Search

In a randomized controlled trial (RCT), it is possible to improve precision and power and reduce sample size by appropriately adjusting for baseline covariates. There are multiple statistical methods to adjust for prognostic baseline covariates, such as an ANCOVA method. In this paper, we propose a clustering-based stratification method for adjusting for the prognostic baseline covariates. Clusters (strata) are formed only based on prognostic baseline covariates, not outcome data nor treatment assignment. Therefore, the clustering procedure can be completed prior to the availability of outcome data. The treatment effect is estimated in each cluster, and the overall treatment effect is derived by combining all cluster-specific treatment effect estimates. The proposed implementation of the procedure is described. Simulations studies and an example are presented.

The document ICH E9 (R1) has brought much attention to the concept of estimand in the clinical trials community. ICH stands for International Conference for Harmonization. In this article, we draw attention to one facet of estimand that is not discussed in that document but is crucial in the context of observational studies, namely weighting for covariate balance. How weighting schemes are connected to estimand, or more specifically to one of its five attributes identified in ICH E9 (R1), the attribute of population, is illustrated using the Rubin Causal Model. Three estimands are examined from both theoretical and practical perspectives. Factors that may be considered in choosing among these estimands are discussed.

In this paper, a propensity score-integrated power prior approach is developed to augment the control arm of a two-arm randomized controlled trial (RCT) with subjects from multiple external data sources such as real-world data (RWD) and historical clinical studies containing subject-level outcomes and covariates. The propensity scores for the subjects in the external data sources versus the subjects in the RCT are first estimated, and then subjects are placed in different strata based on their estimated propensity scores. Within each propensity score stratum, a power prior is formulated with the information contributed by the external data sources, and Bayesian inference on the treatment effect is obtained. The proposed approach is implemented under the two-stage study design framework utilizing the outcome-free principle to ensure the integrity of a study. An illustrative example is provided to demonstrate the implementation of the proposed approach.

Performance goals are numerical target values pertaining to effectiveness or safety endpoints in single-arm medical device clinical studies. Typically, performance goals are determined at the planning stage of the investigational study under consideration based on summarized outcome information from existing relevant clinical trials. In recent years, there is a growing interest in leveraging real-world evidence in medical product development. In this article, we introduce a new method for proposing performance goals by leveraging real-world evidence. The method applies entropy balancing to address possible patient dissimilarities between the study's target patient population and existing real-world patients, and can take into account operation differences between clinical studies and real-world clinical practice. An illustrative example is provided to demonstrate how to implement the proposed method for performance goal determination while leveraging real-world evidence.

The interest in utilizing real-world data (RWD) has been considerably increasing in medical product development and evaluation. With proper usage and analysis of high-quality real-world data, real-world evidence (RWE) can be generated to inform regulatory and healthcare decision-making. This paper proposes a study design and data analysis approach for a prospective, single-arm clinical study that is supplemented with patients from multiple real-world data sources containing patient-level covariate and outcome data. After the amount of information to be borrowed from each real-world data source is determined, the propensity score-integrated composite likelihood method is applied to obtain an estimate of the parameter of interest based on data from the prospective clinical study and this real-world data source. This method is applied to each real-world data source. The final estimate of the parameter of interest is then obtained by taking a weighted average of all these estimates. The performance of the proposed approach is evaluated via a simulation study. A hypothetical example is presented to illustrate how to implement the proposed approach.

The evaluation of diagnostic tests usually involves statistical inference for its sensitivity. As sensitivity is defined as the probability that the test result will be positive when the target condition is present, the key study design consideration of sample size is the determination of the number of subjects with the target condition such that the estimation has adequate precision, or the hypothesis testing has adequate power. Traditionally, one may rely on prospective screening of subjects to obtain the required sample size, which means that if the prevalence of the disease is very low, a large number of subjects would need to be screened, increasing the study duration and cost. In this paper, we consider the possibility of substantially reducing the length and cost of a clinical study by leveraging subjects from a real-world data (RWD) source, focusing specifically on the diagnostic test for the cancer of interest. Using the propensity score methodology, we developed a procedure which ensures that the real-world subjects being leveraged are similar to their prospectively enrolled counterparts, thereby making the leveraging more justified. The procedure allows the down-weighting of the real-world subjects, which can be achieved by either using a Frequentist's method based on the composite likelihood or a Bayesian method based on the power prior. The proposed approach can be applied to the evaluation of any diagnostic test and it is not limited to the current clinical study regarding a cancer diagnostic test. Notably, this paper is in close alignment with a recently released draft framework by the Medical Device Innovation Consortium (MDIC) on real-world clinical evidence and

The spread of COVID-19 has created tremendous challenges to ongoing clinical studies essential to finding effective treatments and cures for a myriad of diseases, with some studies having suspended...

