Use of Hydrogel Electrolyte in Zn-MnO2 Rechargeable Batteries: Characterization of Safety, Performance, and Cu2+ Ion Diffusion.

Achieving commercially acceptable Zn-MnO2 rechargeable batteries depends on the reversibility of active zinc and manganese materials, and avoiding side reactions during the second electron reaction of MnO2. Typically, liquid electrolytes such as potassium hydroxide (KOH) are used for Zn-MnO2 rechargeable batteries. However, it is known that using liquid electrolytes causes the formation of electrochemically inactive materials, such as precipitation Mn3O4 or ZnMn2O4 resulting from the uncontrollable reaction of Mn3+ dissolved species with zincate ions. In this paper, hydrogel electrolytes are tested for MnO2 electrodes undergoing two-electron cycling. Improved cell safety is achieved because the hydrogel electrolyte is non-spillable, according to standards from the US Department of Transportation (DOT). The cycling of "half cells" with advanced-formulation MnO2 cathodes paired with commercial NiOOH electrodes is tested with hydrogel and a normal electrolyte, to detect changes to the zincate crossover and reaction from anode to cathode. These half cells achieved ≥700 cycles with 99% coulombic efficiency and 63% energy efficiency at C/3 rates based on the second electron capacity of MnO2. Other cycling tests with "full cells" of Zn anodes with the same MnO2 cathodes achieved ~300 cycles until reaching 50% capacity fade, a comparable performance to cells using liquid electrolyte. Electrodes dissected after cycling showed that the liquid electrolyte allowed Cu ions to migrate more than the hydrogel electrolyte. However, measurements of the Cu diffusion coefficient showed no difference between liquid and gel electrolytes; thus, it was hypothesized that the gel electrolytes reduced the occurrence of Cu short circuits by either (a) reducing electrode physical contact to the separator or (b) reducing electro-convective electrolyte transport that may be as important as diffusive transport.

The advent of membrane-less zinc-anode aqueous batteries with lithium battery-like voltage.

Zinc (Zn)-anode batteries, although safe and non-flammable, are precluded from promising applications because of their low voltage (<2 V) and poor rechargeability. Here, we report the fabrication of rechargeable membrane-less Zn-anode batteries with high voltage properties (2.5 to 3.4 V) achieved through coupling cathodes and Zn-anodes in gelled concentrated acid and alkaline solutions separated by a gelled buffer interlayer containing the working ions. The concentrated gelled buffer interlayers perform dual functions of regulating the pH of the system and acting as the source and sink of the working ions. With this strategy we show low-cost membrane-less 2.5 to 3.4 V Zn-manganese dioxide (MnO2) batteries capable of cycling 10-100% of 617 mA h g-1-MnO2 and 20-30% of 820 mA h g-1-Zn and demonstrate their application in electric vehicles. This strategy is then applied to other oxide-based cathode systems like Cu2O and V2O5, where voltages of 2 to 3 V are obtained in membrane-less batteries.

Hydroxyl Conducting Hydrogels Enable Low-Maintenance Commercially Sized Rechargeable Zn-MnO2 Batteries for Use in Solar Microgrids.

Zinc (Zn)-manganese dioxide (MnO2) rechargeable batteries have attracted research interest because of high specific theoretical capacity as well as being environmentally friendly, intrinsically safe and low-cost. Liquid electrolytes, such as potassium hydroxide, are historically used in these batteries; however, many failure mechanisms of the Zn-MnO2 battery chemistry result from the use of liquid electrolytes, including the formation of electrochemically inert phases such as hetaerolite (ZnMn2O4) and the promotion of shape change of the Zn electrode. This manuscript reports on the fundamental and commercial results of gel electrolytes for use in rechargeable Zn-MnO2 batteries as an alternative to liquid electrolytes. The manuscript also reports on novel properties of the gelled electrolyte such as limiting the overdischarge of Zn anodes, which is a problem in liquid electrolyte, and finally its use in solar microgrid applications, which is a first in academic literature. Potentiostatic and galvanostatic tests with the optimized gel electrolyte showed higher capacity retention compared to the tests with the liquid electrolyte, suggesting that gel electrolyte helps reduce Mn3+ dissolution and zincate ion migration from the Zn anode, improving reversibility. Cycling tests for commercially sized prismatic cells showed the gel electrolyte had exceptional cycle life, showing 100% capacity retention for >700 cycles at 9.5 Ah and for >300 cycles at 19 Ah, while the 19 Ah prismatic cell with a liquid electrolyte showed discharge capacity degradation at 100th cycle. We also performed overdischarge protection tests, in which a commercialized prismatic cell with the gel electrolyte was discharged to 0 V and achieved stable discharge capacities, while the liquid electrolyte cell showed discharge capacity fade in the first few cycles. Finally, the gel electrolyte batteries were tested under IEC solar off-grid protocol. It was noted that the gelled Zn-MnO2 batteries outperformed the Pb-acid batteries. Additionally, a designed system nameplated at 2 kWh with a 12 V system with 72 prismatic cells was tested with the same protocol, and it has entered its third year of cycling. This suggests that Zn-MnO2 rechargeable batteries with the gel electrolyte will be an ideal candidate for solar microgrid systems and grid storage in general.

