The variable used for the quality of the fish and vegetable components was a previously unprocessed/fresh component being compared to a processed. This study showed that in a modular dish, each component had an effect on the perceived quality of the dish. When replacing a preprocessed component with a fresh one, the perceived pleasantness increased to a higher level. The fish as the main dish component had the largest effect on the quality. Fresh fish has the ability to enhance the taste of soup, even with frozen vegetables. The results from this study indicate that the effect of freshness can also be perceived in the cooked product.The foodservice industry is a highly competitive branch where customer satisfaction and loyalty is dependent on the price and the quality of the food. To improve cost competitiveness, instead of fresh ingredients, more preprocessed items are used as components in dishes. This may impair the perceived product quality, and thus potentially decrease customer satisfaction. The effects of the component quality on a single dish were tested by serving fish soup in a consumer study ( In an iThe objective in this study was to investigate whether a single food component and different component combinations would induce an effect on the sensory characteristics and consumer quality of a dish. The variable used for the component quality was a previously unprocessed component being compared to a processed component. The selected dish was fish soup with varying fish and vegetable mix components. For the test purposes, the component variable referred to in the study was the level of freshness. A secondary aim was to study whether the modular design could be applied to meal solutions and the dish quality increased without significantly increasing the raw material or labor costs. The effects of different components on the quality of the fish soup were tested with both a modified check‐all‐that‐apply method and a consumer study. To the authors’ knowledge, no study similar to this has been carried out with an actual commercial product.22.1Oncorhynchus mykiss), both fresh and frozen. The frozen fish was a previously processed ready‐to‐use product cut into cubes. The fresh fish in this case was a previously unprocessed product, which was kept in ice, but was not frozen at any time during the supply chain. The fresh fish was delivered the same day it was used as a fillet, and manually cut into bite‐sized cubes. The vegetable mix component was also either fresh or nonfresh. The fresh vegetable components were delivered as complete, previously unprocessed vegetables and they were prepared  just before cooking. The nonfresh components were frozen, minimally processed , ready‐to‐use products. The quality of the potato and stock components was not varied between the different samples. The soup was made by cooking the potato and vegetable mix components in the stock. When the other components were cooked, the fish was added immediately before serving in order to minimize the holding time. All the test samples were prepared by experienced kitchen professionals. The kitchen personnel were aware that the study was conducted during the test days and that the raw material mix was modified, but the overall purpose of the study was not disclosed. To reduce the quality variance, the company uses the same standard recipe with exact amounts of each raw material by weight in order to minimize any personnel induced variance.Altogether, four different fish soup samples were tested Table . The fis2.2n = 8, females) participating in the session had more than 10 years’ experience in quantitative sensory profiling and descriptive methods. The samples used during the session were various commercially available fish soups from a Finnish food store. The samples were served cooked and warm. The purpose of the first step was to create a list of attributes of the different kinds of sensory properties of the fish soup samples, as regards flavor (including taste), smell, structure, and appearance. All the perceived properties were collected, listed, and discussed together before selecting the final list of descriptors/attributes. The attributes were grouped according to the sensory modality into four categories .The sensory study was carried out in two steps. In the first step, a 2‐hr session was conducted in the analytical sensory laboratory at the University of Turku . The sensory laboratory was designed according to the ISO 8589 standard. All the assessors  method was used  71 consumers, sample number 3  69 consumers and sample number 4  65 consumers. Of all the respondents, 67.3% were women and 32.2% men. One participant did not check the gender box in the questionnaire. The average age of the participants was 46 ± 12.2 years ranging from 22 to 87 years. The questionnaire was formulated in Finnish. In the consumer study, three soups of the four were used, these were numbers 2, 3, and 4 as depicted in Table The study was conducted in a foodservice restaurant that only served a buffet lunch from 10.30 a.m. to 2 p.m., and typically had 300 customers daily. The consumers were contacted at the entrance to restaurants and asked to participate. The actual purpose of the study was not introduced. When asked, customers were informed that the purpose of the study was to collect user experiences of fish soup. The customers were asked to complete a questionnaire after eating. Customers paid the regular fee for the lunch serving, but a free dessert was promised as a reward after completing the form. The fish soup was a normal part of the lunch buffet offering, which consisted of three warm dishes and salad components. The customers participating in the study chose to eat the soup willingly. Only one sample was available at each point of time; this meant that each participant evaluated only one of the three samples. The restaurant served a buffet lunch from 10.30 a.m. to 2 p.m., and data were gathered during that time period. The data were gathered during three test days. During each test day, two different types of soups were served to reduce the effect of the day.The respondents were asked to evaluate their experience of pleasantness at the appearance, smell, taste, fish texture, and vegetable texture. A balanced 5‐point hedonic scale with descriptors was used for pleasantness . Customers also had the possibility to freely comment on the quality of the fish soup. After finishing their meal, customers returned the questionnaires to a separate collection point and for the completed form they received a free dessert.2.4In order to recognize the underlying dimensions behind the fish soup properties, a principal component analysis (PCA) was performed . For the PCA, the descriptors chosen by the in‐house panel were used. The PCA was carried out with Unscrambler X .p < .05. The effect sizes were calculated using Cohen's d.The differences between the hedonic score ratings for the fish soups were tested using an analysis of variance (ANOVA) using two a priori planned comparisons. The effect of a single component was tested between the sample with only frozen ingredients (4) and samples with either fresh fish (2) or vegetable component (3). All the tests were considered as significant for Statistical analysis was also performed to verify the effects of the demographic profiles. All statistical analyses were performed with IBM SPSS Statistics, version 22.33.1A categorical PCA was used to test the data from the check‐all‐that‐apply (CATA) method used with the sensory panel. Results from the categorical PCA for different soup qualities (odor (O), appearance (A), structure (S), and flavor (F)) are presented in Figure As can be seen from Figure x‐axis and are very neutral in comparison with the soups with frozen fish. These soups differ from each other significantly and are located at the opposite end of the axis. Soup with a fresh vegetable mix (3) received a strong positive loading on PC2, while soup with only frozen ingredients had strong negative loadings. Based on this, PC2 describes the freshness of the vegetable mix only when frozen fish is used. The fresh fish is a much more dominant factor as regard perceived sensory characteristics and therefore the freshness of the vegetables cannot be recognized in this case.The second principal component (PC2) explains 25% of the total variance. The interpretation of PC2 is slightly more demanding than PC1. Both soups with fresh fish (1 and 2) are located very close to the The CATA method was a valuable tool for differentiating the samples. The results show that different combinations of the components produce a variance in the sensory properties perceived by the sensory panel. As can be seen from the Figure The appearance of the soup with fresh components was described as uneven, bright, yellow, and smashed, and the structure, for example, smooth and tattered. The soup with only frozen ingredients, on the other hand, was described as colorless, green, and pasty, with the structure described as even and rubbery. Both the appearance and structure may have an impact on the perceived product properties in context of being consumed. For instance, a dish containing uncharacteristic colors Spence,  or textu3.2p‐values) with calculated effect sizes (Cohen's d) are presented in Table In the consumer study, the soups were perceived differently in a regular consumption context. The average hedonic scores for the fish soup samples and the results from planned comparisons , the texture of the fish received the highest rating and the difference was significant when compared to the soup made purely from frozen ingredients (4). The texture of the vegetables showed a similar type of phenomenon. The pleasantness of the vegetable texture is at the highest level with fresh vegetables, and the difference was significant when comparing to the sample 4. The term textural contrast is used when, for example, a dish contains a variety of different textures  can utilize modular product design effectively to facilitate product variety without additional cost  with significant differences expect in the case of smell. Replacing a preprocessed component with a fresh one increased the perceived pleasantness of the fish soup. The fish as the main dish component had the largest effect on the quality. The fresh vegetable component also had an effect on the quality, but the difference was not as great as with the fish component. The significance of the dish center appears to be more prominent than the vegetable or carbohydrate component. The results from this study indicate that the effect of freshness can also be perceived in the cooked product.None declared.