Meningeal lymphatic vessels (MLVs) contribute to waste product elimination and immune surveillance in brain tissues. MLVs were identified in the dorsal and caudo-basal regions of the dura mater, where they ensure the clearance of cerebrospinal fluid (CSF). Whether MLVs exist in the complex anterior part of the murine and human skull, and how they connect with the glymphatic system and extracranial lymphatic vasculature remained unclear. Here, we generated three-dimensional (3D) maps of MLV drainage by light-sheet fluorescence microscopy (LSFM) imaging of mouse whole-head preparations following fluorescent OVA-A555 tracer injections into the CSF. In humans, we performed real-time magnetic resonance vessel wall imaging (MR-VWI) after systemic gadobutrol injections. We observed a conserved 3D-anatomy of MLVs in mice and humans, and we discovered an extended anterior network around the dural cavernous sinus including multiple capillary beds and exit routes through the foramina of emissary veins. MR-VWI may provide a diagnostic tool for patients with CSF drainage defects and neurological diseases.Short abstractWe established the 3D-anatomy of meningeal lymphatic vasculature and associated CSF drainage by postmortem light-sheet imaging in mice and by real-time magnetic resonance imaging in humans, demonstrating conserved lymphatic circuitries in contact with dural venous sinuses.

International audience; Meningeal lymphatic vessels (MLVs) were identified in the dorsal and caudobasal regions of the dura mater, where they ensure waste product elimination and immune surveillance of brain tissues. Whether MLVs exist in the anterior part of the murine and human skull and how they connect with the glymphatic system and extracranial lymphatics remained unclear. Here, we used light-sheet fluorescence microscopy (LSFM) imaging of mouse whole-head preparations after OVA-A555 tracer injection into the cerebrospinal fluid (CSF) and performed real-time vessel-wall (VW) magnetic resonance imaging (VW-MRI) after systemic injection of gadobutrol in patients with neurological pathologies. We observed a conserved three-dimensional anatomy of MLVs in mice and humans that aligned with dural venous sinuses but not with nasal CSF outflow, and we discovered an extended anterior MLV network around the cavernous sinus, with exit routes through the foramina of emissary veins. VW-MRI may provide a diagnostic tool for patients with CSF drainage defects and neurological diseases.

External data, referred to as data external to the traditional clinical study being planned, include but are not limited to real-world data (RWD) and data collected from clinical studies being conducted in the past or in other countries. The external data are sometimes leveraged to augment a single-arm, prospectively designed study when appropriate. In such an application, recently developed propensity score-integrated approaches including PSPP and PSCL can be used for study design and data analysis when the clinical outcomes are binary or continuous. In this paper, the propensity score-integrated Kaplan-Meier (PSKM) method is proposed for a similar situation but the outcome of interest is time-to-event. The propensity score methodology is used to select external subjects that are similar to those in the current study in terms of baseline covariates and to stratify the selected subjects from both data sources into more homogeneous strata. The stratum-specific PSKM estimators are obtained based on all subjects in the stratum with the external data being down-weighted, and then these estimators are combined to obtain an overall PSKM estimator. A simulation study is conducted to assess the performance of the PSKM method, and an illustrative example is presented to demonstrate how to implement the proposed method.

