to enhance performance through nonmetallic elemental doping of N, B, S, and P in either individual doping or codoping, as well as structural modifications such as the creation of defect sites, edge functionalization, and inter-layer distance manipulation, aiming to provide the general guidelines for designing these devices by the above approaches to achieve optimal performance. Furthermore, this review delves into the challenges and future prospects for the advancement of carbon-based electrodes in energy storage and conversion., (© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Objective: To enable interactive visualization of phenome-wide association studies (PheWAS) on electronic health records (EHR)., Materials and Methods: Current PheWAS technologies require familiarity with command-line interfaces and lack end-to-end data visualizations. pyPheWAS Explorer allows users to examine group variables, test assumptions, design PheWAS models, and evaluate results in a streamlined graphical interface., Results: A cohort of attention deficit hyperactivity disorder (ADHD) subjects and matched non-ADHD controls is examined. pyPheWAS Explorer is used to build a PheWAS model including sex and deprivation index as covariates, and the Explorer's result visualization for this model reveals known ADHD comorbidities., Discussion: pyPheWAS Explorer may be used to rapidly investigate potentially novel EHR associations. Broader applications include deployment for clinical experts and preliminary exploration tools for institutional EHR repositories., Conclusion: pyPheWAS Explorer provides a seamless graphical interface for designing, executing, and analyzing PheWAS experiments, emphasizing exploratory analysis of regression types and covariate selection., Competing Interests: None declared., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Carbon nitrides, a distinguished class of metal-free catalytic materials, have presented a good potential for chemical transformations and are expected to become prominent materials for organocatalysis. This is largely possible due to their low cost, exceptional thermal and chemical stability, non-toxicity, ease of functionalization, porosity development, etc. Especially, the carbon nitrides with increased porosity and nitrogen contents are more versatile than their bulk counterparts for catalysis. These N-rich carbon nitrides are discussed in the earlier parts of the review. Later, the review highlights the role of such carbon nitride materials for the various organic catalytic reactions including Knoevenagel condensation, oxidation, hydrogenation, esterification, transesterification, cycloaddition, and hydrolysis. The recently emerging concepts in carbon nitride-based organocatalysis have been given special attention. In each of the sections, the structure-property relationship of the materials was discussed and related to their catalysis action. Relevant comparisons with other catalytic materials are also discussed to realize their real potential value. The perspective, challenges, and future directions are also discussed. The overall objective of this review is to provide up-to-date information on new developments in carbon nitride-based organic catalysis reactions that could see them rising as prominent catalytic materials in the future., Competing Interests: No potential conflict of interest was reported by the author(s)., (© 2023 The Author(s). Published by National Institute for Materials Science in partnership with Taylor & Francis Group.)

Diffusion weighted magnetic resonance imaging (DW-MRI) captures tissue microarchitecture at millimeter scale. With recent advantages in data sharing, large-scale multi-site DW-MRI datasets are being made available for multi-site studies. However, DW-MRI suffers from measurement variability (e.g., inter- and intra-site variability, hardware performance, and sequence design), which consequently yields inferior performance on multi-site and/or longitudinal diffusion studies. In this study, we propose a novel, deep learning-based method to harmonize DW-MRI signals for a more reproducible and robust estimation of microstructure. Our method introduces a data-driven scanner-invariant regularization scheme to model a more robust fiber orientation distribution function (FODF) estimation. We study the Human Connectome Project (HCP) young adults test-retest group as well as the MASiVar dataset (with inter- and intra-site scan/rescan data). The 8 th order spherical harmonics coefficients are employed as data representation. The results show that the proposed harmonization approach maintains higher angular correlation coefficients (ACC) with the ground truth signals (0.954 versus 0.942), while achieves higher consistency of FODF signals for intra-scanner data (0.891 versus 0.826), as compared with the baseline supervised deep learning scheme. Furthermore, the proposed data-driven framework is flexible and potentially applicable to a wider range of data harmonization problems in neuroimaging.