Correlated hopping directions are observed in ab initio simulation of proton diffusion indicating that the simple random walk model is not valid for the interpretation of experimental results. ab initio simulations that show this assumption is not generally valid. Specifically, we observe that there is an increased probability for the proton to revert back to its previous location. These correlations indicate that the interpretation of the experimental results need to be re-examined and suggest that the timescale of the Grotthuss mechanism is significantly shorter than was previously thought.The aqueous proton displays an anomalously large diffusion coefficient that is up to 7 times that of similarly sized cations. There is general consensus that the proton achieves its high diffusion through the Grotthuss mechanism, whereby protons hop from one molecule to the next. A main assumption concerning the extraction of the timescale of the Grotthuss mechanism from experimental results has been that, on average, there is an equal probability for the proton to hop to any of its neighboring water molecules. Herein, we present  Structural diffusion occurs through the Grotthuss mechanism and gives the proton its large diffusion coefficient. The Grotthuss mechanism consists of shuttling protons through the hydrogen bond network of water. In between exchanges of the excess proton from one water molecule to the next, the total diffusion is supplemented by vehicular diffusion, which refers to the center-of-mass motion of the cation. A multitude of work has gone into understanding the details of the Grotthuss mechanism.While the understanding of the details of the Grotthuss mechanism has evolved over time, one aspect that has remained essentially constant is the reported timescale of the Grotthuss mechanism. Since the pioneering nuclear magnetic resonance study of Meiboom,via concerted hops along water wires.15This is not to say that all studies of the Grotthuss mechanism have relied on the assumptions behind a simple random walk in performing analyses, indeed many have not.Of special note is a study by Halle and Karlström.ab initio molecular dynamics simulations to address whether the simple random walk model is generally valid for the Grotthuss mechanism. In doing so, we have also provided one of the most statistically robust, ab initio determinations of the proton diffusion coefficient to date. Our simulations clearly show correlations between proton hopping directions, suggesting that the simple random walk picture is not universally valid for the Grotthuss mechanism. Consequently, the interpretation of the experimental results for the timescale of the Grotthuss mechanism should be re-examined, with our results suggesting a substantially faster hopping time.In the present work, we have performed 2For our molecular dynamics simulations our system consisted of 31 water molecules and one hydrochloric acid (HCl) molecule in a cubic box with an edge length of 9.87 Å. All calculations were performed with Quantum Espresso v5.4 using the CP module.me in order to keep the propagation of the system adiabatic. Data were sampled every 10 time steps, and the first picosecond of each 8 ps trajectory was discarded for equilibration. We ran 500 independent trajectories at each temperature for a total simulation time of 8 ns. The initial configurations for each trajectory were sampled from a separate molecular dynamics simulation run with the same simulation parameters.A Nose–Hoover chain with 4 thermostats and characteristic frequency 140 THz was used to simulate a canonical (NVT) ensemble with a target temperature of either 300 or 440 K. For our Car–Parrinello molecular dynamics,In order to calculate a proton diffusion coefficient, we have to define the positive charge at each point along the trajectory. There is no unique way to define molecules from a collection of atoms, and this task is even more fraught with peril for an excess charge in water.3O+) as the oxygen atom closest to three hydrogen atoms in each frame of the trajectory. Whether the positive charge is identified as the hydronium ion itself or the hydronium ion is used as a stand-in for the larger Eigen cation (H9O4+) is often inconsequential depending on the analysis. This definition of the positive charge is susceptible to the aforementioned vibrational dynamics causing an excess of proton hops. Previous attempts to overcome this have been to simply ignore any hop that is undone by the next hop, i.e. if two successive hops result in the proton being in the same location as it was initially, those hops are ignored.The most common approach has been to identify a hydronium ion  – R0|2〉 is the mean squared displacement at time is the mean squared displacement at time t. The resulting diffusion coefficients are collected in From our molecular dynamics simulations, we calculated the mean squared displacement of the proton as a function of time. The use of hundreds of independent trajectories allows us to ensure sufficient sampling without having to resort to using overlapping trajectory segments, which bias the mean squared displacements.Pranami and Lamm previously showed that while linear regression can be used to obtain a point estimate of the diffusion coefficient from the mean squared displacements, the statistical uncertainty of the fitting parameters are not reflective of the uncertainty in the diffusion coefficient.2 ps–1 is close to the experimental, infinite-dilution diffusion coefficient of a proton in water at ambient conditions of 0.932 Å2 ps–1.2 ps–1).At 300 K, our calculated proton diffusion coefficient of 1.015 ÅIn order to obtain results closer to ambient conditions, we ran a second set of simulations at 440 K, which was previously suggested as a temperature at which the PBE functional gives better liquid water properties.2 ps–1. Again, since we are dealing with a relatively concentrated system, the corresponding experimental diffusion coefficient would still be expected to be smaller than the limiting value of 0.932 Å2 ps–1, by approximately a factor 1.5.As would be expected with the increase in temperature, the calculated proton diffusion coefficient is significantly larger, 3.004 ÅPrevious work has indicated that DFT methods underestimate the proton transfer barrier relative to wave function methods such as MP2 and CCSD(T).–1 despite the proton transfer barrier being much smaller.Indeed, the PBE functional gives very small barriers for the proton to transfer from one oxygen atom to another see ESI. It is p2 ps–1], yet at 440 K the vehicular component is smaller than the calculated water diffusion coefficient [0.562 (SE = 0.007) Å2 ps–1]. The most likely explanation is that at 300 K when the water is over-structured, the water molecules are hindered in their diffusion. At the same time, the “special pair dance” of the proton causes an elevated diffusion as the central hydronium ion rattles around within its first solvation shell.The calculated vehicular components to the proton diffusion coefficients are interesting in that at 300 K the vehicular diffusivity is larger than our calculated water diffusion coefficient [0.044 (SE = 0.001) Åi.e. structural plus vehicular equals total. Our current results suggest that this is not the case. While the differences between the sums of the components and the totals are small (0.092 at 300 K and 0.199 at 440 K), our bootstrapping analysis indicates that these differences are statistically significant at the 95% confidence level. This type of correlation between the components of the diffusion process has been suggested before based on physical arguments surrounding the polarization resulting from the hopping of the charge from one site to another.et al. in their the self-consistent iterative multistate empirical valence bond simulations of HCl solutions.51A common assumption concerning the proton diffusion coefficient has been that the structural and vehicular components are independent, While correlation between the components of the diffusion process is noteworthy, it does not have any bearing on the validity of the simple random walk assumption for interpretation of the experimental results. For the simple random walk picture to be valid for the Grotthuss mechanism, the probability for the proton to hop to any of its three neighbors should be equal and not depend on the proton's history. In i.e. a mean-squared displacement that grows linearly with time), and the calculated distribution of hopping probabilities is approximately normal, suggesting that there are not two distinct populations from which the dynamics emerge.As with previous studies, we observed multiple partner exchanges as part of the “special pair dance” before the majority of the proton hopping events.R2〉c represents the mean squared displacement of the correlated walk, represents the mean squared displacement of the correlated walk, 〈ΔΔR2〉s represents the mean squared displacement of the simple walk, q is the probability to reverse the previous hop, and p is the probability to hop to one of the other sites. Assuming an average hop length ε and hopping time τ, we can relate the diffusion coefficient to these quantities using the general random walk modelThe implications of needing to go beyond the simple random walk model for interpreting the experimental results can be substantial. The details of the relationship between a simple random walk and the corresponding correlated random walk are known.p + q = 1.For the hydronium ion with three neighbors, we have that 2q = 0.652 at 300 K and q = 0.587 at 440 K, both significantly different than the 1/3 expected for a simple random walk. From eqn (3), using the equilibrium distance between the hydronium ion and a water molecule of 2.5 Å (see 2 ps–1, we get a hopping timescale of between 0.460 and 0.665 ps, depending on the assumed vehicular component (sodium ion, 0.133 Å2 ps–1; water molecule, 0.230 Å2 ps–1 8As given in .5 Å see , along wet al. did observe that the proton hopping rates decreased much more slowly than the diffusion coefficient with increases in concentration in their multistate empirical valence bond simulations.Given that our simulations were performed at an HCl concentration of ∼1.7 M, it is reasonable to wonder whether the correlations we observe can be applied to the infinite-dilution case. While a more complete answer to that question would require additional investigations, we note that the concentration of our system sits right on the edge of where changes begin to occur in the experimental vibrational spectra.et al. concluded that the dynamics measured in the spectroscopy experiments correspond to changes in the proton solvation asymmetry rather than directly relating to the proton transfer.However, we note that spectroscopic studies aiming to gain insight into aqueous proton dynamics have been done at even higher concentrations than we have studied here,4ab initio molecular dynamics simulations, we have found significant correlation between hopping directions in the Grotthuss mechanism of aqueous proton diffusion. Specifically, we found an elevated probability for the proton to return to its previous site compared to what would be expected for a simple random walk. These results suggest that the interpretation of the experimental results for proton diffusion needs to be re-examined. Until now, the experimental results have generally been interpreted in terms of a simple random walk, resulting in a timescale of approximately 1.5 ps for the Grotthuss mechanism. However, re-interpreting those results in terms of the correlated random walk suggested by our simulations, results in the timescale being closer to 0.5 ps. Furthermore, our results also provide evidence of correlation between the components of the diffusion coefficient. This could mean that the timescale of the Grotthuss mechanism is even faster since we found a negative correlation, meaning that the individual components add to more than the total. While we have found that the correlations between the components of the diffusion and the hopping directions are robust to temperature and chosen functional, further work is needed to assess the dependence of these correlations on concentration.Through large sets of There are no conflicts to declare.Supplementary informationClick here for additional data file.


