Modern cooling strategies for ultra-deep hydropower mines

The increasing mining depths of South African gold mine deposits are resulting in ever-increasing heat loads associated with depth and the geothermal gradient. Together with changes in the mining horizons brought about by the depletion of older, shallower, highgrade reserves, this leads to the need for the continuous review and redesign of cooling requirements. Traditionally, cooling requirements were met by using a combination of cooling strategies, including bulk air cooling on surface and underground. If this proved to be insufficient, chilled service water and secondary remote air-cooling systems were introduced. This paper reviews these practices in order to provide a cost-effective means of catering for the introduction of hydropower at the Gold Fields Ltd South African operations. Some of the equipment that has been developed to meet the requirements of both hydropower and refrigeration includes hydropower fans, cooling coils and in-stope venturis. These are individually described and discussed, together with their roles within the greater strategy. The planned change in the cooling strategy and the employment of these technologies have effectively doubled the cooling available, from 10 MW to more than 20 MW, extracted from the hydropower water used to drive the mining equipment. In conclusion, the cooling strategy described allows a total heat load of approximately 52 MW to be successfully ventilated and cooled through the use of combined surface and underground refrigeration installations, and through the use of hydropower-chilled water.http://www.mvssa.co.zaam2017Mining Engineerin

In situ oligonucleotide synthesis on poly(dimethylsiloxane): a flexible substrate for microarray fabrication

In this paper, we demonstrate in situ synthesis of oligonucleotide probes on poly(dimethylsiloxane) (PDMS) microchannels through use of conventional phosphoramidite chemistry. PDMS polymer was moulded into a series of microchannels using standard soft lithography (micro-moulding), with dimensions <100 μm. The surface of the PDMS was derivatized by exposure to ultraviolet/ozone followed by vapour phase deposition of glycidoxypropyltrimethoxysilane and reaction with poly(ethylene glycol) spacer, resulting in a reactive surface for oligonucleotide coupling. High, reproducible yields were achieved for both 6mer and 21mer probes as assessed by hybridization to fluorescent oligonucleotides. Oligonucleotide surface density was comparable with that obtained on glass substrates. These results suggest PDMS as a stable and flexible alternative to glass as a suitable substrate in the fabrication and synthesis of DNA microarrays

Predicting Maximum Tree Heights and Other Traits from Allometric Scaling and Resource Limitations

Terrestrial vegetation plays a central role in regulating the carbon and water cycles, and adjusting planetary albedo. As such, a clear understanding and accurate characterization of vegetation dynamics is critical to understanding and modeling the broader climate system. Maximum tree height is an important feature of forest vegetation because it is directly related to the overall scale of many ecological and environmental quantities and is an important indicator for understanding several properties of plant communities, including total standing biomass and resource use. We present a model that predicts local maximal tree height across the entire continental United States, in good agreement with data. The model combines scaling laws, which encode the average, base-line behavior of many tree characteristics, with energy budgets constrained by local resource limitations, such as precipitation, temperature and solar radiation. In addition to predicting maximum tree height in an environment, our framework can be extended to predict how other tree traits, such as stomatal density, depend on these resource constraints. Furthermore, it offers predictions for the relationship between height and whole canopy albedo, which is important for understanding the Earth's radiative budget, a critical component of the climate system. Because our model focuses on dominant features, which are represented by a small set of mechanisms, it can be easily integrated into more complicated ecological or climate models.National Science Foundation (U.S.) (Research Experience for Undergraduates stipend)Gordon and Betty Moore FoundationNational Science Foundation (U.S.) (Graduate Research Fellowship Program)Massachusetts Institute of Technology. Presidential FellowshipEugene V. and Clare Thaw Charitable TrustEngineering and Physical Sciences Research CouncilNational Science Foundation (U.S.) (PHY0202180)Colorado College (Venture Grant Program

Modelling climate change responses in tropical forests: similar productivity estimates across five models, but different mechanisms and responses