7T magnetic resonance imaging (MRI) has the potential to drive our understanding of human brain function through new contrast and enhanced resolution. Whole brain segmentation is a key neuroimaging technique that allows for region-by-region analysis of the brain. Segmentation is also an important preliminary step that provides spatial and volumetric information for running other neuroimaging pipelines. Spatially localized atlas network tiles (SLANT) is a popular 3D convolutional neural network (CNN) tool that breaks the whole brain segmentation task into localized sub-tasks. Each sub-task involves a specific spatial location handled by an independent 3D convolutional network to provide high resolution whole brain segmentation results. SLANT has been widely used to generate whole brain segmentations from structural scans acquired on 3T MRI. However, the use of SLANT for whole brain segmentation from structural 7T MRI scans has not been successful due to the inhomogeneous image contrast usually seen across the brain in 7T MRI. For instance, we demonstrate the mean percent difference of SLANT label volumes between a 3T scan-rescan is approximately 1.73%, whereas its 3T-7T scan-rescan counterpart has higher differences around 15.13%. Our approach to address this problem is to register the whole brain segmentation performed on 3T MRI to 7T MRI and use this information to finetune SLANT for structural 7T MRI. With the finetuned SLANT pipeline, we observe a lower mean relative difference in the label volumes of ~8.43% acquired from structural 7T MRI data. Dice similarity coefficient between SLANT segmentation on the 3T MRI scan and the after finetuning SLANT segmentation on the 7T MRI increased from 0.79 to 0.83 with p<0.01. These results suggest finetuning of SLANT is a viable solution for improving whole brain segmentation on high resolution 7T structural imaging.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing., (© 2022. The Author(s).)

A robust medical image computing infrastructure must host massive multimodal archives, perform extensive analysis pipelines, and execute scalable job management. An emerging data format standard, the Brain Imaging Data Structure (BIDS), introduces complexities for interfacing with XNAT archives. Moreover, workflow integration is combinatorically problematic when matching large amount of processing to large datasets. Historically, workflow engines have been focused on refining workflows themselves instead of actual job generation. However, such an approach is incompatible with data centric architecture that hosts heterogeneous medical image computing. Distributed automation for XNAT toolkit (DAX) provides large-scale image storage and analysis pipelines with an optimized job management tool. Herein, we describe developments for DAX that allows for integration of XNAT and BIDS standards. We also improve DAX's efficiencies of diverse containerized workflows in a high-performance computing (HPC) environment. Briefly, we integrate YAML configuration processor scripts to abstract workflow data inputs, data outputs, commands, and job attributes. Finally, we propose an online database-driven mechanism for DAX to efficiently identify the most recent updated sessions, thereby improving job building efficiency on large projects. We refer the proposed overall DAX development in this work as DAX-1 (DAX version 1). To validate the effectiveness of the new features, we verified (1) the efficiency of converting XNAT data to BIDS format and the correctness of the conversion using a collection of BIDS standard containerized neuroimaging workflows, (2) how YAML-based processor simplified configuration setup via a sequence of application pipelines, and (3) the productivity of DAX-1 on generating actual HPC processing jobs compared with earlier DAX baseline method. The empirical results show that (1) DAX-1 converting XNAT data to BIDS has similar speed as accessing XNAT data only; (2) YAML can integrate to the DAX-1 with shallow learning curve for users, and (3) DAX-1 reduced the job/assessor generation latency by finding recent modified sessions. Herein, we present approaches for efficiently integrating XNAT and modern image formats with a scalable workflow engine for the large-scale dataset access and processing., (© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