The accumulation of soiling on photovoltaic (PV) modules affects PV systems worldwide. Soiling consists of mineral dust, soot particles, aerosols, pollen, fungi and/or other contaminants that deposit on the surface of PV modules. Soiling absorbs, scatters, and reflects a fraction of the incoming sunlight, reducing the intensity that reaches the active part of the solar cell. Here, we report on the comparison of naturally accumulated soiling on coupons of PV glass soiled at seven locations worldwide. The spectral hemispherical transmittance was measured. It was found that natural soiling disproportionately impacts the blue and ultraviolet (UV) portions of the spectrum compared to the visible and infrared (IR). Also, the general shape of the transmittance spectra was similar at all the studied sites and could adequately be described by a modified form of the Ångström turbidity equation. In addition, the distribution of particles sizes was found to follow the IEST-STD-CC 1246E cleanliness standard. The fractional coverage of the glass surface by particles could be determined directly or indirectly and, as expected, has a linear correlation with the transmittance. It thus becomes feasible to estimate the optical consequences of the soiling of PV modules from the particle size distribution and the cleanliness value. Power reductions greater than 50% have been reported in the literature because of soiling4. Soiling losses are generally quantified by using soiling stations. These systems are made of at least two PV devices, one of which is regularly cleaned while the other is left to soil naturally. By comparing the ratio of the electrical outputs of the two devices, it is possible to estimate the impact of soiling on the PV performance6. The International Electrotechnical Commission’s (IEC) metric to monitor and quantify the impact of soiling on PV modules is the soiling ratio, rs, which expresses the ratio of the electrical output of a soiled PV device to the output of the same device under clean conditions7. Like the transmittance, a higher soiling ratio translates to less soiling deposited on the modules. A value of 1 indicates clean conditions, with no soiling. For a more detailed definition of rs, please refer to the Methodology section. The fractional loss of solar-generated power due to soiling is 1 − rs.A careful monitoring of soiling is required to mitigate its effect8. Novel sensors that require less maintenance and do not need a clean reference PV device are getting the attention of the market, and are based on the optical characterization of a soiled glass coupon10. Recently, a new procedure to estimate soiling losses using transmittance data has been validated through a one year study in the south of Spain11. Rather than using soiling stations or sensors, there are also methods to extract losses directly from PV performance data13.Soiling stations have the advantage of directly measuring the impact of soiling on PV, but require careful maintenance to avoid significant measurement errorsAll these methods directly measure the impact of soiling or estimate the losses of PV power based on the broadband transmittance of a soiled glass plate. On the other hand, they do not consider the optical properties, the composition, and the size distribution of particles deposited via soiling. These factors can have an impact on the adhesion of soiling to the PV module’s surface and need to be investigated if effective soiling mitigation strategies, such as cleaning methods or anti-soiling coatings, are going to be developed.15 investigated the optical, chemical and mechanical characteristics of dust particles collected on PV modules installed in the Middle East. Despite the importance of these studies toward the characterization of dust in high-soiling regions, they did not investigate the fundamental connections between the properties of the dust and the PV losses. Prior studies on the spectral characteristics of the optical transmittance and the particle size distribution of soiled glass coupons have been reported16, but not for natural soiling. In a separate study, a variety of artificial soil types were sprayed onto glass to study the resulting optical properties and PV panel spectral quantum efficiency17. In contrast to artificial soiling, other groups measured the transmittance loss versus the mass of deposits accumulated on glass plates soiled outdoors, also taking note of the angle of incidence of the light18. Borrowing from prior studies in atmospheric science, dry deposition rates on surfaces, aerosol optical properties, and Mie scattering theory, researchers have also developed a parameter to determine the change in PV panel transmittance for a given mass per unit area of atmospheric particulate matter (PM) deposited19. The experimental work utilized deposits collected from soiled PV panels in Gandhinagar, India, and took into consideration both particle absorption and scattering from a variety of particles, such as soot, salts, pollen, and mineral dust. Separate tests carried out outdoors in Mumbai, India, measured spectral reflectance, angle of incidence effects, and quantum efficiency for carefully modified full-sized PV modules20. All of these investigations of natural soiling gave important contributions to the field, but were conducted at only one location, and did not compare the optical properties of PV glass soiled at multiple sites with diverse climate conditions.Previous studies24. Identical low-iron glass coupons were soiled at each location over an 8-week period between January and March 201625. The samples were mounted horizontally . Because of the short data collection period, and of the single measurement taken only at the end of it, the soiling is not expected to be wholly representative of the sites. Correlating soiling with the specific climate conditions of a site is outside the scope of this work, which aims to investigate the spectral and optical characteristics of different types of soiling to attempt to find commonalities. The spectral transmittance and the particle size distribution (PSD) of the soiling was compared in order to find correlations that could be universally valid, and that could open possibilities to modelling PV soiling losses through the optical characterization of dust.This work presents the results of an international collaboration that investigated the spectral effects of soiling naturally deposited on PV glass installed at various locations worldwide. Outdoor tests have been conducted at seven locations worldwide: Golden , San José , Chennai (India), Jaén (Spain), El Shorouk City (Egypt), Tezpur (India), and Penryn (UK). These places were chosen to represent a wide variety of climates and environmental conditions (See the locations listed in Supplementary Information)The present work draws upon one of the largest pools of naturally deposited soiling samples. While most of the soiling studies focus on a single site, there are only a limited number of investigations conducted on soiling from multiple locations. Together with this, we present the first effort where empirical models from other disciplines are brought in to describe both the spectral and optical characteristics of the samples, together with an examination of their corresponding PSDs.θi, as measured from the surface normal. This light is reflected and transmitted in accordance with Fresnel’s equations. As a rule of thumb, 4% of the light is reflected at each air-glass interface at near-normal incidence. Here, the subscripts i, r, and t denote incident, refracted and transmitted light, respectively. Light that is transmitted at the air-glass interface is refracted via Snell’s law and can reach the interface at the back to be transmitted out of the glass . Less than 91% of the incident light exits the glass (at θi = θt) for uncoated, clean glass.Realizing the complexities of a commercial PV module from the optical standpoint, the system under study in the present report was simplified so that it just includes the soiling on a sheet of low-iron glass see Fig. . In thisrs is a function of the angle of the incoming light20. Indeed, this is why it is defined in the IEC standard for conditions found at noon7.Contamination and soiling shown on the right hand side of Fig. 3. In this study, we utilized bare glass without any coatings.Also shown in Fig. 11. Measurements were performed at two locations on each sample and the results were analyzed as described below. Table 26. It should be noted that the relative transmittance of soiling is not necessarily equivalent to a soiling ratio for a PV module. Indeed, the numerator of the soiling ratio (rs) for a PV module is the integral over wavelength of the product of the relative transmittance due to the soiling, the spectral response of the solar cell, SR(λ), and the incoming solar spectral irradiance11 . The denominator for rs is a similar integral, but omits the transmittance. The resulting predicted soiling ratios are also reported in Table 27 and summarized in the Methods section. For the majority of the sites, the three approaches yield very similar values.Spectral hemispherical transmittance was measured for each of the soiled glass coupon samples. As explained in the Methodology, all the transmittance values mentioned in this work are relative hemispherical transmittance, in that they are the ratio of the transmittance of the soiled coupon to the transmittance of a clean reference coupon. The soiled coupons represent a snapshot in time for a particular location and are affected by the specific weather events that occurred during the soiling periodrs, reported in Table 11.Fig. 28. The expected yellowing and degradation rates due to the outdoor UV exposure of polymer encapsulants and polymer back sheets in PV modules may therefore have to be adjusted if soiling is present.There are additional reasons that make the results of Fig. 29. This turbidity is due to optical scattering that is primarily described by Mie theory, averaged over the distribution of particle sizes and optical properties. According to the Ångström turbidity equation, the transmittance τ of a column of air at a wavelength λ can be empirically modeled as,α is an index relating to the size of the particles, β is a parameter representing the amount or concentration of aerosols, and m is the optical path length . The term β is typically expressed in units related to the number of particles per volume or by the mass of suspended material per volume30. It should be noted that the Ångström turbidity