Development and validation of high definition phenotype-based mortality prediction in critical care units.

OBJECTIVES: The objectives of this study are to construct the high definition phenotype (HDP), a novel time-series data structure composed of both primary and derived parameters, using heterogeneous clinical sources and to determine whether different predictive models can utilize the HDP in the neonatal intensive care unit (NICU) to improve neonatal mortality prediction in clinical settings. MATERIALS AND METHODS: A total of 49 primary data parameters were collected from July 2018 to May 2020 from eight level-III NICUs. From a total of 1546 patients, 757 patients were found to contain sufficient fixed, intermittent, and continuous data to create HDPs. Two different predictive models utilizing the HDP, one a logistic regression model (LRM) and the other a deep learning long-short-term memory (LSTM) model, were constructed to predict neonatal mortality at multiple time points during the patient hospitalization. The results were compared with previous illness severity scores, including SNAPPE, SNAPPE-II, CRIB, and CRIB-II. RESULTS: A HDP matrix, including 12 221 536 minutes of patient stay in NICU, was constructed. The LRM model and the LSTM model performed better than existing neonatal illness severity scores in predicting mortality using the area under the receiver operating characteristic curve (AUC) metric. An ablation study showed that utilizing continuous parameters alone results in an AUC score of >80% for both LRM and LSTM, but combining fixed, intermittent, and continuous parameters in the HDP results in scores >85%. The probability of mortality predictive score has recall and precision of 0.88 and 0.77 for the LRM and 0.97 and 0.85 for the LSTM. CONCLUSIONS AND RELEVANCE: The HDP data structure supports multiple analytic techniques, including the statistical LRM approach and the machine learning LSTM approach used in this study. LRM and LSTM predictive models of neonatal mortality utilizing the HDP performed better than existing neonatal illness severity scores. Further research is necessary to create HDP-based clinical decision tools to detect the early onset of neonatal morbidities.

Post-lockdown challenges for ophthalmologists during COVID-19 pandemic in India: A survey-based analysis.

Purpose: The purpose of this study is to evaluate the post-lockdown challenges during Coronavirus disease 2019 (COVID-19) pandemic amongst the ophthalmologists in India. Methods: An online survey was sent to the practicing ophthalmologists across India. Data were collected from the responding ophthalmologists and analysed using Medcalc 16.4 software. Results: A total of 794 responses were obtained. Most respondents (51%) were in the age group 30-50 years and were in independent practice (40.05%). Almost three-fourth of ophthalmologists resumed their surgical services after a gap of more than a month post-lockdown. Almost a third of the respondents had significant reduction in their surgical workload during this period. Significant fear of contracting COVID-19 infection in the operation theatres was reported while moderate difficulty was found in procuring protective gear during immediate post-national lockdown period. Conclusion: The pandemic has changed the ophthalmic practice significantly, with patient and staff safety becoming areas of major concern. Both financial and psychological concerns affecting healthcare workers need addressing for continued patient care.

Designing a bed-side system for predicting length of stay in a neonatal intensive care unit.