FOXG1 Syndrome (FS) is a devastating neurodevelopmental disorder that is caused by a heterozygous loss-of-function (LOF) mutation of the FOXG1 gene, which encodes a transcriptional regulator important for telencephalic brain development. People with FS have marked developmental delays, impaired ambulation, movement disorders, seizures, and behavior abnormalities including autistic features. Current therapeutic approaches are entirely symptomatic, however the ability to rescue phenotypes in mouse models of other genetic neurodevelopmental disorders such as Rett syndrome, Angelman syndrome, and Phelan-McDermid syndrome by postnatal expression of gene products has led to hope that similar approaches could help modify the disease course in other neurodevelopmental disorders such as FS. While FoxG1 protein function plays a critical role in embryonic brain development, the ongoing adult expression of FoxG1 and behavioral phenotypes that present when FoxG1 function is removed postnatally provides support for opportunity for improvement with postnatal treatment. Here we generated a new mouse allele of Foxg1 that disrupts protein expression and characterized the behavioral and structural brain phenotypes in heterozygous mutant animals. These mutant animals display changes in locomotor behavior, gait, anxiety, social interaction, aggression, and learning and memory compared to littermate controls. Additionally, they have structural brain abnormalities reminiscent of people with FS. This information provides a framework for future studies to evaluate the potential for post-natal expression of FoxG1 to modify the disease course in this severe neurodevelopmental disorder., Competing Interests: The authors have declared that no competing interests exist.

The field of artificial intelligence (AI) in medical imaging is undergoing explosive growth, and Radiology is a prime target for innovation. The American College of Radiology Data Science Institute has identified more than 240 specific use cases where AI could be used to improve clinical practice. In this context, thousands of potential methods are developed by research labs and industry innovators. Deploying AI tools within a clinical enterprise, even on limited retrospective evaluation, is complicated by security and privacy concerns. Thus, innovation must be weighed against the substantive resources required for local clinical evaluation. To reduce barriers to AI validation while maintaining rigorous security and privacy standards, we developed the AI Imaging Incubator. The AI Imaging Incubator serves as a DICOM storage destination within a clinical enterprise where images can be directed for novel research evaluation under Institutional Review Board approval. AI Imaging Incubator is controlled by a secure HIPAA-compliant front end and provides access to a menu of AI procedures captured within network-isolated containers. Results are served via a secure website that supports research and clinical data formats. Deployment of new AI approaches within this system is streamlined through a standardized application programming interface. This manuscript presents case studies of the AI Imaging Incubator applied to randomizing lung biopsies on chest CT, liver fat assessment on abdomen CT, and brain volumetry on head MRI., (© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

One of the key focuses of the agricultural industry for preventing the decline in crop yields due to pests is to develop effective, safe, green, and sustainable pesticide formulation. A key objective of industry is to deliver active ingredients (AIs) that have minimal off site migration and non-target activity. Nanoporous materials have received significant attention internationally for the efficient loading and controlled, targeted delivery of pesticides. This is largely made possible due to their textural features including high surface area, large pore-volume, and tunable pore size. Additionally, the easier manipulation of their surface chemistry and stability in different environments are added advantages. The unique features of these materials allow them to address the solubility of the active ingredients, their efficient loading onto the porous channels, and slow and controlled delivery over time. One of their major advantages is the wide range of materials that could be suitably designed via different approaches to either adsorb, encapsulate, or entrap the active ingredient. This review is a timely presentation of recent progress made in nanoporous materials and discusses critical aspects of pesticide formulation and delivery., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Non-linear gradients impact diffusion weighted (DW) MRI by corrupting the experimental setup and lead to problems during image encoding including the effects in-plane distortion, in-plane shifts, intensity modulations and phase errors. Recent studies have been shown this may present significant complication in the interpretation of results and conclusion while studying tractography and tissue microstructure in data. To interpret the degree in consequences of gradient non-linearities between the desired and achieved gradients, we introduced empirically derived gradient nonlinear fields at different orientations and different tensor properties. The impact is assessed through diffusion tensor properties including mean diffusivity (MD), fractional anisotropy (FA) and principal eigen vector (PEV). The study shows lower FA are more susceptible to LR fields and LR fields with determinant <1 or >1 corrupt tensor more. The corruption can result in significantly different FA based on true-FA and LR field. Apparent MD decreases for negative determinant, on the other hand positive determinant shows the opposite effect. LR field have a larger impact on PEV when FA value is small. The results are dependent on the underlying orientation, non-linear field corruption can cause both increase and decrease of estimated FA, MD and PEV value. This work provides insight into characterizing the non-linear gradient error and aid in selecting correction techniques to address the inaccuracies in b-values.