In medical product development, there has been a growing interest in utilizing real-world data which have become abundant owing to advances in biomedical science, information technology and engineering. High-quality real-world data may be utilized to generate real-world evidence for regulatory or healthcare decision-making. We discuss propensity score-based approaches for leveraging patients from a real-world data source to construct a control group for a non-randomized comparative study or to augment a single-arm or randomized prospective investigational clinical study. The proposed propensity score-based approaches leverage real-world patients that are similar to those prospectively enrolled into the investigational clinical study in terms of baseline characteristics. Either frequentist or Bayesian inference can then be applied for outcome data analysis, with the option of down-weighting information from the real-world data source. Examples based on pre-market regulatory review experience are provided to illustrate the implementation of the proposed approaches.

In addition to extensive preclinical testing, commercialization of medical devices in the United States under some circumstances may be supported with clinical data derived from single arm studies ...

In this paper, a propensity score-integrated composite likelihood (PSCL) approach is developed for cases in which the control arm of a two-arm randomized controlled trial (RCT) (treated vs control) is augmented with patients from real-world data (RWD) containing both clinical outcomes and covariates at the patient-level. RWD patients who were treated with the same therapy as the control arm of the RCT are considered for the augmentation. The PSCL approach first estimates the propensity score for every patient as the probability of the patient being in the RCT rather than the RWD, and then stratifies all patients into strata based on the estimated propensity scores. Within each propensity score stratum, a composite likelihood function is specified and utilized to down-weight the information contributed by the RWD source. Estimates of the stratum-specific parameters are obtained by maximizing the composite likelihood function. These stratum-specific estimates are then combined to obtain an overall population-level estimate of the parameter of interest. The performance of the proposed approach is evaluated via a simulation study. A hypothetical two-arm RCT and a hypothetical RWD source are used to illustrate the implementation of the proposed approach.

Although the effect of pearl powder has been recognized for more than a thousand years from healthcare to beauty care, there has yet to be an in-depth understanding of its anti-photoaging effect. In the present study, the protective effect of pearl extract (PE) on UV-induced photoaging in mice was evaluated. First, the amino acid analysis of PE was carried out. Then, different dosages of pearl extract gel (PEG) were applied topically on the shaved dorsal skins regions of mice before UV irradiation. Skin physiological and histological analysis, antioxidant enzymes and inflammatory factor test were used to evaluate the anti-photoaging effect of PEG. The results showed that PEG contained 14 amino acids, and could inhibit UV-irritated skin wrinkles, laxity, thickness, and dryness. Moreover, PEG upregulated the activities of CAT, GSH-Px, SOD and decreased MDA level, and suppressed the production of IL-1β, IL-6, PGE

SLIT2 is a secreted polypeptide that guides migration of cells expressing Roundabout 1 and 2 (ROBO1 and ROBO2) receptors. Herein, we investigated SLIT2/ROBO signaling effects in gliomas. In patients with glioblastoma (GBM), SLIT2 expression increased with malignant progression and correlated with poor survival and immunosuppression. Knockdown of SLIT2 in mouse glioma cells and patient-derived GBM xenografts reduced tumor growth and rendered tumors sensitive to immunotherapy. Tumor cell SLIT2 knockdown inhibited macrophage invasion and promoted a cytotoxic gene expression profile, which improved tumor vessel function and enhanced efficacy of chemotherapy and immunotherapy. Mechanistically, SLIT2 promoted microglia/macrophage chemotaxis and tumor-supportive polarization via ROBO1- and ROBO2-mediated PI3K-γ activation. Macrophage Robo1 and Robo2 deletion and systemic SLIT2 trap delivery mimicked SLIT2 knockdown effects on tumor growth and the tumor microenvironment (TME), revealing SLIT2 signaling through macrophage ROBOs as a potentially novel regulator of the GBM microenvironment and immunotherapeutic target for brain tumors. ispartof: JOURNAL OF CLINICAL INVESTIGATION vol:131 issue:16 ispartof: location:United States status: published

We are now at an amazing time for medical product development in drugs, biological products and medical devices. As a result of dramatic recent advances in biomedical science, information technology and engineering, ``big data'' from health care in the real-world have become available. Although big data may not necessarily be attuned to provide the preponderance of evidence to a clinical study, high-quality real-world data can be transformed into scientific evidence for regulatory and healthcare decision-making using proven analytical methods and techniques, such as propensity score methodology and Bayesian inference. In this paper, we extend the Bayesian power prior approach for a single-arm study (the current study) to leverage external real-world data. We use propensity score methodology to pre-select a subset of real-world data containing patients that are similar to those in the current study in terms of covariates, and to stratify the selected patients together with those in the current study into more homogeneous strata. The power prior approach is then applied in each stratum to obtain stratum-specific posterior distributions, which are combined to complete the Bayesian inference for the parameters of interest. We evaluate the performance of the proposed method as compared to that of the ordinary power prior approach by simulation and illustrate its implementation using a hypothetical example, based on our regulatory review experience.