Increased length of stay (LOS) in intensive care units is directly associated with the financial burden, anxiety, and increased mortality risks. In the current study, we have incorporated the association of day-to-day nutrition and medication data of the patient during its stay in hospital with its predicted LOS. To demonstrate the same, we developed a model to predict the LOS using risk factors (a) perinatal and antenatal details, (b) deviation of nutrition and medication dosage from guidelines, and (c) clinical diagnoses encountered during NICU stay. Data of 836 patient records (12 months) from two NICU sites were used and validated on 211 patient records (4 months). A bedside user interface integrated with EMR has been designed to display the model performance results on the validation dataset. The study shows that each gestation age group of patients has unique and independent risk factors associated with the LOS. The gestation is a significant risk factor for neonates < 34 weeks, nutrition deviation for < 32 weeks, and clinical diagnosis (sepsis) for ≥ 32 weeks. Patients on medications had considerable extra LOS for ≥ 32 weeks' gestation. The presented LOS model is tailored for each patient, and deviations from the recommended nutrition and medication guidelines were significantly associated with the predicted LOS.

Machine Learning-Based Automatic Classification of Video Recorded Neonatal Manipulations and Associated Physiological Parameters: A Feasibility Study.

Our objective in this study was to determine if machine learning (ML) can automatically recognize neonatal manipulations, along with associated changes in physiological parameters. A retrospective observational study was carried out in two Neonatal Intensive Care Units (NICUs) between December 2019 to April 2020. Both the video and physiological data (heart rate (HR) and oxygen saturation (SpO2)) were captured during NICU hospitalization. The proposed classification of neonatal manipulations was achieved by a deep learning system consisting of an Inception-v3 convolutional neural network (CNN), followed by transfer learning layers of Long Short-Term Memory (LSTM). Physiological signals prior to manipulations (baseline) were compared to during and after manipulations. The validation of the system was done using the leave-one-out strategy with input of 8 s of video exhibiting manipulation activity. Ten neonates were video recorded during an average length of stay of 24.5 days. Each neonate had an average of 528 manipulations during their NICU hospitalization, with the average duration of performing these manipulations varying from 28.9 s for patting, 45.5 s for a diaper change, and 108.9 s for tube feeding. The accuracy of the system was 95% for training and 85% for the validation dataset. In neonates <32 weeks' gestation, diaper changes were associated with significant changes in HR and SpO2, and, for neonates ≥32 weeks' gestation, patting and tube feeding were associated with significant changes in HR. The presented system can classify and document the manipulations with high accuracy. Moreover, the study suggests that manipulations impact physiological parameters.

Zincate-Blocking-Functionalized Polysulfone Separators for Secondary Zn-MnO2 Batteries.

Alkaline zinc-manganese dioxide (Zn-MnO2) batteries are well suited for grid storage applications because of their inherently safe, aqueous electrolyte and established materials supply chain, resulting in low production costs. With recent advances in the development of Cu/Bi-stabilized birnessite cathodes capable of the full 2-electron capacity equivalent of MnO2 (617 mA h/g), there is a need for selective separators that prevent zincate (Zn(OH)4)2- transport from the anode to the cathode during cycling, as this electrode system fails in the presence of dissolved zinc. Herein, we present the synthesis of N-butylimidazolium-functionalized polysulfone (NBI-PSU)-based separators and evaluate their ability to selectively transport hydroxide over zincate. We then examine their impact on the cycling of high depth of discharge Zn/(Cu/Bi-MnO2) batteries when inserted in between the cathode and anode. Initially, we establish our membranes' selectivity by performing zincate and hydroxide diffusion tests, showing a marked improvement in zincate-blocking (DZn (cm2/min): 0.17 ± 0.04 × 10-6 for 50-PSU, our most selective separator vs 2.0 ± 0.8 × 10-6 for Cellophane 350P00 and 5.7 ± 0.8 × 10-6 for Celgard 3501), while maintaining similar crossover rates for hydroxide (DOH (cm2/min): 9.4 ± 0.1 × 10-6 for 50-PSU vs 17 ± 0.5 × 10-6 for Cellophane 350P00 and 6.7 ± 0.6 × 10-6 for Celgard 3501). We then implement our membranes into cells and observe an improvement in cycle life over control cells containing only the commercial separators (cell lifetime extended from 21 to 79 cycles).