equation attempts to account for both scattering and absorption by the particles30. The value of α would be 4 for very small particles and 0 for very large particles. Distributions of particle sizes lead to intermediate values of α.The Ångström turbidity equation describes the attenuation of light by aerosols suspended in the atmosphereβsur, where the subscript “sur” denotes surface, which represents both the mass of particles per unit area on the glass surface, and the strength of forward scattering of those particles. Thus, we propose a reformulated version of the original Ångström turbidity equation,In order to consider the transmittance of aerosols and particles on the glass cover of a PV module, it is useful to modify the Ångström equation so that it is applicable to surfaces instead of volumes. We propose that its empirical approach can be modified to describe the wavelength dependence of the transmittance of light due to small particles adhering to the glass. These are some of the same particles that were originally suspended in the air. The product γ*. This is a wavelength independent component due to very large particles that cannot stay suspended in the air, and hence fall on the glass surface. The fully-modified equation becomes,In order to better fit our data, we also found it useful to introduce an additional correcting offset parameter, γ* term can also correct for a small amount systematic errors due to light that goes undetected by the measurement system. Equations . Plots of the fits to Eqs.  found at the various sites are such that γ* is paired with α* and β*sur in a way that leads to the linear relationship, Eq. , and the soiling ratio for a soiled PV module were also considered. Burton et al.17 used artificial soiling to explore soiling’s effect on a module’s quantum efficiency, which is directly related to SR(λ). That work established a strong correlation between the measurement of the spectral transmittance of glass coupons and quantum efficiency measurements for a module. The work of Piedra et al.33 considered the particle size distribution for the deposits and also utilized Mie scattering theory. Our experimental results strongly suggest that the general shape of the spectral curves found in each of these prior studies is also found under a diverse set of natural soiling conditions. In addition, we assert that the spectral transmittance  curves show a reasonable fit to an empirical model inspired by the Ångström turbidity equation, which also has at its roots Mie scattering theory.Qasam and co-workers have studied the spectral characteristics of glass coupons soiled outdoors in Kuwait34 . The image analysis attempted to identify each particle and its projected area, A, and the results were tabulated. The particle areas thus obtained were summed and the result was divided by the total analyzed area in the micrograph to yield the measured fractional area coverage, f.The optical properties of the soiled PV glass are expected to depend upon the size distribution of the deposited particles. That relationship, at first glance, may be challenging to find, given the range of sizes and shapes, their relative abundance and their differing chemical compositions. There are, however, a number of fundamental insights that can be gleaned from images obtained from a microscope. First, the open platform software ImageJ was used for image analysis of micrographs taken at 100× and 500× magnificationsD. The effective diameter for an equivalent round particle was calculated from D = (4A/π)1/2. Some typical particle size distribution densities for coupons soiled outdoors are shown in Fig. D (summed from D to ∞) is shown on the right y-axis. This red line yields the same value of f that was previously mentioned as it approaches the smallest diameter values. It is apparent from the plot that one should not neglect the smallest of particles, in part because they are quite numerous and their combined coverage is significant. The shape of the curve for the number of particles and that for the particle area coverage appear the same even though the former is logarithmic, while the area coverage uses a linear scale.The data was then further processed to estimate the particle size distribution density versus particle diameter, from D = A/π1/2. S35, IEST-STD-CC 1246E, to calculate the cleanliness level of each soiled coupon. The cumulative distribution of particle diameters is specified in this standard by a cleanliness level, L, with N(D) being the number of particles per unit area having a diameter between D and L (both in micrometers):Having obtained the particle size distribution density, we can then fit it to an existing model that may describe its shape or other properties. In the approach that follows, we exploited the framework described in the Institute of Environmental Sciences and Technology (IEST) Product Cleanliness Level standardL and D. For convenience, the cumulative distribution in the equation can also be expressed per unit square micrometer. The size distribution density for the particles is given by dN(D)/dD. Plotting log10N(D) versus (log10D)2 yields a straight line of slope 0.926 with a y-axis intercept that yields the cleanliness level, L. For a y-axis value on Fig. L is approximately 870 µm, while –2.5 gives 1070 µm.Because one cannot take the logarithm of a number with the units, Eq.  implicitL value lying within the range 600 to 1200 μm (Table R2 and root-mean-square error (RMSE) in each case to quantify the goodness of the fit. Table Figure μm Table . The lowR2) results than 500×, probably because the lower magnification covers 25 times as much area, yielding better statistics, and is less exposed to the effect of any non-uniformity of the soiling than the higher magnification, with the only exception for the coupon exposed in Tezpur, India  can be derived from the IEST-STD-CC 1246E distribution, yieldingMa, πD2/4 with respect to D. The 0.926 is the particle distribution slope given in Eq. . The equf values are collected in Table f can be estimated by using the fits to the particle size distribution to obtain a cleanliness level . It exhibited an irregular and mottled pattern of particle clustering, and even some branched features that resemble fungal hyphae previously reported in other PV glass soiling studiesAnother challenge concerned the sample from Egypt. It was found to be very non-uniform, and so a reliable particle count could not be extracted with ImageJ. It exhibited layers of sand particles on top of other layers. No other soiled glass coupon had such a complex morphology. There were quite harsh weather conditions during the soiling deposition, such as strong, sandy winds and a heavy rain. For future studies, therefore, the time period for the collection of soiling on glass coupons from places like Egypt should be shortened, perhaps to only one week, so that only a single layer is present.Overall, our results imply that the particle size distribution can, to first order, ignore the composition of the deposits. To our knowledge, this is the first time it is shown that the distribution described in IEST-STD-CC 1246 cleanliness standard is also valid for soiling deposited outdoors. That standard was introduced to describe the cleanliness of contamination-critical products, such as clean rooms and spacecraft.f should be closely related to the broadband hemispherical transmittance, τb, since, to first order, the deposited particles block or obscure the passage of light through the glass to the solar cell. This is supported by the results from other studies41. The results of Tables b represents the optical losses due to the presence of soiling.We can combine the results from the two types of optical instruments, the spectrophotometer and the microscope. It is expected that the measured Figure 36.Better results were found for 100×, than at 500× (not shown) for almost all the sites, perhaps again because 100× is less susceptible to non-uniformity over the larger area viewed by the microscope. One should note that the area probed using the spectrophotometer is on the order of a square centimeter, while that for the images taken at 100× is on the order of a square millimeter. One way to mitigate this problem is to stitch together a tiled mosaic of microscope images so that they represent a larger area, as has been done in recent studiesL. This relationship can find immediate application in monitoring PV soiling and estimating its effect on PV performance. Outdoor microscopes have already been employed to measure particle size and deposition rates related to soiling42, and could be used for the optical characterization of natural soiling of PV modules suggested by our work. The results augment and extend those of other studies made at lower soiling 44.The outlying data point in Fig. There are additional complexities for PV soiling that can now be discussed. Referring back to Fig. 36, their general optical characteristics and their particle size distribution after deposition may not strongly depend on the details the chemical nature of the particles. The application of the Ångström turbidity equation and the IEST-STD-CC 1246 standard should therefore be further tested to establish its usefulness in estimating the impact of soiling on the electrical performance of PV modules. For the optical aspects of PV soiling that include particle composition, the more rigorous approach of Piedra et al.33 can be employed, for example by utilizing the real and imaginary parts of the refractive index of the particles.One should recognize that the composition of atmospheric particulates also depends on location and it varies with time. The composition of particles deposited by soiling therefore also varies. One of the main findings of this report is the fact that soiling collected on PV glass in different locations has a similar spectral behavior that can be modeled using the equations inspired by the Ångström turbidity equation. While the composition of the particles is certainly involved in how they adhere to the glass17, asserts that the measured spectral transmittance of the soiled cover glass on a PV module is relevant to quantify the impact of soiling on a module’s performance. With the completion of the studies of this report, this can now be explored further via measurements of a module’s effective transmittance. To accomplish this, the external spectral quantum efficiency, EQE(λ), or spectral response, SR(λ)46, can be utilized. It can first be measured for a small soiled module and then that module can be