Development and experimental evaluation of a complete solar thermophotovoltaic system

We present a practical implementation of a solar thermophotovoltaic (TPV) system. The system presented in this paper comprises a sunlight concentrator system, a cylindrical cup-shaped absorber/emitter (made of tungsten coated with HfO2), and an hexagonal-shaped water-cooled TPV generator comprising 24 germanium TPV cells, which is surrounding the cylindrical absorber/emitter. This paper focuses on the development of shingled TPV cell arrays, the characterization of the sunlight concentrator system, the estimation of the temperature achieved by the cylindrical emitters operated under concentrated sunlight, and the evaluation of the full system performance under real outdoor irradiance conditions. From the system characterization, we have measured short-circuit current densities up to 0.95 A/cm2, electric power densities of 67 mW/cm2, and a global conversion efficiency of about 0.8%. To our knowledge, this is the first overall solar-to-electricity efficiency reported for a complete solar thermophotovoltaic system. The very low efficiency is mainly due to the overheating of the cells (up to 120 °C) and to the high optical concentrator losses, which prevent the achievement of the optimum emitter temperature. The loss analysis shows that by improving both aspects, efficiencies above 5% could be achievable in the very short term and efficiencies above 10% could be achieved with further improvements

Entomological and epidemiological datasets used to estimate the probability of <i>T. cruzi</i> transmission from vector to human (<i>T</i>) per contact with infected vector.

<p>Data are presented as mean ± SE (when available in the original study). Entomological data were combined to estimate the number of potentially infectious contact per person per year (PIC).</p><p>^M1 and ^M2 refer to the triatomine collection methods and the corresponding correction factors defined in the main text.</p>a<p>Vector densities were further corrected for seasonality using monthly variations in infestation of <i>T. infestans</i><a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002505#pntd.0002505-Gorla1" target="_blank">[32]</a> or</p>b<p>for the seasonal infestation pattern of <i>T. dimidiata</i><a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002505#pntd.0002505-Barbu1" target="_blank">[21]</a>.</p>c<p>The biting rate was estimated and corrected for seasonal variations according to <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002505#pntd.0002505-Catala1" target="_blank">[33]</a>. Incidence was either measured directly or derived from prevalence using data on children or on all age-categories at the house or village scale.</p>d<p>children <15 years old,</p>e<p>incidence after 2 years of exposure at the household scale,</p>f<p>incidence after 3 years of exposure at the village scale.</p

This figure illustrates how aggregation varies with either host-parasite encounters or parasite success (in infecting hosts).

<p>In Panel A we have adopted the values Var(<i>S</i>)/<i>E</i>[<i>S</i>] = 1 and Var(ℰ)<i>E</i>[<i>S</i>] = 1. We show how parasite aggregation varies with the mean number of encounters. The level of aggregation decreases with the number of encounters, and asymptotically approaches a value that depends only on the variance-to-mean ratio of parasite success, i.e., Var(<i>S</i>)/<i>E</i>[<i>S</i>]. In Panel B we have adopted the values Var(<i>ℰ</i>)/<i>E</i>[<i>ℰ</i>] = 1 and Var(<i>S</i>) = 0.5. We show how parasite aggregation varies with the mean parasite success in infecting hosts. The level of aggregation initially decreases with the average success of parasites in infecting their hosts until a minimum is reached at a value of <i>E</i>[<i>S</i>] of </p><p></p><p><mi>M</mi><mo>=</mo></p><p></p><p>Var<mo stretchy="false">(</mo><mi>S</mi><mo stretchy="false">)</mo><mi>E</mi><mo stretchy="false">[</mo><mo>ℰ</mo><mo stretchy="false">]</mo><mo>/</mo>Var<mo stretchy="false">(</mo><mo>ℰ</mo><mo stretchy="false">)</mo></p><p></p><p></p><p></p>, as indicated on the abscissa. The level of aggregation then starts to increase, with an asymptotically achieved slope that is directly proportional to the variance-to-mean-ratio of encounters, i.e., Var(ℰ)/<i>E</i>[ℰ].<p></p

Entomological datasets used to predict human prevalence.