7T MRI provides unprecedented resolution for examining human brain anatomy in vivo . For example, 7T MRI enables deep thickness measurement of laminar subdivisions in the right fusiform area. Existing laminar thickness measurement on 7T is labor intensive, and error prone since the visual inspection of the image is typically along one of the three orthogonal planes (axial, coronal, or sagittal view). To overcome this, we propose a new analytics tool that allows flexible quantification of cortical thickness on a 2D plane that is orthogonal to the cortical surface (beyond axial, coronal, and sagittal views) based on the 3D computational surface reconstruction. The proposed method further distinguishes high quality 2D planes and the low-quality ones by automatically inspecting the angles between the surface normals and slice direction. In our approach, we acquired a pair of 3T and 7T scans (same subject). We extracted the brain surfaces from the 3T scan using MaCRUISE and projected the surface to the 7T scan's space. After computing the angles between the surface normals and axial direction vector, we found that 18.58% of surface points were angled at more than 80° with the axial direction vector and had 2D axial planes with visually distinguishable cortical layers. 15.12% of the surface points with normal vectors angled at 30° or lesser with the axial direction, had poor 2D axial slices for visual inspection of the cortical layers. This effort promises to dramatically extend the area of cortex that can be quantified with ultra-high resolution in-plane imaging methods.

Type 1 diabetes (T1D) affects over 200,000 children and is associated with an increased risk of cognitive dysfunction. Prior imaging studies suggest the neurological changes underlying this risk are multifactorial, including macrostructural, microstructural, and inflammatory changes. However, these studies have yet to be integrated, limiting investigation into how these phenomena interact. To better understand these complex mechanisms of brain injury, a well-powered, prospective, multisite, and multimodal neuroimaging study is needed. We take the first step in accomplishing this with a preliminary characterization of multisite, multimodal MRI quality, motion, and variability in pediatric T1D. We acquire structural T1 weighted (T1w) MRI, diffusion tensor MRI (DTI), functional MRI (fMRI), and magnetic resonance spectroscopy (MRS) of 5-7 participants from each of two sites. First, we assess the contrast-to-noise ratio of the T1w MRI and find no differences between sites. Second, we characterize intervolume motion in DTI and fMRI and find it to be on the subvoxel level. Third, we investigate variability in regional gray matter volumes and local gyrification indices, bundle-wise DTI microstructural measures, and N-acetylaspartate to creatine ratios. We find the T1-based measures to be comparable between sites before harmonization and the DTI and MRS-based measures to be comparable after. We find a 5-15% coefficient of variation for most measures, suggesting ~150-200 participants per group on average are needed to detect a 5% difference across these modalities at 0.9 power. We conclude that multisite, multimodal neuroimaging of pediatric T1D is feasible with low motion artifact after harmonization of DTI and MRS.