A question that routinely arises in medical device clinical studies is the homogeneity across demographic subgroups, geographical regions, or investigational sites of the enrolled patients in terms of treatment effects or outcome variables. The main objective of this paper is to discuss statistical concepts and methods for the assessment of such homogeneity and to provide the practitioner a statistical framework and points to consider in conducting homogeneity assessment. Demographic subgroups, geographical regions, and investigational sites are discussed separately as each has its unique issues. Specific considerations are also given to randomized controlled trials, non-randomized comparative studies, and single-arm studies. We point out that judicious use of statistical methods, in conjunction with sound clinical judgment, is essential in handling the issue of homogeneity of treatment effect in medical device clinical studies.

Nonrandomized comparative studies have been widely proposed and conducted in the premarket safety and effectiveness evaluation of medical devices. A typical case is to compare results of a clinical...

Observational (non-randomized) comparative studies have been adopted in the pre-market safety/effectiveness evaluation of medical devices. There has been an increased interest in utilizing this design with the growing available real-world data. However, in such studies, biases that are introduced in every stage and aspect of study need to be addressed. Otherwise, the objectivity of study design and validity of study results will be compromised. In this paper, challenges and opportunities are discussed from the regulatory perspective. Considerations and good statistical practice to mitigate the potential bias are presented.

Supercapacitors (SCs) with high gravimetric and volumetric performances are highly demanded for diverse applications, but still remain challenge to achieve for traditional porous carbon materials due to the unbalance between porous properties and high density. Herein, we report a novel oxygen-enriched crumpled graphene (OCGN) with high density prepared through a simple and facile strategy. The as-prepared OCGN possesses several advantageous features of abundant stable active oxygen-containing functional groups (17.26%), high packing density (1.28 g cm−3) and facile ion channel, resulting in high gravimetric and volumetric specific capacitance (300.5 F g−1, 384.6 F cm−3), outstanding rate performance and cyclic stability. In addition, the assembled symmetric SC exhibits a large gravimetric and volumetric energy density of 20.4 Wh kg−1 and 26.1 Wh L−1 in aqueous electrolyte, which is higher than majority of previously reported carbon-based aqueous SCs. The encouraging results demonstrate that designing oxygen-enriched carbon materials with high density may be a novel strategy to realize both high gravimetric and volumetric performance.

Leveraging external data is a topic that have recently received much attention. The propensity score-integrated approaches are a methodological innovation for this purpose. In this paper we adapt these approaches, originally introduced to augment single-arm studies with external data, for the augmentation of both arms of a randomized controlled trial (RCT) with external data. After recapitulating the basic ideas, we provide a step-by-step tutorial of how to implement the propensity score-integrated approaches, from study design to outcome analysis, in the RCT setting in such a way that the study integrity and objectively are maintained. Both the Bayesian (power prior) approach and the frequentist (composite likelihood) approach are included. Some extensions and variations of these approaches are also outlined at the end of this paper.

In medical product development, there has been an increased interest in utilizing real-world data which have become abundant with recent advances in biomedical science, information technology, and engineering. High-quality real-world data may be analyzed to generate real-world evidence that can be utilized in the regulatory and healthcare decision-making. In this paper, we consider the case in which a single-arm clinical study, viewed as the primary data source, is supplemented with patients from a real-world data source containing both clinical outcome and covariate data at the patient-level. Propensity score methodology is used to identify real-world data patients that are similar to those in the single-arm study in terms of the baseline characteristics, and to stratify these patients into strata based on the proximity of the propensity scores. In each stratum, a composite likelihood function of a parameter of interest is constructed by down-weighting the information from the real-world data source, and an estimate of the stratum-specific parameter is obtained by maximizing the composite likelihood function. These stratum-specific estimates are then combined to obtain an overall population-level estimate of the parameter of interest. The performance of the proposed approach is evaluated via a simulation study. A hypothetical example based on our experience is provided to illustrate the implementation of the proposed approach.

