Carbon nitride thin films as all-in-one technology for photocatalysis

Organic π-conjugated polymers are promising heterogeneous photocatalysts that involve photoredox or energy transfer processes. In such settings, the materials are usually applied in the form of dis..

Machine learning to refine decision making within a syndromic surveillance service

Background: Worldwide, syndromic surveillance is increasingly used for improved and timely situational awareness and early identification of public health threats. Syndromic data streams are fed into detection algorithms, which produce statistical alarms highlighting potential activity of public health importance. All alarms must be assessed to confirm whether they are of public health importance. In England, approximately 100 alarms are generated daily and, although their analysis is formalised through a risk assessment process, the process requires notable time, training, and maintenance of an expertise base to determine which alarms are of public health importance. The process is made more complicated by the observation that only 0.1% of statistical alarms are deemed to be of public health importance. Therefore, the aims of this study were to evaluate machine learning as a tool for computer-assisted human decision-making when assessing statistical alarms. Methods: A record of the risk assessment process was obtained from Public Health England for all 67505 statistical alarms between August 2013 and October 2015. This record contained information on the characteristics of the alarm (e.g. size, location). We used three Bayesian classifiers- naïve Bayes, tree-augmented naïve Bayes and Multinets - to examine the risk assessment record in England with respect to the final ‘Decision’ outcome made by an epidemiologist of ‘Alert’, ‘Monitor’ or ‘No-action’. Two further classifications based upon tree-augmented naïve Bayes and Multinets were implemented to account for the predominance of ‘No-action’ outcomes. Results: The attributes of each individual risk assessment were linked to the final decision made by an epidemiologist, providing confidence in the current process. The naïve Bayesian classifier performed best, correctly classifying 51.5% of ‘Alert’ outcomes. If the ‘Alert’ and ‘Monitor’ actions are combined then performance increases to 82.6% correctly classified. We demonstrate how a decision support system based upon a naïve Bayes classifier could be operationalised within an operational syndromic surveillance system. Conclusions: Within syndromic surveillance systems, machine learning techniques have the potential to make risk assessment following statistical alarms more automated, robust, and rigorous. However, our results also highlight the importance of specialist human input to the process

Role of microstructure in the electron–hole interaction of hybrid lead halide perovskites

Organic–inorganic metal halide perovskites have demonstrated high power conversion efficiencies in solar cells and promising performance in a wide range of optoelectronic devices. The existence and stability of bound electron–hole pairs in these materials and their role in the operation of devices with different architectures remains a controversial issue. Here we demonstrate, through a combination of optical spectroscopy and multiscale modelling as a function of the degree of polycrystallinity and temperature, that the electron–hole interaction is sensitive to the microstructure of the material. The long-range order is disrupted by polycrystalline disorder and the variations in electrostatic potential found for smaller crystals suppress exciton formation, while larger crystals of the same composition demonstrate an unambiguous excitonic state. We conclude that fabrication procedures and morphology strongly influence perovskite behaviour, with both free carrier and excitonic regimes possible, with strong implications for optoelectronic devices

Growth and mass wasting of volcanic centers in the northern South Sandwich arc, South Atlantic, revealed by new multibeam mapping

New multibeam (swath) bathymetric sonar data acquired using an EM120 system on the RRS James Clark Ross, supplemented by sub-bottom profiling, reveals the underwater morphology of a not, vert, similar 12,000 km2 area in the northern part of the mainly submarine South Sandwich volcanic arc. The new data extend between 55° 45′S and 57° 20′S and include Protector Shoal and the areas around Zavodovski, Visokoi and the Candlemas islands groups. Each of these areas is a discrete volcanic center. The entirely submarine Protector Shoal area, close to the northern limit of the arc, forms a 55 km long east–west-trending seamount chain that is at least partly of silicic composition. The seamounts are comparable to small subaerial stratovolcanoes in size, with volumes up to 83 km3, indicating that they are the product of multiple eruptions over extended periods. Zavodovski, Visokoi and the Candlemas island group are the summits of three 3–3.5 km high volcanic edifices. The bathymetric data show evidence for relationships between constructional volcanic features, including migrating volcanic centers, structurally controlled constructional ridges, satellite lava flows and domes, and mass wasting of the edifices. Mass wasting takes place mainly by strong erosion at sea level, and dispersal of this material along chutes, probably as turbidity currents and other mass flows that deposit in extensive sediment wave fields. Large scale mass wasting structures include movement of unconsolidated debris in slides, slumps and debris avalanches. Volcanism is migrating westward relative to the underlying plate and major volcanoes are asymmetrical, being steep with abundant recent volcanism on their western flanks, and gently sloping with extinct, eroded volcanic sequences to their east. This is consistent with the calculated rate of subduction erosion of the fore-arc