Reproducible identification of white matter pathways across subjects is essential for the study of structural connectivity of the human brain. One of the key challenges is anatomical differences between subjects and human rater subjectivity in labeling. Labeling white matter regions of interest presents many challenges due to the need to integrate both local and global information. Clearly communicating the manual processes to capture this information is cumbersome, yet essential to lay a solid foundation for comprehensive atlases. Segmentation protocols must be designed so the interpretation of the requested tasks as well as locating structural landmarks is anatomically accurate, intuitive and reproducible. In this work, we quantified the reproducibility of a first iteration of an open/public multi-bundle segmentation protocol. This allowed us to establish a baseline for its reproducibility as well as to identify the limitations for future iterations. The protocol was tested/evaluated on both typical 3 T research acquisition Baltimore Longitudinal Study of Aging (BLSA) and high-acquisition quality Human Connectome Project (HCP) datasets. The results show that a rudimentary protocol can produce acceptable intra-rater and inter-rater reproducibility. However, this work highlights the difficulty in generalizing reproducible results and the importance of reaching consensus on anatomical description of white matter pathways. The protocol has been made available in open source to improve generalizability and reliability in collaboration. The goal is to improve upon the first iteration and initiate a discussion on the anatomical validity (or lack thereof) of some bundle definitions and the importance of reproducibility of tractography segmentation., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Purpose: Diffusion-weighted imaging allows investigators to identify structural, microstructural, and connectivity-based differences between subjects, but variability due to session and scanner biases is a challenge., Methods: To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm 2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de-identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi-compartment neurite orientation dispersion and density model, (3) white-matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region-wise fractional anisotropy, mean diffusivity, and principal eigenvector; region-wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle-wise shape, volume, fractional anisotropy, and length; and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length., Results: We plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability., Conclusions: This study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects., (© 2021 International Society for Magnetic Resonance in Medicine.)

Aqueous film-forming foam, used in firefighting, and biowastes, including biosolids, animal and poultry manures, and composts, provide a major source of poly- and perfluoroalkyl substances (PFAS) input to soil. Large amounts of biowastes are added to soil as a source of nutrients and carbon. They also are added as soil amendments to improve soil health and crop productivity. Plant uptake of PFAS through soil application of biowastes is a pathway for animal and human exposure to PFAS. The complexity of PFAS mixtures, and their chemical and thermal stability, make remediation of PFAS in both solid and aqueous matrices challenging. Remediation of PFAS in biowastes, as well as soils treated with these biowastes, can be achieved through preventing and decreasing the concentration of PFAS in biowaste sources (i.e., prevention through source control), mobilization of PFAS in contaminated soil and subsequent removal through leaching (i.e., soil washing) and plant uptake (i.e., phytoremediation), sorption of PFAS, thereby decreasing their mobility and bioavailability (i.e., immobilization), and complete removal through thermal and chemical oxidation (i.e., destruction). In this review, the distribution, bioavailability, and remediation of PFAS in soil receiving solid biowastes, which include biosolids, composts, and manure, are presented., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Purpose: Diffusion weighted MRI imaging (DWI) is often subject to low signal-to-noise ratios (SNRs) and artifacts. Recent work has produced software tools that can correct individual problems, but these tools have not been combined with each other and with quality assurance (QA). A single integrated pipeline is proposed to perform DWI preprocessing with a spectrum of tools and produce an intuitive QA document., Methods: The proposed pipeline, built around the FSL, MRTrix3, and ANTs software packages, performs DWI denoising; inter-scan intensity normalization; susceptibility-, eddy current-, and motion-induced artifact correction; and slice-wise signal drop-out imputation. To perform QA on the raw and preprocessed data and each preprocessing operation, the pipeline documents qualitative visualizations, quantitative plots, gradient verifications, and tensor goodness-of-fit and fractional anisotropy analyses., Results: Raw DWI data were preprocessed and quality checked with the proposed pipeline and demonstrated improved SNRs; physiologic intensity ratios; corrected susceptibility-, eddy current-, and motion-induced artifacts; imputed signal-lost slices; and improved tensor fits. The pipeline identified incorrect gradient configurations and file-type conversion errors and was shown to be effective on externally available datasets., Conclusions: The proposed pipeline is a single integrated pipeline that combines established diffusion preprocessing tools from major MRI-focused software packages with intuitive QA., (© 2021 International Society for Magnetic Resonance in Medicine.)