cleaned. The ratio of SR(λ), soiled to clean, can then be directly compared to the relative transmittance versus wavelength for the case of the glass alone as described in this study. This work is at present ongoing and will be described in a future report. The results and techniques described in the present report therefore serve as a baseline and reference for that research.Our work, and the work of othersIn this study, we characterized the soiling that was naturally deposited on low-iron glass used for PV modules. Accumulation was sampled at seven locations worldwide with very different climactic and environmental conditions. A somewhat surprising result is that the spectral characteristics of the soiling were, in general, remarkably similar at these locations, in that they exhibited lower transmittance in the UV and blue regions of the spectrum, and a gradual asymptotic increase towards the red and infrared regions. The general shape of the transmittance curve can be described via a modified form of the Ångström turbidity equation, which itself has long been found to be useful in describing the transmittance of the Earth’s atmosphere due to the presence of aerosols and particulate matter. This is the first time that an equation that describes the attenuation of light by aerosols suspended in the atmosphere has been proposed and applied to describe the wavelength dependence of the transmittance of light due to those particles that end up adhering to the glass surface. This could provide a useful characterization tool to measure soiling losses in deployed photovoltaic arrays for different types of PV modules by considering the incoming spectral irradiance and each PV module’s spectral response.L of naturally soiled coupons after eight weeks of exposure was found to be between 600 and 1200 µm. The fractional area coverage f could be measured and estimated using a formalism related to the cleanliness standard. As suggested by other studies, the transmittance linearly correlates to f, thus linking the measurements from a suitably equipped spectrophotometer and a microscope. Future work should therefore explore additional connections between the IEST-STD-CC 1246E cleanliness standard and the Ångström turbidity equation analysis approach.We also found that the distribution of particle sizes at the various sites closely follows the distribution represented by the IEST-STD-CC 1246E cleanliness standard. To our knowledge, this is the first time it has been shown that the distribution described in this standard is valid also for outdoor-deposited soiling relevant to solar conversion. The cleanliness level The empirical models we have presented, relating to both the spectral and the particle size distribution, can serve as a reference for future studies, as researchers will be able to use them to optically characterize the soiling accumulated on PV glass and PV modules in order to more easily model the expected electrical losses. A variety of sites should be studied worldwide, but with a larger sample size and more replicates at each site so that a full statistical and uncertainty analysis can be performed.25. At the end of the 8-week collection campaign, the coupons that were never cleaned were sent to NREL and the University of Exeter for detailed analysis. Each coupon was placed in an individual case for shipping, which limited the loss of dust and avoided cross-contamination among the coupons during the transportation. A loop of tape placed at between the bottom of the glass and the bottom of the box prevented the former from moving within the box. The coupons, and the cases, were visually inspected at arrival to check for a loss of dust from the surface of the glass, which did not occur.Seven 4 cm × 4 cm, 3 mm thick Diamant low-iron glass coupons from Saint-Gobain Glass were shipped to different sites worldwide. The seven locations (listed and described in Supplementary Information) were chosen to represent a large variety of soiling and environmental conditions. To maximize the collection of particles, the glass was mounted in a horizontal orientation. The full details of the testing procedure has been described elsewhere51. In addition, recently launched low-maintenance soiling detectors quantify the loss of PV modules through the optical analysis of the contamination accumulated on glass53. DustIQ, produced by Kipp&Zonen, measures the backward reflection of a soiled PV glass, whereas MARS, developed by Atonometrics, measures the brightness of the pixels of a camera placed under the glass. Both prototypes are being tested and have shown good results when their measurements are compared with the soiling loss experienced by PV modules55.Glass coupons are a standard method for the analysis of soiling of photovoltaics and have been used in a number of research papers7, expresses the ratio between the performance of a soiled PV device under outdoor conditions and the performance of the same PV device but without soiling. It has a value of 1 if no soiling is present on the PV surface, while it decreases while soiling accumulates. In the present work, the methodology described in prior work11 has been followed. The performance of the modules has been quantified though the short-circuit current and, therefore, the soiling ratio at any given time, t, has can be expressed as:Iscsoil and Iscref are the short-circuit currents of the soiled and the clean, reference PV device. These currents can be measured directly from the PV devices, or can be related to the solar spectral irradiance and solar cells spectral response according to the following equations56:λ1 and λ2 are the lower and upper limits of the PV absorption band. In this work, the optical spectrum has been limited to the wavelength range 350 to 1100 nm. APV is the active area of the PV devices (solar cells in a module). EG is the spectral irradiance. For the results shown in Table 27 has been utilized even though the soiling ratio is operationally determined for solar noon. SR(λ) is the spectral response of the photovoltaic material. The results in Table τ is the relative spectral hemispherical transmittance due to the soiling. Referring to Fig. external spectral response of a soiled module.The most common metric to quantify the soiling loss is the soiling ratio. This metric, defined in the IEC 61724-1 standardτ(λ), calculated as a ratio of the hemispherical transmittance of each soiled coupon to the hemispherical transmittance of the unsoiled reference coupon.The spectral hemispherical transmittance of the soiled coupons after the 8-week outdoor exposure was taken using a Cary 500 spectrophotometer with an integrating sphere attachment at 1 nm steps between 300 nm and 1100 nm. For the processing of the broadband transmittance, only the hemispherical transmittance between 350 nm and 1100 nm was considered, because of the confounding factor of the absorption of the glass itself at wavelengths less than 350 nm. Three measurements were taken per coupon and averaged, then three more measurements were taken in a different location on the coupon and averaged to partially mitigate the non-uniformity of soiling. Only one set of transmittance measurements  were available for Chennai and Jaén. The transmittance of a clean reference coupon was taken at the start and the end of each set of measurements to check the consistency of the measurement and to obtain the relative hemispherical transmittance of soiling, λ ≥ 800 nm was applied. Examples are shown in Supplementary Information. Although a full uncertainty analysis has not been completed for the spectrophotometer work, the standard uncertainty associated with the repeatability of the transmittance measurements is estimated to be ±0.005.We also applied an offset correction to all the measurements to correct for detector and filter change near 800 nm. The offset was calculated as the difference between the broadband transmittance between 790 nm and 799 nm and the broadband transmittance between 800 nm and 809 nm. Then, the offset correction to all the data for wavelengths 2 is the smallest area, as that was the pixel area at that magnification). The image settings  were adjusted to capture images under optimal conditions41. The micrographs were analyzed using the image processing software package, ImageJ34. Its auto threshold function was used to generate 8-bit grayscale images. Some images presented an oversaturation on one or more of the corners. In order to process all the images in the most consistent way, only the central part of the image was used for counting particles, using a rectangular mask.Microscope images were captured at 100× and 500× magnification for the coupons soiled at each of the seven sites. The image of each coupon has been taken using a Keyence VHX-5000 microscope, at a resolution of 1200 pixels × 800 pixels. Particles less than 2.4 µm in diameter could not be counted at 100 × (4.5 µmA(x), where x is a counting index. The particle areas thus obtained were summed and the result was divided by the total analyzed area, Am, in the micrograph  to yield the measured fractional area coverage, f. The fractional area coverage is given by,ImageJ was used to count the particles and measure their area projected on the plane of the glass coupon. The area of each particle is given as D, and the particle size distribution, both the size distribution density and the cumulative values, N(D).The results were further processed to estimate the effective particle diameter, 57. Nonlinear least-squares with a Trust Region Reflective algorithm were employed. The initial guesses and the boundary conditions for each variables are shown in Table L value between 1 and 3000 (at steps of 1) that returned the lowest mean squared error.The curve fitting for Eqs.  and 3)  3) was pL between 2% to 3% by repeating the analysis using particle diameters increased or decreased by 1 µm. To estimate the sampling size uncertainty, the ImageJ analysis was repeated with a rectangular mask of one half of the area used for the results reported. The resulting L values differed by 3% to 7%. Given the variance of the particle size distribution and that, with the exception of Penryn, the number of particles was over 3000, this uncertainty is reasonable58.An uncertainty of ±0.005 was considered in the measurement of the hemispherical transmittance. We also estimated an uncertainty in the estimation of the projected effective spherical particle diameter to be roughly 1 µm. This leads to uncertainties in the determination of Supplementary Information.