<p>Basic entomological data (vector density and infection rate) and the estimate of the probability of transmission were used to predict human prevalence. Vector densities (average per house) were corrected according to the collection methods used (M1, M2 and M3) as defined in the main text. Density and infection rates were available for domestic (D) or peridomestic (P) habitats. Mean infection rates are given for the villages. The biting rate was set up to 0.2, i.e. the average rate estimated by <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002505#pntd.0002505-Catala1" target="_blank">[33]</a>.</p>a<p>The proportion of blood-meals on humans was selected according to the habitat of the bugs. For <i>T. infestans</i>, this proportion was set to 0.4 in the domestic habitat <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002505#pntd.0002505-Rabinovich1" target="_blank">[11]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002505#pntd.0002505-Catal1" target="_blank">[24]</a> and to 0.01 in the peridomicile <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002505#pntd.0002505-Bosseno1" target="_blank">[27]</a>. For other species, this proportion was set to 0.26 <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002505#pntd.0002505-GuzmanTapia2" target="_blank">[46]</a> and 0.01 in the domestic and peridomestic habitat, respectively. SZ, NZ: Respectively south and North zone of Cochabamba, Bolivia <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002505#pntd.0002505-MedranoMercado1" target="_blank">[45]</a>.</p

Maximum likelihood estimate of the probability of transmission of <i>T. cruzi</i>.

<p>(A) profile likelihood, maximum likelihood estimate (MLE) of the probability of transmission <i>T</i>, and its 95% maximum likelihood confidence interval (MLCI). (B) Distribution of the MLE of <i>T</i> obtained from the sensitivity analyses (1000 replications). Grey and black horizontal bars on the top of the figure represent the 95% MLCI (with the grey dot corresponding to the MLE) and the interval including 95% of the MLE estimates obtained from the sensitivity analysis.</p

Sensitivity analyses of the probability of transmission of <i>T. cruzi</i>.

<p>Each panel gives the distribution of point estimates of T obtained from the sensitivity analyses (1000 replications). Panels A, B and C correspond to datasets 2, 3 and 4, respectively, while panels D, E and F correspond to each of the three villages included in dataset 5. Black bars represent the interval including 95% of the point estimates obtained from the sensitivity analysis. The grey dots and bars represent the maximum likelihood estimate (MLE) and 95% maximum likelihood confidence interval (MLCI) obtained from the dataset 1 for comparison (see <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002505#pntd-0002505-g001" target="_blank">Figure 1</a>).</p

Sensitivity of the basic reproduction number (R₀) to vector’s demography and feeding rates, and to pathogen’s transmissibility and virulence.

<p>All six vector-borne diseases appear on the same graph. Squares correspond to diseases with only human hosts: human African trypanosomiasis (HAT), dengue (DEN) and malaria (MAL). Circles correspond to diseases with non-human hosts: Chagas disease (CD), Japanese encephalitis (JE), and visceral leishmaniasis (VL). Larger symbols correspond to the key determinants of the variations of R₀ (see main text for comments). Sensitivities were calculated from 10,000 simulations for each disease.</p

Distribution of the prevalence of infected and recovered humans when some immigrant vectors are infectious ().

<p>Black and grey bars give the prevalence of infectious () and recovered () humans, respectively. Numbers above bars give (if any) the percentage of simulations leading to prevalence larger than 5%. Distributions were obtained from 10,000 simulations for each disease.</p

Distribution of the prevalence of infectious and recovered humans when no immigrant vector is infectious ().

<p>Black and grey bars give the prevalence of infectious () and recovered () humans, respectively. Numbers above bars give (if any) the percentage of simulations leading to prevalence larger than 5%. Distributions were obtained from 10,000 simulations for each disease.</p

Distribution of the pathogen’s basic reproduction number (R₀) for each of the six vector-borne diseases considered.

<p>(<b>A</b>) Diseases with only human hosts: human African trypanosomiasis (HAT), dengue (DEN) and malaria (MAL). (<b>B</b>) Diseases with non-human hosts: Chagas disease (CD), Japanese encephalitis (JE), and visceral leishmaniasis (VL). Distributions were obtained from 10,000 simulations for each disease.</p