Valorisation of food waste offers an economical and environmental opportunity, which can reduce the problems of its conventional disposal. Food waste is commonly disposed of in landfills or incinerated, causing many environmental, social, and economic issues. Large amounts of food waste are produced in the food supply chain of agriculture: production, post-harvest, distribution (transport), processing, and consumption. Food waste can be valorised into a range of products, including biofertilisers, bioplastics, biofuels, chemicals, and nutraceuticals. Conversion of food waste into these products can reduce the demand of fossil-derived products, which have historically contributed to large amounts of pollution. The variety of food chain suppliers offers a wide range of feedstocks that can be physically, chemically, or biologically altered to form an array of biofertilisers and soil amendments. Composting and anaerobic digestion are the main large-scale conversion methods used today to valorise food waste products to biofertilisers and soil amendments. However, emerging conversion methods such as dehydration, biochar production, and chemical hydrolysis have promising characteristics, which can be utilised in agriculture as well as for soil remediation. Valorising food waste into biofertilisers and soil amendments has great potential to combat land degradation in agricultural areas. Biofertilisers are rich in nutrients that can reduce the dependability of using conventional mineral fertilisers. Food waste products, unlike mineral fertilisers, can also be used as soil amendments to improve productivity. These characteristics of food wastes assist in the remediation of contaminated soils. This paper reviews the volume of food waste within the food chain and types of food waste feedstocks that can be valorised into various products, including the conversion methods. Unintended consequences of the utilisation of food waste as biofertilisers and soil-amendment products resulting from their relatively low concentrations of trace element nutrients and presence of potentially toxic elements are also evaluated., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier Ltd.)

Purpose: Generalizability is an important problem in deep neural networks, especially with variability of data acquisition in clinical magnetic resonance imaging (MRI). Recently, the spatially localized atlas network tiles (SLANT) can effectively segment whole brain, non-contrast T1w MRI with 132 volumetric labels. Transfer learning (TL) is a commonly used domain adaptation tool to update the neural network weights for local factors, yet risks degradation of performance on the original validation/test cohorts. Approach : We explore TL using unlabeled clinical data to address these concerns in the context of adapting SLANT to scanning protocol variations. We optimize whole-brain segmentation on heterogeneous clinical data by leveraging 480 unlabeled pairs of clinically acquired T1w MRI with and without intravenous contrast. We use labels generated on the pre-contrast image to train on the post-contrast image in a five-fold cross-validation framework. We further validated on a withheld test set of 29 paired scans over a different acquisition domain. Results: Using TL, we improve reproducibility across imaging pairs measured by the reproducibility Dice coefficient (rDSC) between the pre- and post-contrast image. We showed an increase over the original SLANT algorithm (rDSC 0.82 versus 0.72) and the FreeSurfer v6.0.1 segmentation pipeline ( rDSC = 0.53 ). We demonstrate the impact of this work decreasing the root-mean-squared error of volumetric estimates of the hippocampus between paired images of the same subject by 67%. Conclusion: This work demonstrates a pipeline for unlabeled clinical data to translate algorithms optimized for research data to generalize toward heterogeneous clinical acquisitions., (© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Molybdenum disulfide (MoS 2 ) is a promising candidate as a high-performing anode material for sodium-ion batteries (SIBs) due to its large interlayer spacing. However, it suffers from continued capacity fading. This problem could be overcome by hybridizing MoS 2 with nanostructured carbon-based materials, but it is quite challenging. Herein, we demonstrate a single-step strategy for the preparation of MoS 2 coupled with ordered mesoporous carbon nitride using a nanotemplating approach which involves the pyrolysis of phosphomolybdic acid hydrate (PMA), dithiooxamide (DTO) and 5-amino-1H-tetrazole (5-ATTZ) together in the porous channels of 3D mesoporous silica template. The sulfidation to MoS 2 , polymerization to carbon nitride (CN) and their hybridization occur simultaneously within a mesoporous silica template during a calcination process. The CN/MoS 2 hybrid prepared by this unique approach is highly pure and exhibits good crystallinity as well as delivers excellent performance for SIBs with specific capacities of 605 and 431 mAhg -1 at current densities of 100 and 1000 mAg -1 , respectively, for SIBs., (© 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Magnetite (Fe³O⁴) and Cobalt-doped Fe³O⁴ nanoparticles were obtained by hydrothermal reaction. The synthesized products were characterized by X-ray diffraction, Energy dispersive spectroscopy, Scanning electron microscopy, and Zeta potential. The results show that Co was substituted in the Fe³O⁴ crystal structure as CoFe₂O₄ phase. The synthesized materials are nanometer in size having uniform morphology, negatively charged and cobalt concentration varied from 2.5 to 7.5 wt.%. The magnetite and Co-doped magnetite nanoparticles at a low concentration (3 wt.%) were dispersed in the epoxy resin. The effect of the magnetite and Co-doped magnetite nanoparticles on the anticorrosion performance of the protective epoxy coatings covered on carbon steel surface was characterized by Electrochemical Impedance Spectroscopy (EIS) and salt fog exposure. Codoped magnetite nanoparticles at 2.5 wt.% provided high protection of the coatings. In addition, Pull-off tests confirmed an adhesion improvement of the epoxy coating filled by the Co-doped Fe³O⁴ nanoparticles.