Development of data dictionary for neonatal intensive care unit: advancement towards a better critical care unit.

BACKGROUND: Critical care units (CCUs) with extensive use of various monitoring devices generate massive data. To utilize the valuable information of these devices; data are collected and stored using systems like clinical information system and laboratory information management system. These systems are proprietary, allow limited access to their database and, have the vendor-specific clinical implementation. In this study, we focus on developing an open-source web-based meta-data repository for CCU representing stay of the patient with relevant details. METHODS: After developing the web-based open-source repository named data dictionary (DD), we analyzed prospective data from 2 sites for 4 months for data quality dimensions (completeness, timeliness, validity, accuracy, and consistency), morbidity, and clinical outcomes. We used a regression model to highlight the significance of practice variations linked with various quality indicators. RESULTS: DD with 1555 fields (89.6% categorical and 11.4% text fields) is presented to cover the clinical workflow of a CCU. The overall quality of 1795 patient days data with respect to standard quality dimensions is 87%. The data exhibit 88% completeness, 97% accuracy, 91% timeliness, and 94% validity in terms of representing CCU processes. The data scores only 67% in terms of consistency. Furthermore, quality indicators and practice variations are strongly correlated (P < 0.05). CONCLUSION: This study documents DD for standardized data collection in CCU. DD provides robust data and insights for audit purposes and pathways for CCU to target practice improvements leading to specific quality improvements.

iCHRCloud: Web & Mobile based Child Health Imprints for Smart Healthcare.

Reducing child mortality with quality care is the prime-most concern of all nations. Thus in current IT era, our healthcare industry needs to focus on adapting information technology in healthcare services. Barring few preliminary attempts to digitalize basic hospital administrative and clinical functions, even today in India, child health and vaccination records are still maintained as paper-based records. Also, error in manually plotting the parameters in growth charts results in missed opportunities for early detection of growth disorders in children. To address these concerns, we present India's first hospital linked, affordable automated vaccination and real-time child's growth monitoring cloud based application- Integrated Child Health Record cloud (iCHRcloud). This application is based on HL7 protocol enabling integration with hospital's HIS/EMR system. It provides Java (Enterprise Service Bus and Hibernate) based web portal for doctors and mobile application for parents, enhancing doctor-parent engagement. It leverages highchart to automate chart preparation and provides access of data via Push Notification (GCM and APNS) to parents on iOS and Android mobile platforms. iCHRcloud has also been recognized as one of the best innovative solution in three nationwide challenges, 2016 in India. iCHRcloud offers a seamless, secure (256 bit HTTPS) and sustainable solution to reduce child mortality. Detail analysis on preliminary data of 16,490 child health records highlight the diversified need of various demographic regions. Thus, primary lesson would be to implement better validation strategies to fulfill the customize requisites of entire population. This paper presents first glimpse of data and power of the analytics in policy framework.

iNICU - Integrated Neonatal Care Unit: Capturing Neonatal Journey in an Intelligent Data Way.

Neonatal period represents first 28 days of life, which is the most vulnerable time for a child's survival especially for the preterm babies. High neonatal mortality is a prominent and persistent problem across the globe. Non-availability of trained staff and infrastructure are the major recognized hurdles in the quality care of these neonates. Hourly progress growth charts and reports are still maintained manually by nurses along with continuous calculation of drug dosage and nutrition as per the changing weight of the baby. iNICU (integrated Neonatology Intensive Care Unit) leverages Beaglebone and Intel Edison based IoT integration with biomedical devices in NICU i.e. monitor, ventilator and blood gas machine. iNICU is hosted on IBM Softlayer based cloud computing infrastructure and map NICU workflow in Java based responsive web application to provide translational research informatics support to the clinicians. iNICU captures real time vital parameters i.e. respiration rate, heart rate, lab data and PACS amounting for millions of data points per day per child. Stream of data is sent to Apache Kafka layer which stores the same in Apache Cassandra NoSQL. iNICU also captures clinical data like feed intake, urine output, and daily assessment of child in PostgreSQL database. It acts as first Big Data hub (of both structured and unstructured data) of neonates across India offering temporal (longitudinal) data of their stay in NICU and allow clinicians in evaluating efficacy of their interventions. iNICU leverages drools based clinical rule based engine and deep learning based big data analytical model coded in R and PMML. iNICU solution aims to improve care time, fills skill gap, enable remote monitoring of neonates in rural regions, assists in identifying the early onset of disease, and reduction in neonatal mortality.