Accurately predicting the response of Amazonia to climate change is important for predicting climate change across the globe. Changes in multiple climatic factors simultaneously result in complex non-linear ecosystem responses, which are difficult to predict using vegetation models. Using leaf- and canopy-scale observations, this study evaluated the capability of five vegetation models (Community Land Model version 3.5 coupled to the Dynamic Global Vegetation model – CLM3.5–DGVM; Ecosystem Demography model version 2 – ED2; the Joint UK Land Environment Simulator version 2.1 – JULES; Simple Biosphere model version 3 – SiB3; and the soil–plant–atmosphere model – SPA) to simulate the responses of leaf- and canopy-scale productivity to changes in temperature and drought in an Amazonian forest. The models did not agree as to whether gross primary productivity (GPP) was more sensitive to changes in temperature or precipitation, but all the models were consistent with the prediction that GPP would be higher if tropical forests were 5 °C cooler than current ambient temperatures. There was greater model–data consistency in the response of net ecosystem exchange (NEE) to changes in temperature than in the response to temperature by net photosynthesis (An), stomatal conductance (gs) and leaf area index (LAI). Modelled canopy-scale fluxes are calculated by scaling leaf-scale fluxes using LAI. At the leaf-scale, the models did not agree on the temperature or magnitude of the optimum points of An, Vcmax or gs, and model variation in these parameters was compensated for by variations in the absolute magnitude of simulated LAI and how it altered with temperature. Across the models, there was, however, consistency in two leaf-scale responses: (1) change in An with temperature was more closely linked to stomatal behaviour than biochemical processes; and (2) intrinsic water use efficiency (IWUE) increased with temperature, especially when combined with drought. These results suggest that even up to fairly extreme temperature increases from ambient levels (+6 °C), simulated photosynthesis becomes increasingly sensitive to gs and remains less sensitive to biochemical changes. To improve the reliability of simulations of the response of Amazonian rainforest to climate change, the mechanistic underpinnings of vegetation models need to be validated at both leaf- and canopy-scales to improve accuracy and consistency in the quantification of processes within and across an ecosystem.This research was enabled by a grant from the Andes–Amazon Initiative of The Gordon and Betty Moore Foundation. L. Rowland gratefully acknowledges financial support from the Natural Environment Research Council (UK) for a NERC PhD studentship, and NERC grant NE/J011002/1; PM also acknowledges support from ARC FT110100457

Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation

Data-model integration plays a critical role in assessing and improving our capacity to predict ecosystem dynamics. Similarly, the ability to attach quantitative statements of uncertainty around model forecasts is crucial for model assessment and interpretation and for setting field research priorities. Bayesian methods provide a rigorous data assimilation framework for these applications, especially for problems with multiple data constraints. However, the Markov chain Monte Carlo (MCMC) techniques underlying most Bayesian calibration can be prohibitive for computationally demanding models and large datasets. We employ an alternative method, Bayesian model emulation of sufficient statistics, that can approximate the full joint posterior density, is more amenable to parallelization, and provides an estimate of parameter sensitivity. Analysis involved informative priors constructed from a meta-analysis of the primary literature and specification of both model and data uncertainties, and it introduced novel approaches to autocorrelation corrections on multiple data streams and emulating the sufficient statistics surface. We report the integration of this method within an ecological workflow management software, Predictive Ecosystem Analyzer (PEcAn), and its application and validation with two process-based terrestrial ecosystem models: SIPNET and ED2. In a test against a synthetic dataset, the emulator was able to retrieve the true parameter values. A comparison of the emulator approach to standard brute-force MCMC involving multiple data constraints showed that the emulator method was able to constrain the faster and simpler SIPNET model's parameters with comparable performance to the brute-force approach but reduced computation time by more than 2 orders of magnitude. The emulator was then applied to calibration of the ED2 model, whose complexity precludes standard (brute-force) Bayesian data assimilation techniques. Both models are constrained after assimilation of the observational data with the emulator method, reducing the uncertainty around their predictions. Performance metrics showed increased agreement between model predictions and data. Our study furthers efforts toward reducing model uncertainties, showing that the emulator method makes it possible to efficiently calibrate complex models.</p

Exploring High Aspect Ratio Gold Nanotubes as Cytosolic Agents: Structural Engineering and Uptake into Mesothelioma Cells.

The generation of effective and safe nanoagents for biological applications requires their physicochemical characteristics to be tunable, and their cellular interactions to be well characterized. Here, the controlled synthesis is developed for preparing high-aspect ratio gold nanotubes (AuNTs) with tailorable wall thickness, microstructure, composition, and optical characteristics. The modulation of optical properties generates AuNTs with strong near infrared absorption. Surface modification enhances dispersibility of AuNTs in aqueous media and results in low cytotoxicity. The uptake and trafficking of these AuNTs by primary mesothelioma cells demonstrate their accumulation in a perinuclear distribution where they are confined initially in membrane-bound vesicles from which they ultimately escape to the cytosol. This represents the first study of the cellular interactions of high-aspect ratio 1D metal nanomaterials and will facilitate the rational design of plasmonic nanoconstructs as cytosolic nanoagents for potential diagnosis and therapeutic applications.BLF-Papworth Fellowship from the British Lung Foundation and the Victor Dahdaleh Foundation

Monitoring plant functional diversity from space

The world’s ecosystems are losing biodiversity fast. A satellite mission designed to track changes in plant functional diversity around the globe could deepen our understanding of the pace and consequences of this change and how to manage it