Nanoporous carbon (HNC) with a flake and nanotubular morphology and a high specific surface area is prepared by using natural halloysite nanotubes (HNTs), a low-cost and naturally available clay material with a mixture of flaky and tubular morphology. A controlled pore-filling technique is used to selectively control the porosity, morphology, and the specific surface area of the HNC. Activated nanoporous carbon (AHNC) with a high specific surface area is also prepared by using HNT together with the activation process with zinc chloride (ZnCl 2 ). HNC exhibits flakes and tubular morphologies, which offer a high specific surface area (837 m 2 /g). The specific surface area of AHNC is 1646 m 2 /g, 74 times greater than the specific surface area of pure HNT (22.5 m 2 /g). These data revealed that the single-step activation combined with the nanotemplating results in creating a huge impact on the specific surface area of the HNC. Both HNC and AHNC are employed as adsorbents for CO 2 adsorption at different pressures and adsorption temperatures. The CO 2 adsorption capacity of AHNC is 25.7 mmol/g at 0 °C, which is found to be significantly higher than that of activated carbon (AC), mesoporous carbon (CMK-3), mesoporous carbon nitride (MCN-1), and multiwalled carbon nanotube (MWCNT). AHNC is also tested as an electroactive material and demonstrates good supercapacitance, cyclic stability, and high capacitance retention. Specific capacitance of AHNC in the aqueous electrolyte is 197 F/g at 0.3 A/g, which is higher than that of AC, MWCNT, and CMK-3. The technique adopted for the preparation of both HNC and AHNC is quite unique and simple, has the potential to replace the existing highly expensive and sophisticated mesoporous silica-based nanotemplating strategy, and could also be applied for the fabrication of series of advanced nanostructures with unique functionalities.