Noninferiority trials are commonly utilized to evaluate the safety and effectiveness of medical devices. It could happen that the noninferiority hypothesis is rejected while the performance of the active control is clinically not satisfactory. This may pose a great challenge when making a regulatory decision. To avoid such a difficult situation, we propose to conduct a companion test to assess the performance of the active control when testing the main noninferiority hypothesis and to incorporate such a test into the study design. Under our proposal, the noninferiority of the investigational device to the active control can only be claimed when both hypotheses are rejected. The operating characteristics of the proposed study design based on these two tests can be fully evaluated at the design stage. This proposed approach is aimed to facilitate regulatory decision making in a more transparent manner.

Simultaneous integration of high power density, high energy density, long cycle life and superior safety in a single energy storage system is still a huge challenge. Addressing this issue requires the design of new energy storage systems with novel electrodes. Herein, we propose a novel electrochemical energy storage device called a quasi-solid-state Na-ion hybrid capacitor (QSS–NIC) based on surface oxygen-functionalized crumpled graphene (OCG) as both the negative and positive electrodes and a Na-ion conducting gel polymer as the electrolyte. The as-prepared OCG with dense and porous structure ensures abundant ion-accessible active sites and short ion diffusion path. The surface oxygen functional groups within OCG are favorable for high energy storage when applied as both battery-type negative electrodes and capacitor-type positive electrodes. Benefiting from the elaborate design of electrode materials and device configuration, the QSS–NIC achieves a high energy density of 121.3 W h kg−1, high power density of 8000 W kg−1 and a long cycling life of over 2500 cycles with a capacitance retention of ~ 86.7%. This work successfully demonstrates a proof of concept of quasi-solid-state Na-ion hybrid capacitors as a high performance energy storage device based on two graphene electrodes, which narrows the performance gap between conventional electrochemical capacitors and batteries.

Although two-dimensional (2D) carbon materials are widely investigated, a well-defined 2D carbon nanosheet with an ordered mesostructure has rarely been realized. Monolayer-ordered mesoporous carbon nanosheets (OMCNS) were prepared through confinement assembly of resol and F127 in the interlayer of montmorillonite (MONT). The nanoscale distance of the interlayer space of MONT only allow the assembly of resol and F127 in the same plane, leading to ordered mesopores perpendicular to carbon nanosheets, and favor the formation of sp2 carbon, resulting in a high degree of graphitization. The mesopores on the carbon nanosheets provide efficient ion diffusion, and the high degree of graphitization provides a fast electron-transport route, enabling OMCNS as excellent electrode materials for electric double layer capacitors.

High-performance electrical double-layer capacitors (EDLCs) require carbon electrode materials with high specific surface area, short ion-diffusion pathways, and outstanding electrical conductivity. Herein, a general approach combing the molten-salt method and chemical activation to prepare N-doped carbon nanosheets with high surface area (654 m2 g−1) and adjustable porous structure is presented. Owing to their structural features, the N-doped carbon nanosheets exhibited superior capacitive performance, demonstrated by a maximum capacitance of 243 F g−1 (area-normalized capacitance up to 37 μF cm−2) at a current density of 0.5 A g−1 in aqueous electrolyte, high rate capability (179 F g−1 at 20 A g−1), and excellent cycle stability. This method provides a new route to prepare porous and heteroatom-doped carbon nanosheets for high-performance EDLCs, which could also be extended to other polymer precursors and even waste biomass.

The preparation of free-standing electrode materials with high specific capacitance and flexibility is very important for the production of flexible electric double layer capacitors. There is a great incompatibility, however, between the flexibility and the porosity of the electrode material. In this work, by using coaxial electrospinning, we propose an interface miscibility induced approach to the design of double-capillary carbon nanofibers (DCNF) with micropores in the inner capillary and mesopores in the outer capillary. The unique structure achieves synergism between high accessibility to electrolyte, a short diffusion length for ions, high conductivity, and high flexibility. The DCNFs can be directly used as electrodes to assemble flexible supercapacitors, which show a high gravimetric capacitance of 133 F g −1 and excellent high-rate performance in ionic liquid electrolyte. The maximum energy density and power density reach 56.6 Wh kg −1 and 114 kW kg −1 , respectively. The combination of scalable coaxial electrospinning technology and supercapacitors with excellent performance may pave the way to wearable and safe electronics.

