Portable multi-sensor air quality monitoring platform for personal exposure studies

© 1998-2012 IEEE. Poor air quality is considered among the main causes of millions of premature deaths annually, about 8 million in 2012 according to the World Health Organization [1]. Several epidemiological studies have found a relationship between exposure beyond specified limits and burden of disease [2]. These and many more have led to an increased and urgent need to both monitor and consequently limit personal exposure to harmful pollutants [3]. There is a generalized attention from different governmental agencies globally to limit anthropogenic emissions via legislations and policies. City councils, including for instance in Cambridge, UK, promote new policies that help accelerate switching from combustion engines to electric vehicles both for public and for private transport, accompanied by installation of a distributed urban network of rapid charging points by 2020 [4], along with imposition of road taxes, and exemption from the same based on vehicles' emission of polluting gases and smaller particulate matter (PM2.5). Besides, reduction of indoor wood burning for heating, as one of the largest sources of indoor particulate matter, also has the potential to drastically reduce PM2.5 exposure levels. Citizens are nowadays more conscious of and attentive to their personal exposure to polluting agents and environments and prone to adopt cleaner solutions for living, transport, energy generation and heating. Adoption of personal exposure monitoring devices on an individual level therefore offers multiple benefits and is motivated by the willingness to continue making healthier choices in everyday life consistently. Access to high resolution data of personal exposure with high space and time accuracy is however difficult to achieve with currently available centralized or public network of monitoring stations. Providing individuals with portable devices for air quality monitoring that operate in real time and allow them to monitor and record their personalized exposure levels in combination with conventional geolocation offering a half-meter or less space resolution may become a unique instrument and breakthrough, not only for the individuals, but also for the city councils, and other government agencies to shape and fine-tune their policies for control of air quality in urban, industrial and rural areas. This is possible with the use of both existing and emerging technologies for autonomous sensors, data communication and modern mobile networks, including Internet of Things

Machine learning methods for automatic silent speech recognition using a wearable graphene strain gauge sensor

Silent speech recognition is the ability to recognise intended speech without audio information. Useful applications can be found in situations where sound waves are not produced or cannot be heard. Examples include speakers with physical voice impairments or environments in which audio transference is not reliable or secure. Developing a device which can detect non-auditory signals and map them to intended phonation could be used to develop a device to assist in such situations. In this work, we propose a graphene-based strain gauge sensor which can be worn on the throat and detect small muscle movements and vibrations. Machine learning algorithms then decode the non-audio signals and create a prediction on intended speech. The proposed strain gauge sensor is highly wearable, utilising graphene’s unique and beneficial properties including strength, flexibility and high conductivity. A highly flexible and wearable sensor able to pick up small throat movements is fabricated by screen printing graphene onto lycra fabric. A framework for interpreting this information is proposed which explores the use of several machine learning techniques to predict intended words from the signals. A dataset of 15 unique words and four movements, each with 20 repetitions, was developed and used for the training of the machine learning algorithms. The results demonstrate the ability for such sensors to be able to predict spoken words. We produced a word accuracy rate of 55% on the word dataset and 85% on the movements dataset. This work demonstrates a proof-of-concept for the viability of combining a highly wearable graphene strain gauge and machine leaning methods to automate silent speech recognition.EP/S023046/

Covalently interconnected transition metal dichalcogenide networks via defect engineering for high-performance electronic devices.