In this study, chitosan and alginate were selected to prepare alginate/chitosan nanoparticles to load the drug lovastatin by the ionic gelation method. The synthesized nanoparticles loaded with drug were characterized by Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), laser scattering and differential scanning calorimetry (DSC) methods. The FTIR spectrum of the alginate/chitosan/lovastatin nanoparticles showed that chitosan and alginate interacted with lovastatin through hydrogen bonding and dipolar-dipolar interactions between the C-O, C=O, and OH groups in lovastatin, the C-O, NH, and OH groups in chitosan and the C-O, C=O, and OH groups in alginate. The laser scattering results and SEM images indicated that the alginate/chitosan/lovastatin nanoparticles have a spherical shape with a particle size in the range of 50-80 nm. The DSC diagrams displayed that the melting temperature of the alginate/chitosan/lovastatin nanoparticles was higher than that of chitosan and lower than that of alginate. This result means that the alginate and chitosan interact together, so that the nanoparticles have a larger crystal degree when compared with alginate and chitosan individually. Investigations of the in vitro lovastatin release from the alginate/chitosan/lovastatin nanoparticles under different conditions, including different alginate/chitosan ratios, different solution pH values and different lovastatin contents, were carried out by ultraviolet-visible spectroscopy. The rate of drug release from the nanoparticles is proportional to the increase in the solution pH and inversely proportional to the content of the loaded lovastatin. The drug release process is divided into two stages: a rapid stage over the first 10 hr, then the release becomes gradual and stable. The Korsmeyer-Peppas model is most suitable for the lovastatin release process from the alginate/chitosan/lovastatin nanoparticles in the first stage, and then the drug release complies with other models depending on solution pH in the slow release stage. In addition, the toxicity of alginate/chitosan/lovastatin (abbreviated ACL) nanoparticles was sufficiently low in mice in the acute toxicity test. The LD 50 of the drug was higher than 5000 mg/kg, while in the subchronic toxicity test with treatments of 100 mg/kg and 300 mg/kg ACL nanoparticles, there were no abnormal signs, mortality, or toxicity in general to the function or structure of the crucial organs. The results show that the ACL nanoparticles are safe in mice and that these composite nanoparticles might be useful as a new drug carrier.

Knowledge about polar derivatives of polycyclic aromatic hydrocarbons (PAHs) in soils is limited despite the extensive study of the environmental presence and persistence of non-polar parent PAHs. Polar PAHs have greater potential to be more toxic at low environmental concentrations compared to their homocyclic analogues. For both polar and non-polar PAHs, combustion of fossil fuels is often the main source especially in industrialised environments. This study investigated the concentration profiles of PAHs and its associated polar PAHs such as nitrated PAHs (NPAHs), oxygenated PAHs (oxy-PAHs) and nitrogen, sulphur and oxygen heterocyclic PAHs (N/S/O-heterocyclic PAHs) in a well-known industrial heritage city of Australia. The most abundant polar PAHs were 9-fluorenone (oxy-PAHs), 2-nitrofluorene (NPAHs) and carbazole (heterocyclic-PAHs). A positive correlation (r = 0.5, p < 0.01) between ∑13PAHs and ∑19 polar PAHs was observed, implying a possible spatial association between parent and polar PAHs. The concentrations of polar PAHs in soil samples, across various landuse patterns, were used to calculate the excess lifetime cancer risk (ELCR) from incidental ingestion of soils. The computed ELCR values ranged from 8.2*10 -7 (industrial soils) to 2.3*10 -6 (residential soils), indicating negligible cancer risks. This is the first known study on the occurrence and concentrations of polar and non-polar PAHs in any Australian city, and the results may serve a baseline purpose for improved risk assessment of contaminated sites., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Here we report on the structural characterization and the hydrogen storage performance of naturally derived halloysite nanotubes (HNTs). HNTs were mined from different deposits in Australia and purified with different processes including crushing, blunging, reblunging, sedimentation and filtration. The clay materials were characterized by different techniques such as powder XRD, TGA, XPS, FTIR spectroscopy, SEM, TEM, and N₂ sorption. Characterization results revealed that they are highly porous in nature with tubular morphology and exhibited excellent thermal stability. Among the halloysite materials studied, HNT1 which is having higher halloysite content and less kaolinite exhibited hydrogen uptake of 0.5 wt.% at 1 bar and -196 °C, which is increased to 1.33 wt.% when the pressure raised to 48 bar. High hydrogen uptake was linked with the high surface area, hollow tubular aluminosilicate structure and the large interlayer spacing of the HNTs as they favour physisorption of hydrogen. It was also demonstrated that HNT1 is considered to be better material than some of the materials reported so far in terms of their cost-effectiveness and environmental safety for the hydrogen storage.