We report experiments investigating how experience influences the endowment effect. Our experiments feature endowments which are bundles of unfamiliar consumption goods. We examine how a subject’s willingness to swap items from their endowment is influenced by prior experiences of tasting the goods in question and by prior experiences of choosing between them. We do not find a statistically significant endowment effect in our baseline treatment and, because of this, we are unable to test for an effect of consumption experience. We do find an endowment effect when the endowment is acquired in two instalments and, in this setting, we find some evidence that choice experience increases trading. In a follow-up experiment, we find evidence that the absence of an endowment effect in our baseline treatment is due to subjects being more willing to swap when they do not have to give up the last unit of their endowment.The online version of this article (10.1007/s40881-017-0044-z) contains supplementary material, which is available to authorized users. We present new experiments studying determinants of the ‘endowment effect’  with each element s, of a state space S. The state space represents ‘taste uncertainty’ which can arise from extrinsic or intrinsic sources. For example, when in a restaurant and considering the act “order fish”, extrinsic uncertainty may exist in relation to whether the fish will be cooked well or not, while intrinsic uncertainty might reflect an individual’s lack of clarity about their own preference (e.g. not being sure whether one is in the mood for fish).We draw on a theory proposed by Loomes et al.  ; hence, there is an endowment effect.How LOS explain the endowment effect can be illustrated with a simple example. Imagine a choice between two acts s1 or s2 occurs for sure. The individual then has a strict preference ranking of the two acts which is independent of the status quo.taste uncertainty hypothesis and in Sect. A distinctive property of the LOS model is that “the strength of status quo effects is positively related to the extent of taste uncertainty” LOS, p. 132). The intuition follows from the previous example. In the absence of taste uncertainty, either 2. The inchoice experience hypothesis. While we do not know of a current theory which specifically predicts this, more than one plausible psychological mechanism might work in this direction.Our experiment was also designed to test the conjecture that prior experiences of making choices between specific goods may weaken a subsequent endowment effect, relative to those goods. We call this conjecture the biases, causing stated preferences to deviate from underlying preferences , that would provide another conduit for operation of the choice experience hypothesis, consistent with the LOS model.taste uncertainty hypothesis by investigating whether consumption experience, in an environment where subjects are uncertain about how much they will enjoy available alternatives, reduces the endowment effect. We do this by comparing behaviour in two treatments which we label BASELINE and TASTING. These two treatments are represented in the left-hand tree in Fig. We test the would be unsure how much they might like them. The two goods were premium organic vegetable crisps and handmade organic lemonade . The goods were supplied by specialist wholesalers and had similar retail prices of approximately £2. The limited availability and premium nature of the goods meant that subjects were unlikely to have tasted them before. The second distinctive feature of our BASELINE treatment was that each endowment was a bundle of goods rather than a single object: each subject was randomly endowed with either a ‘crisps-rich’ bundle consisting of two packets of crisps and a single bottle of lemonade (which we denote ccl) or a ‘lemonade-rich’ bundle consisting of a single packet of crisps and two bottles of lemonade (denoted cll). The rationale for using bundles is explained below.Our BASELINE treatment was a variant of Knetsch’s  classic The TASTING treatment was identical to the BASELINE treatment except that each subject consumed a small amount of the two goods before being endowed with their bundle. Starting at the top of Fig. Under conventional preference theory in which endowments play no role, even if tasting changes a subject’s ranking of the bundles, each subject has a 50% chance of not being endowed with their most preferred bundle. Hence, we should expect a 50% chance of swapping in both the BASELINE and TASTING treatments.Given the assumptions that (1) the goods used in our experiment are ones for which individuals would have taste uncertainty and that (2) consuming small quantities of these goods would reduce taste uncertainty, the comparison of behaviour in the BASELINE and TASTING treatments provides a simple test of the hypothesis that taste uncertainty contributes to the endowment effect. On that hypothesis, we should expect the endowment effect to be relatively weaker in the TASTING treatment.choice experience hypothesis. Our test involves a comparison of two treatments which we label CHOOSING and PASSIVE. These treatments employ a novel variant of the swapping task that involves sequences of choices. It facilitates a test of the choice experience hypothesis by requiring subjects to choose between the two goods en route to their endowment while, nevertheless, ensuring that they receive a random endowment of goods before facing the swap decision.Our second main objective was to test the decision node at which subjects chose either one packet of crisps or one bottle of lemonade, which they were then physically given. A random device then determined a ‘top-up’ to their endowment, so that with equal probability they would find themselves with either a crisps-rich or a lemonade-rich bundle.The PASSIVE and CHOOSING treatments are described by the right-hand tree in Fig. This “top-up” method ensures that the bundle held at the point of the swap decision is randomly determined in both the PASSIVE and CHOOSING treatments, and note that it is independent of the initial choice in the latter. Hence, the prediction of a 50% swap rate based on standard theory applies to both. Comparing behaviour between these treatments provides a test of the choice experience hypothesis. On that hypothesis, we should expect more swaps in the CHOOSING treatment than in the PASSIVE treatment.h = 0.5 (approximately the difference between a 0.25 and a 0.5 swap rate) and a 0.05 significance level, the power is 80% for within treatment tests (i.e. tests for an endowment effect) and 70% for between treatment tests (i.e. tests for differences in swap rates). Following the swap decision, each subject completed a questionnaire. This provides information on individual characteristics which we exploit in the analysis of Sect. To test our hypotheses, 210 subjects recruited at the University of Nottingham, were randomly assigned to our four treatments. Each treatment had around 50 subjects. For an effect size with Cohen’s Endowments column shows the number of subjects initially endowed with each of the two bundles. The Swaps column reports the total number of swaps and (in parentheses) the number of swaps in each possible direction: swapping crisps for lemonade (c → l) or lemonade for crisps (c ← l). The Swap rate is the proportion of subjects who swapped. The final column reports p-values for Boschloo tests of the null hypothesis (based on standard preference theory) that the final allocation is independent of endowment (i.e. there is 50% swapping rate), against the alternative hypothesis that there is an endowment effect (i.e. the swap rate is less than 50%).Table We comment first on the results for the BASELINE and TASTING treatments. While the swap rates for these treatments have the expected pattern with BASELINE < TASTING < 0.5, neither has a statistically significant endowment effect. The absence of an endowment effect in the BASELINE treatment is noteworthy, and we examine this further in Sect. p = 0.1023, Boschloo test with one-sided alternative hypothesis) though we do also find a (weakly) significant effect of choice experience in the individual-level analysis below  and the experience of choosing weakens it. In line with the choice experience hypothesis, the trading rate rises to 0.35 for the CHOOSING treatment where subjects are approximately 50% more likely to trade. This treatment difference just fails to reach significance at the 10% level . Comparing the BASELINE and PASSIVE treatments, which control for the experiences of choosing between and tasting the goods, respectively 42% and 23% of subjects swapped their endowment . These tests provide evidence that acquiring an endowment in stages strengthens the endowment effect. We think this is an intriguing discovery and briefly discuss its interpretation and potential significance in Sect. An unanticipated feature of our results is the difference between the treatments in which the acquisition of endowments occurred in two steps , rather than one step . While 44% of subjects swapped in the one-step treatments, only 29% of subjects did so in two-step treatments . Hence, in these data, more loss averse individuals were less likely to trade. While this result supports theories, including LOS, which invoke loss aversion to explain the endowment effect, we note that we do not replicate this association in the follow-up study reported in Sect. In all three models that include individual-level characteristics , the coefficient for measured loss aversion is negative (other characteristics are never significant). Tests of the null hypothesis that the swap rate is independent of loss aversion are rejected at the 5% level .Models 3 and 5 provide evidence that the experience of choosing part of the endowment increases the trading rate (and reduces the endowment effect). Tests of the null hypothesis that the trading rates in the PASSIVE and CHOOSING treatments are equal are rejected at the 10% level in favour of the alternative hypothesis that the trading rate is Finally, this analysis confirms that acquiring endowments in two steps decreases the trading rate (increases the endowment effect). This is evidenced by the significant negative coefficients on PASSIVE in models 3 and 5 and by the significant coefficient for ‘Two-step’ in model 4.A notable feature of the above results is our failure to find a significant endowment effect in our BASELINE treatment, which is closest to the classic swapping task of Knetsch . In thisonly mug or only chocolate bar; whereas, in our experiment, subjects were giving up only one of two packets of crisps or one of two bottles of lemonade. It is possible that loss aversion may be more acute in situations where one would be giving up the last unit of a good. However, since there is some evidence that the extent of an endowment effect may depend on the nature of the goods being traded . The experiments were conducted at the University of Exeter. A total of 184 subjects participated, with 92 in each treatment. Within each treatment, half of the subjects had each endowment. We used more subjects per treatment in the follow-up experiment to increase statistical power. If the effect size has Cohen’s h = 0.4 (approximately the difference between a 0.3 and a 0.5 swap rate) and the significance level is 0.05, 92 subjects per treatment gives 99% power for within treatment tests for an endowment effect and 77% power for between treatment tests for differences in swap rates. Testing for smaller effect sizes requires considerably more observations.The follow-up experiment had two treatments. The first replicated our original BASELINE treatment; the second was the same except that endowments were p = 0.0003, Boschloo test with one-sided alternative hypothesis). These results clearly implicate the use of bundles (as opposed to unfamiliar goods) as the culprit for eliminating the endowment effect in our original BASELINE treatment.In the baseline replication (with bundles), there were 39 swaps . This replicates the BASELINE finding reported in Sect. taste uncertainty hypothesis and the choice experience hypothesis.Our initial design was set up to test two hypotheses partly motivated by existing theory and evidence: the reduces the endowment effect observed in a later swap task involving those same goods. While we have found only modest support for this effect, there is a case for further investigation because the operation of it appears to cohere with emerging theory and evidence. From a theoretical viewpoint, the choice experience hypothesis can be interpreted as an implication of the LOS model. We view this theory as an attractive putative account of our data because it models mechanisms which may explain not only why the endowment effect occurs, but also why it changes as a consequence of particular types of experience. From an empirical viewpoint, we see a possible parallel with Engelmann and Hollard  is the factor which most likely suppressed the endowment effect in our BASELINE treatment. Although it was not part of our initial strategy to test the influence of bundles, we view this aspect of our results as identifying a psychologically plausible determinant of the endowment effect. That is, people are less prone to it when they do not have to give up their final unit of an endowed good.A final intriguing finding is that even though there was no endowment effect for bundles acquired in a single step, it re-emerged for bundles acquired in two steps. This result, while unanticipated, is possibly related to so-called “splitting effects” reported across a broad range of decision contexts Below is the link to the electronic supplementary material.