Regenerable Cu-intercalated MnO2 layered cathode for highly cyclable energy dense batteries.

Manganese dioxide cathodes are inexpensive and have high theoretical capacity (based on two electrons) of 617 mAh g-1, making them attractive for low-cost, energy-dense batteries. They are used in non-rechargeable batteries with anodes like zinc. Only ∼10% of the theoretical capacity is currently accessible in rechargeable alkaline systems. Attempts to access the full capacity using additives have been unsuccessful. We report a class of Bi-birnessite (a layered manganese oxide polymorph mixed with bismuth oxide (Bi2O3)) cathodes intercalated with Cu2+ that deliver near-full two-electron capacity reversibly for >6,000 cycles. The key to rechargeability lies in exploiting the redox potentials of Cu to reversibly intercalate into the Bi-birnessite-layered structure during its dissolution and precipitation process for stabilizing and enhancing its charge transfer characteristics. This process holds promise for other applications like catalysis and intercalation of metal ions into layered structures. A large prismatic rechargeable Zn-birnessite cell delivering ∼140 Wh l-1 is shown.

Microemulsion-based synthesis and electrochemical evaluation of different nanostructures of LiCoO2 prepared through sacrificial nanowire templates.

For the first time, we demonstrate the use of a microemulsion reaction to synthesize different nanostructures of LiCoO2 cathode material. By varying the annealing temperature and time, porous nanowires and nanoparticles of LiCoO2 are obtained. The electrochemical performances of these different nanostructures obtained under the respective annealing conditions are evaluated. It is shown that nanoparticles formed under the annealing condition of 700 °C, 1.5 h perform the best, delivering an initial capacity of around 135 mA h g(-1), which is close to the theoretical capacity of LiCoO2, 140 mA h g(-1). They also exhibit a capacity retention of around 93% by 100 cycles at 0.1 C. Comparisons are made between our LiCoO2 material obtained under different annealing conditions and those in the literature.

Nanostructure-based thermoelectric conversion: an insight into the feasibility and sustainability for large-scale deployment.

Significant scientific progress has been achieved using nanostructured materials for thermoelectric energy harvesting and solid-state cooling through the conversion of waste heat into electricity and vice versa. However, the connection between the small-scale proof-of concept results achieved in research labs and real industrial scale manufacture is still missing. Herein we develop an analysis to determine the appropriate thermoelectric nanomaterials for the large-scale manufacture and deployment in the near future. We cover key parameters such as ZT value, cost, abundance, and toxicity. Maximum ZT values are considered at three temperature ranges. Material cost and abundance are visually demonstrated to improve ease of interpretation. Toxicity is also evaluated to minimize the environmental impact during manufacture and recycling. Lastly, a parameter termed "efficiency ratio" is calculated to give a better qualitative understanding of the feasibility and sustainability of these nanomaterials.

Self-templated synthesis and thermal conductivity investigation for ultrathin perovskite oxide nanowires.

The large thermal conductivity of bulk complex metal oxides such as SrTiO(3), NaCo(2)O(4), and Ca(3)Co(4)O(9) has set a barrier for the improvement of thermoelectric figure of merit and the applications of these materials in high temperature (≥1000 K) thermoelectric energy harvesting and solid-state cooling. Here, we present a self-templated synthesis approach to grow ultrathin SrTiO(3) nanowires with an average diameter of 6 nm in large quantity. The thermal conductivity of the bulk pellet made by compressing nanowire powder using spark plasma sintering shows a 64% reduction in thermal conductivity at 1000 K, which agrees well with theoretical modeling.

Semiconductor nanostructure-based photovoltaic solar cells.

Substantial efforts have been devoted to design, synthesize, and integrate various semiconductor nanostructures for photovoltaic (PV) solar cells. In this article, we will review the recent progress in this exciting area and cover the material chemistry and physics related to all-inorganic nanostructure solar cells, hybrid inorganic nanostructure-conductive polymer composite solar cells, and dye-sensitized solar cells.