Non-inferiority trials are commonly seen in medical device trials. However, the violation of the constancy assumption could be a serious concern. Some approaches have been proposed to address the impact of violation of the constancy assumption on the validity of a non-inferiority trial such as prevention through good trial conduct and adjustment through analysis. In this paper, we propose a new approach via adding a companion constancy test to the non-inferiority test that consequently protects the validity of a non-inferiority trial under Bayesian framework. In addition, as some clinical data from historical studies are available for the active control prior to the start of the investigational study, we consider two different methods to borrow the available information. We use a simulation study to assess the performance of the proposed approach.

Cranial lymphatic vessels (LVs) are involved in the transport of fluids, macromolecules and central nervous system (CNS) immune responses. Little information about spinal LVs is available, because these delicate structures are embedded within vertebral tissues and difficult to visualize using traditional histology. Here we show an extended vertebral column LV network using three-dimensional imaging of decalcified iDISCO+-clarified spine segments. Vertebral LVs connect to peripheral sensory and sympathetic ganglia and form metameric vertebral circuits connecting to lymph nodes and the thoracic duct. They drain the epidural space and the dura mater around the spinal cord and associate with leukocytes. Vertebral LVs remodel extensively after spinal cord injury and VEGF-C-induced vertebral lymphangiogenesis exacerbates the inflammatory responses, T cell infiltration and demyelination following focal spinal cord lesion. Therefore, vertebral LVs add to skull meningeal LVs as gatekeepers of CNS immunity and may be potential targets to improve the maintenance and repair of spinal tissues., The lymphatic vasculature is essential to maintain fluid homeostasis and immune surveillance, including in the brain where lymphatic vessels were only recently identified. Here, Jacob et al. provide an anatomical map of lymphatic vessels in the vertebral column, where they find these contribute to fluid drainage and immune responses.

In this work, an exfoliated MXene (e-MXene) nanosheets/nickel-aluminum layered double hydroxide (MXene/LDH) composite as supercapacitor electrode material is fabricated by in situ growth of LDH on e-MXene substrate. The LDH platelets homogeneously grown on the surface of the e-MXene sheets construct a three-dimensional (3D) porous structure, which not only leads to high active sites exposure of LDH and facile liquid electrolyte penetration, but also alleviates the volume change of LDH during the charge/discharge process. Meanwhile, the e -MXene substrate forms a conductive network to facilitate the electron transport of active material. The optimized MXene/LDH composite exhibits a high specific capacitance of 1061 F g−1 at a current density of 1 A g−1, excellent capacitance retention of 70% after 4000 cycle tests at a current density of 4 A g−1 and a good rate capability with 556 F g−1 retention at 10 A g−1.

In this short note, we highlight some key elements of the process of submitting an investigational device exemption of a nonrandomized clinical study that involves the use of propensity score methodology. Focus is given to the assessment of balance for propensity score stratification.

Graphene is known to suffer from severe aggregation and incomplete recovery of a π–π conjugated system during the reduction process from graphene oxide. Here we report that these issues can be addressed by using a modified molten salt system. The advantages of the molten salt for reducing graphene show in three aspects: (i) prevent restacking; (ii) restore the conjugated network; (iii) serve as reaction medium for KNO 3 activation and nitrogen doping. The molten-salt method-derived graphene (MNG) displays a highly sp 2 –hybrid constitution, nitrogen doping and hierarchically porous structure. With this design, the MNG–based supercapacitor manifests outstanding specific capacitance (234 F g −1 and 130 F g −1 in 6 M KOH and EMIMBF 4 electrolyte, respectively), high power density, combined with excellent cycling stability and low self-discharge rate. The facile and scalable features of this strategy will be helpful for the rational design of functionalized graphene-based materials for diverse applications.