The IBO method provides a convenient quantitative method for deriving curly arrows in a rational manner rather than the normal  For this purpose, we simply take the Kohn‐Sham wave functions along every step of the IRC and transform the occupied molecular orbitals into IBOs. These localised orbitals now provide a direct link between the computational results and Lewis structure depictions. To monitor the changes of the IBOs and by extension bonds and lone pairs, we match IBOs between frames with the maximum overlap. In order to probe if an IBO and therefore a bond/lone pair is involved in a bond making or breaking event, we compute and plot the quantity orbital change . This criterion simplifies the identification of IBOs that are to be inspected in a given transformation. This procedure is automated in the IboView codewhere seven IBOs which are involved in changes, unlike the standard textbook curly arrow formalisms. So where does this discrepancy originate?After we carried this procedure out, our analysis reveals that there are The answer to this question is rather simple and is captured by the single word: orientation. We have grouped the seven IBOs, and by extension the seven curly arrows, into three groups Scheme , using c(blue) and the O−O σ bond of the peracid (red),The alkene π bond The π lone pair on the O−H oxygen of the peracid (green), the changes from a π lone pair to a true C=O π bond and the reverse (magenta and grey),Breaking and making of the O−H bond in the starting material and product (orange and purple). In Scheme I involves the transformation of the alkene π bond to one of the C−O bonds in the epoxide unit (blue) and the cleavage of the O−O σ bond of the peracid (red). Inspecting the IBOs it becomes clear that the alkene, much as expected, acts as a nucleophile and that the electron pair of this bond is rearranged into becoming a covalent C−O σ bond in the epoxide. Also, in this group is the heterolytic cleavage of the O−O σ bond resulting in the formation of a lone pair localised on what is to become the carbonyl C=O oxygen atom. However, unlike in the textbook mechanism, this does not become part of the delocalised π bonding regime. Figure Group II. The most prominent is the formation of the second C−O σ bond of the epoxide, which is formed from a lone pair on the O−H oxygen atom (green). This already departs from the four‐arrow textbook mechanism, where the electron pair of the O−H σ bond is conventionally used for the C−O bond formation in the epoxide, matching instead the five‐arrow variation. This bond change is delayed compared the bond changes in group I, which may be attributed to the formation of the “hidden intermediate” which is stabilised by contribution of carbocation resonance character at the Me‐substituted carbon atom. In addition, this bond formation coincides with the change from an O‐centred π lone pair to the C=O π bond (magenta) and the opposite process (grey).Subsequent changes to the bonding occur in group III conclude the epoxide formation, where the two other changes to bonds/lone pairs are associated with an intramolecular proton transfer. As the O−O bond breaks and the epoxide forms, the O−H bond (orange) morphs into a lone pair on the oxygen atom of the epoxide and the proton is picked up by a lone pair on the oxygen atom (purple) originally associated with the carbonyl oxygen atom of the peracid. Again, the orientation of the lone pairs is such that they do not allow for participation in delocalisation in the π system of the carboxylate unit, as implied by the textbook mechanism.The final changes in group does orientation matter? The question is of course rhetorical and the answer is yes. We may use the phenyl anion to make this point clear. In the anion, the carbon‐centred lone pair of the carbanion is localised and may not be drawn as part of the mesomeric structures used to represent the aromatic ring. The argument for this is simply that the orientation of this lone pair is perpendicular to the 6‐electron π system of the aromatic ring. In exactly the same way such a difference is found in the reaction between alkenes and peracids. The textbook representations do not take this subtle difference into account and therefore fail to provide even a qualitative description of the underlying changes to the electronic structure. One could argue that curly arrows are merely a formalismOverall, these changes identified by the IBO analysis of the electron flow only partially agree with conventional textbook mechanisms. It is crucial to appreciate that the major differences are a result of the orientation of bonds and Ione pairs featuring π symmetry and their changes. One could argue that this is a subtlety of minor relevance. So, If we accept this connection, there are some important conclusions that can be made. In particular, the orientation of IBOs, or more generally the orientation of bonding interactions, is fundamentally important, but is often not captured by curly arrows. If, however, we pay close attention we can indeed devise curly arrow mechanisms that are meaningful representations of electron flow in chemical reactions. An informally taught rule that should therefore be viewed with great caution is that in curly arrow representations, the mechanism represented by the least number of curly arrows may be the most appropriate. This latter statement inadvertently renders curly arrows a formalism, which they do not have to be.I that the two C−O bonds in the epoxide are not formed in a synchronous fashion. Instead, the initial C−O bond is formed upon nucleophilic attack of the alkene onto the electrophilic oxygen, which occurs with an SN2‐like concomitant O−O bond scission. The presence of the Me substituent allows for the development of some temporary carbocation character, leading to a delay in the formation of the second C−O bond of the epoxide. As the O−O bond scission occurs in a concerted fashion, plot II indicates that the proton transfer occurs only after the epoxide is fully formed. Although the reaction proceeds with a single transition state, we can consider structure (6) in Figure c.f. Scheme 6), which results in the formation of the product upon intramolecular proton transfer.Until this point, we have inspected the changes of the IBOs and their connection to curly arrows with little focus on how these changes are choreographed. In Figure cis and trans dicyanopropene we also find the epoxide formation to be more concerted in terms of the two forming C−O bonds, as is the case for a single CN substituent. For trans‐dicyanopropene in particular, the changes are more drastic. At first glance the epoxide formation becomes more concerted, but a second notable feature is that the proton transfer no longer occurs after the epoxide formation but precedes it. The changes in dipole moment, as indicators of hidden ionic intermediates at the proton transfer stages, also reflect this |2 represented by the orbital, and is formally defined as:However, the orbitals really are three‐dimensional functions φ, and the two‐dimensional iso‐surface representation is but one of multipe possibilities of condensing important parts of information they carry into a form accessible to human understanding. An even simpler representation, which nevertheless carries information relevant to the curly arrow formalism, is to represent each IBO φ with a single point, its orbital centroid; this is effectively the “average location” of the charge density |φ because of the synchronous coupling with the C−O bond formations. To recover these insights from curly arrow representations of the mechanisms does require inclusion of additional information such as that imparted in Figure Variation in the substituents on the ethene can be seen to strongly influence whether a proton transfer occurs before or after the C−O bonds are formed. It is of interest therefore to compute the kinetic isotope effects (KIE) resulting from deuterium substitution on both the ethene and on the transferring proton Table . For tho2.22Cl2 using the electronic structure code Gaussian 16.We computed the reaction pathway at the M06‐2X3e. g. the closed shell epoxidation of an alkene by a peracid can be placed on a firmer theoretical foundation by deriving and analysing intrinsic bond orbitals (IBOs) along the reaction path. This procedure reveals the importance of including transformations between σ and π bonds. This recognition suggests that the five‐arrow mechanism  should be addressed to the authors.SupplementaryClick here for additional data file.