Solution-processed semiconducting transition metal dichalcogenides are at the centre of an ever-increasing research effort in printed (opto)electronics. However, device performance is limited by structural defects resulting from the exfoliation process and poor inter-flake electronic connectivity. Here, we report a new molecular strategy to boost the electrical performance of transition metal dichalcogenide-based devices via the use of dithiolated conjugated molecules, to simultaneously heal sulfur vacancies in solution-processed transition metal disulfides and covalently bridge adjacent flakes, thereby promoting percolation pathways for the charge transport. We achieve a reproducible increase by one order of magnitude in field-effect mobility (µFE), current ratio (ION/IOFF) and switching time (τS) for liquid-gated transistors, reaching 10-2 cm2 V-1 s-1, 104 and 18 ms, respectively. Our functionalization strategy is a universal route to simultaneously enhance the electronic connectivity in transition metal disulfide networks and tailor on demand their physicochemical properties according to the envisioned applications.European Commission through the Graphene Flagship, the ERC Grants SUPRA2DMAT (GA-833707), FUTURE-PRINT (GA-694101), Hetero2D, GSYNCOR, the EU Grant Neurofibres, the Agence Nationale de la Recherche through the Labex projects CSC (ANR-10-LABX-0026 CSC) and NIE (ANR-11-LABX-0058 NIE) within the Investissement d’Avenir program (ANR-10-120 IDEX-0002-02), the International Center for Frontier Research in Chemistry (icFRC), EPSRC Grants EP/K01711X/1, EP/K017144/1, EP/N010345/1, EP/L016057/1, and the Faraday Institution. The HAADF-STEM characterization was carried out in the Advanced Microscopy Laboratory (Dublin), a Science Foundation Ireland (SFI) supported centre

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Background: Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. // Methods: We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung's disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. // Findings: We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung's disease) from 264 hospitals (89 in high-income countries, 166 in middle-income countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in low-income countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. // Interpretation: Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between low-income, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Five insights from the Global Burden of Disease Study 2019

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a rules-based synthesis of the available evidence on levels and trends in health outcomes, a diverse set of risk factors, and health system responses. GBD 2019 covered 204 countries and territories, as well as first administrative level disaggregations for 22 countries, from 1990 to 2019. Because GBD is highly standardised and comprehensive, spanning both fatal and non-fatal outcomes, and uses a mutually exclusive and collectively exhaustive list of hierarchical disease and injury causes, the study provides a powerful basis for detailed and broad insights on global health trends and emerging challenges. GBD 2019 incorporates data from 281 586 sources and provides more than 3.5 billion estimates of health outcome and health system measures of interest for global, national, and subnational policy dialogue. All GBD estimates are publicly available and adhere to the Guidelines on Accurate and Transparent Health Estimate Reporting. From this vast amount of information, five key insights that are important for health, social, and economic development strategies have been distilled. These insights are subject to the many limitations outlined in each of the component GBD capstone papers.Peer reviewe

Printable sensors for Nitrogen dioxide and Ammonia sensing at room temperature

© 2019 IEEE. Several studies have found a link between poor air quality and incidences of respiratory and cardiovascular diseases [1]-[6]. A World Health Organisation (WHO) report put the number of deaths caused by household air pollution at 4.3 million in 2012 [7]. Attempts at reducing poor air quality-related mortality have seen specification of safe human exposure limits for daily, and yearly average by government agencies such as Environmental Protection Agency (EPA) and World Health Organisation (WHO). Vehicle exhausts are key sources of Nitrogen dioxide, a key pollutant of ambient air. Other sources of NO2 includes fossil fuels and industrial engines. [8] NO2 is very toxic and causes acid rain. [9] Hence the need for sensitive, but more importantly selective sensors to monitor the levels of NO2 in breathable air. It is also desired that these sensors be able to provide fast response at room temperature. Exhaled breath testing has been known to be a quick, safe and non-intrusive approach to early detection of declining health due to the presence of biomarkers corresponding to underlying diseases in them. Volatile Organic Compounds (VOCs) and ammonia are some of these markers.[10] Ammonia is passed out in urine and the concentrations of ammonia in exhaled breath only increases as health declines and reaches 1ppm in the event of kidney failure[11] Chemiresistors are good candidates for these sensitive and selective sensors and research is ongoing to develop new material composites with the desired properties for this purpose. In this work we present chemiresistors based on two new material composites with potential to for applications in ambient air quality monitoring and breath analysis at room temperature. These composites can be printed or drop-casted on interdigitated electrodes on flexible substrates such as Polyimide (PI) and Polyethylene (PET). The NO2 sensor, with Graphene-Carboxymethylcellulose (CMC) sodium salt active material composite shows sensitivity and selectivity to NO2 at room temperature while the NH3 sensor, based on a Polyaniline - Zinc Oxide composite shows sensitivity at room temperature