Two-dimensional (2D) carbon nanosheets have emerged as an attractive candidate for electrical double layer capacitors (EDLCs) due to their large specific surface area, good electrical conductivity and high charge mobility. However, the easy aggregation nature of nanosheets hinders rapid transport of electrolyte ions, reducing the ion-accessible area and restricting the ion transportation. Herein, we propose a template strategy for preparing three-dimensional (3D) porous carbon nanosheets (PCNs) with an oriented and interconnected nanostructure. Zinc layered hydroxide nitrate is used as a layered template and provides a nanospace to confine the carbonization process of the organic carbon precursor (gallic acid). The unique nanostructure and large surface area of chemically activated PCNs (aPCNs) significantly shorten the ion transport length in low dimensions and improve the electrolyte wettability and ion accessible surface area for charge storage. The aPCNs exhibit excellent performance as demonstrated by their large specific capacitance (327 F g−1 at a current density of 0.5 A g−1), superior rate capability (retaining 60.2% at 20 A g−1) and stable cyclability. In particular, the assembled symmetric device based on aPCNs delivers an energy density as high as 10.2 W h kg−1 at a power density of 301 W kg−1.

In this work, ternary metal nitrides are proposed as novel electrode materials for supercapacitors. We selectively fabricate ternary vanadium titanium nitride/carbon (VTiN/C) nanofibers through a facile electrospinning strategy and investigate their electrochemical performance for the first time. The obtained well-interconnected VTiN/C nanofibers with VTiN nanoparticles embedded into carbon ensure rapid electron/ion transfer and offer a highly ion-accessible surface. Appealingly, the VTiN-4/C nanofibers exhibit a greatly improved performance, with a high specific capacitance (430.7 F g−1, 0.5 A g−1) and a good rate capability. Furthermore, the performance of the VTiN/C nanofibers is found to be controllable by adjusting the V/Ti atomic ratio. The encouraging electrochemical performance suggests that ternary VTiN could be a promising electrode material for supercapacitors.

Graphene is considered a promising electrochemical capacitors electrode material due to its high surface area and high electrical conductivity. However, restacking interactions between graphene nanosheets significantly decrease the ion-accessible surface area and impede electronic and ionic transfer. This would, in turn, severely hinder the realization of high energy density. Herein, we report a strategy for preparation of few-layer graphene material with abundant crumples and high-level nitrogen doping. The two-dimensional graphene nanosheets (CNG) feature high ion-available surface area, excellent electronic and ion transfer properties, and high packing density, permitting the CNG electrode to exhibit excellent electrochemical performance. In ionic liquid electrolyte, the CNG electrode exhibits gravimetric and volumetric capacitances of 128 F g(-1) and 98 F cm(-3), respectively, achieving gravimetric and volumetric energy densities of 56 Wh kg(-1) and 43 Wh L(-1). The preparation strategy described here provides a new approach for developing a graphene-based supercapacitor with high gravimetric and volumetric energy densities.

Based on the high specific area and superior electrochemical property of graphene oxide and excellent electro-catalytic performance of Ag2S, graphene oxide stabilized nano Ag2S composite (GO-Ag2S) was proposed and constructed in this work by one-pot homogeneous precipitation. It was expected GO-Ag2S could possess special stability, dispersion and electrochemical activity, which were estimated by SEM, TEM, EDS, XRD, FT-IR, cyclic voltammetry and electrochemical impedance spectra. It was noted that there was a synergistically improved electrochemical performance between Ag2S and GO in as-prepared GO-Ag2S owing to their strong interactions. Especially, it exhibited an exclusively electrochemical response to heavy metal Hg2+ with high sensitivity once modified onto glassy carbon electrode (GCE). Under the optimized conditions, a wide liner range of 0.0–290.0 × 10−10 mol/L and a low detection limit of 8.63 × 10−12 mol/L for Hg2+ could be reached. Once applied for sensing Hg2+ in really environmental water samples and fruit juice samples, the recovery was between 95.5% and 105.7%. Mercury amalgam formed onto the surface of nano Ag2S was accordingly proposed for GO-Ag2S to recognize Hg2+ after the electro-catalytic redox of Hg2+.