The blood flow through the major vessels holds great diagnostic potential for the identification of cardiovascular complications and is therefore routinely assessed with current diagnostic modalities. Heart valves are subject to high hydrodynamic loads which render them prone to premature degradation. Failing native aortic valves are routinely replaced with bioprosthetic heart valves. This type of prosthesis is limited by a durability that is often less than the patient’s life expectancy. Frequent assessment of valvular function can therefore help to ensure good long-term outcomes and to plan reinterventions. In this article, we describe how unsupervised novelty detection algorithms can be used to automate the interpretation of blood flow data to improve outcomes through early detection of adverse cardiovascular events without requiring repeated check-ups in a clinical environment. The proposed method was tested in an in-vitro flow loop which allowed simulating a failing aortic valve in a laboratory setting. Aortic regurgitation of increasing severity was deliberately introduced with tube-shaped inserts, preventing complete valve closure during diastole. Blood flow recordings from a flow meter at the location of the ascending aorta were analyzed with the algorithms introduced in this article and a diagnostic index was defined that reflects the severity of valvular degradation. The results indicate that the proposed methodology offers a high sensitivity towards pathological changes of valvular function and that it is capable of automatically identifying valvular degradation. Such methods may be a step towards computer-assisted diagnostics and telemedicine that provide the clinician with novel tools to improve patient care. Heart valve disease is an increasingly common pathology of the cardiovascular system and more than 800,000 patients annually are expected to require heart valve replacement by 2050 . Aortic Our method monitors the temporal evolution of valvular function to detect progressive valve degradation. It uses training data and monitoring data that originates from the implanted valve, acquired during specific phases. These phases are illustrated in To enable assessment of valvular function it is necessary to measure the flow characteristics downstream of the AV. This flow data can be obtained through current diagnostic modalities, such as Doppler echocardiography or flow-MRI. Preferably, the data is acquired using a flow sensing implant, capable of in-situ flow measurement and transcutaneous data transmission. The use of such technology allows for automation of the data acquisition step, facilitating the recording of periodic measurements in a semi-continuous fashion over an extended period of time . The datA sin(2πft + ϕ) + b is fitted to this periodic signal using a Levenberg-Marquardt algorithm [ϕ and the frequency f of the signal’s first harmonic  that will be used  was defined to characterize the dynamic behavior of the postvalvular flow characteristics. These are the phase in the cardiac cycle (Z1), the instantaneous flow rate (Z2) and the heart rate (Z3).A typical flow recording contains continuously acquired data from multiple subsequent heartbeats. The preprocessing step condenses this data into a single cardiac cycle and performs feature scaling to maximize the performance of the machine learning model. This is done by fitting a sinusoid to the data, which allows identifying the instantaneous phase in the cardiac cycle from multiple heartbeats. lgorithm , 50 to oharmonic . While aharmonic  by definnvariant ). A set Flow measurements downstream of the healthy aortic valve are used to obtain a patient-specific physiological baseline. This data is recorded for the freshly implanted valve (after the body has adapted to the surgery) yielding a statistical model of the fully-operational BHV. After model training, all further measurements will be compared to this baseline model to detect valvular pathology, i.e. behavior that deviates from this baseline. We hypothesize that such a deviation can be automatically detected using a novelty detection algorithm, which is a special type in the class of anomaly detection algorithms , and thax1, …, xl, it is the training objective to define a hyperplane in a high-dimensional feature space that separates this data from the origin with the largest possible margin. Finding a hyperplane that separates the data from the origin in the feature space is a concept that is special to one-class classification and builds on the principle that the origin is part of the outlier class, which allows deriving the optimal hyperplane using classical two-class SVM methodologies. The hyperplane is parameterized by a vector containing the feature weights ω and a bias term ρ. Maximizing the distance to the origin equates to minimizing the term ∥ω∥2. A set of slack variables ξi is introduced to prevent overfitting by allowing a predetermined fraction of boundary violations . This is controlled by the hyperparameter ν which sets the maximum fraction of allowable boundary violations and the minimum fraction of support vectors. As SVMs rely on linearly separable datasets, but real data is often highly nonlinear, the original data must be transformed into a higher-dimensional space in which the data becomes linearly separable using a transformation Φ(xi). The one-class SVM training objective can then be formulated as constrained optimization problemω ⋅ Φ(xi)) ≥ ρ − ξi and ξi ≥ 0 ensure that the training samples lie on the correct side of the hyperplane. A decision function is defined that predicts whether a given input sample should be regarded regular, or irregular and takes the formα and the kernel definition asρ of the decision function can be computed asConsidering a training dataset, given by training samples -trick”) . The optWe define a diagnostic index that is indicative of the severity of valvular malfunction. It quantifies how well a given measurement agrees with the baseline model, where model agreement equates lack of novelty. This model compliance index (MCI) is defined as the fraction of measurement samples that lie within the decision boundary (inliers), compared to the total number of acquired samples of that recording,2, 31.4 mm2 and 99.4 mm2, respectively. All measurements were performed at a constant heart rate, which reduced the problem to two representative features, Z1 and Z2 for this setting, because the feature Z3 (heart rate) becomes obsolete.Aortic regurgitation (AR) is a common heart valve disease in which the valve’s leaflets fail to close completely during the diastolic phase . This leγ (which controls the amount of regularization) and the fraction of allowable boundary violations ν. γ and ν to illustrate their effect. An algorithm that allows no or few boundary violations  is said to have a hard margin. One that has more relaxed constraints on boundary violations has a soft margin. The kernel coefficient γ controls the width of the RBF and thereby the radius of influence of each training sample. A high γ limits the influence of each training sample to its close proximity and can lead to an irregular and complex decision function. When γ is reduced, it acts as a regularization term leading to a smoother decision function. The parameter ν is used to penalize boundary violations. When ν is small, the training algorithm ensures that a large fraction of the training samples reside within the inlier region. A relaxation of ν typically leads to a smaller inlier region as more training samples are allowed to fall into the outlier region. It was our training objective to accurately resolve the dynamics of the flow rate profile while keeping the decision function smooth and avoiding overfitting. Models with high regularization (top row in γ (bottom row) resulted in an irregular decision function that would not generalize well to new data. Models with a hard margin showed a tendency to overfitting (left column), while gaps appeared in the decision function  when ν was set too high (right column). The optimal set of model parameters was determined in a grid search in which various combinations of ν and γ were analyzed. The performance metric for parameter optimization was the ability of the model to discern baseline data from AR data, i.e the sensitivity of the MCI towards pathological flow. It was evaluated as the average difference between the MCI for AR and the baseline MCI. The highest sensitivity was achieved for ν = 0.05, and it increased with γ. Little gains in sensitivity could be obtained beyond γ = 7. Instead, the resulting models became more irregular and showed signs of overfitting, limiting their ability to generalize to new data. We tuned the model to allow for 5% training errors  to account for noise in the baseline data, while avoiding gaps in the decision function. The kernel coefficient of the RBF was set to γ = 7 for a good compromise between sensitivity and regularization (i.e. maintaining a smooth decision function). ν = 0.05 and γ = 7. It reflects the flow characteristics of the fully-functioning valve, including its natural variance.The blue lines in For each of the three cases of AR, eight measurements with a length of 1000 samples each (∼16 cardiac cycles) were recorded with the downstream flow sensor. This data was subsequently preprocessed as outlined above and applied to the baseline SVM. The results of model prediction were then used to compute the MCI. Z1, Z2). This was done to reduce the complexity of the problem and to limit the amount of data that needed to be acquired to obtain a representative physiological baseline. However, an analogous approach may be used that includes all three features . The study of three cases of AR yielded discrete results for the MCI, which can be understood as intermediate stages of the gradual process of valvular degradation in the human body. Monitoring the MCI over time enables determination of the severity and the rate of valvular degradation which may be used to guide further action. We limited the experimental test case to the detection of AR, but the same approach may be used to detect valvular defects due to other pathologies, such as aortic stenosis. Furthermore, we limited our discussion to the detection of BHV malfunction, but the nature of this method  permits applications in other scenarios where monitoring of physiological function is of interest.The high sensitivity of the MCI with respect to AR indicates that the novelty detection algorithm is capable of identifying valvular degradation based on the flow rate recordings. Even mild AR led to a significant reduction in MCI from the baseline. Keeping the base frequency of the pulsating piston pump constant for all scenarios allowed us to reduce the problem to two features Click here for additional data file.S2 Dataset(ZIP)Click here for additional data file.