Alex De Visscher

The specific ion theory (SIT) is a relatively simple model for the description of activity coefficients of ions in electrolyte solutions. However, the SIT model does not account for ion pairing, a phenomenon that is pronounced in 2–2 electrolytes. In this work, a recent extension of the SIT model is further extended to account for ion pairing. The model extension is tested with reported data for BeSO₂, MgSO₄, MnSO₄, NiSO₄, CuSO₄, ZnSO₄, and CdSO₄. The standard error of the fit is typically around 1%. To get this accuracy in model fits of the activity coefficients that are consistent with literature data of the MgSO₄⁰ ion pair concentration, it was necessary to work in a modified molality scale and to reduce the SIT parameter B from 1.5 to 1.2. The drawback of this choice is that a value of B equal to 1.2 leads to worse predictions of the activity coefficients of 1–1 electrolytes. It is concluded that not accounting for ion pairing in 2–2 electrolytes leads to overestimations of the ionic strength of up to 30% and leads to overestimations of the Debye-Huckel term for the activity coefficient of 2–2 electrolytes by up to 20%.

Chemical engineering involves a skill set that is transferrable to a broad range of other areas. A case in point is the work that is being done by chemical engineers to better understand and fight the COVID‐19 epidemic. In this study, we consider a problem that has eluded the COVID‐19 research community, which is nevertheless very tractable with a chemical engineering mindset: the true or intrinsic mortality rate of COVID‐19, that is, the fraction or percentage of COVID‐19‐infected people that die of the disease. We solve this problem in two locations (Spain and the state of New York) for the epidemic's first wave using a combination of daily death data, a fit of a computer simulation of an epidemiological model with adjustable parameters, and independent results of immunological blood testing on a random sample of the population. Parallels are drawn with the problem of determining the turnover frequency of a catalyst based on a similar combination of data and approaches. It is concluded from the study that the intrinsic mortality rate of COVID‐19 was 1.45 ± 0.45% during the first wave, a number that reflects OECD countries. By incorporating data on the age dependence of the mortality rate, a relationship f_mort = (3.0 ± 0.7) × 10⁻⁵ exp(0.1a), where a is the age in years, is tentatively put forward for the mortality rate as a fraction.

Various models have been developed to determine ammonia (NH₃) emissions from animal manure–processing lagoons to enable relatively simple estimations of emissions. These models allow estimation of actual emissions without intensive field measurements or “one‐size‐fits‐all” emission factors. Two mechanisms for lagoon NH₃ emissions exist: (a) diffusive gas exchange from the water surface and (b) mass‐flow (bubble transport) from NH₃ contained within the ebullition gas bubble (as it rises to the surface) produced from anaerobic decomposition of organic matter. The purpose of this research is to determine whether gas ebullition appreciably affects NH₃ emissions and therefore should be considered in emissions models. Specifically, NH₃ mass‐flow emissions were calculated and compared with calculated diffusive NH₃ emissions. Mass‐flow NH₃ emissions were evaluated based on a two‐film model, in connection with the acid dissociation constant of ammonium, to predict the degree of NH₃ gas saturation within the bubbles. Average daily ammoniacal nitrogen concentration, pH, and measured biological gas production (ebullition) in conjunction with literature values for Henry's law constant were used to calculate emissions from NH₃ saturation of ebullition gases. Ebullition enhancement of NH₃ surface emissions due to increased turbulence was estimated from average lagoon ebullition rates and literature values of turbulence enhancement. Ebullition enhancement of NH₃ surface emissions and ebullition mass‐flow NH₃ emissions was determined to be 3 emissions. Therefore, because ebullition effects are small, they may be neglected when developing process models to estimate NH₃ emissions from water surfaces of swine manure processing lagoons.

Estimations of the infection mortality rate of COVID-19, the disease caused by the SARS-CoV-2 virus, are prone to biases due to underdiagnosis, false positives, false negatives, and time lag between diagnosis and death. With a systematic analysis that combines epidemiological modeling of COVID-19 in Spain and in the state of New York, and results of random immunological testing in the spring of 2020 in both locations, most of the bias is eliminated and any remaining bias is evaluated and reported as an uncertainty estimate. A true infection mortality rate of 1.45 ± 0.45 % is obtained, representing an average for the two locations, and obtained with a different technique to minimize the effect of potential biases. In the absence of specific local data, this number can be used for the first wave of COVID-19 in OECD countries. This mortality rate estimate of the new coronavirus is sufficiently accurate to be used as a basis for policy decisions. When differences in age distribution between Spain and the state of New York are accounted for, tentative infection mortality rates of 1.18 ± 0.26 % and 1.94 ± 0.43 % are put forward for New York and Spain, respectively.

An epidemiological model for COVID-19 developed earlier was extended to determine the effects of behavioral changes, immunity loss, and vaccination on second and subsequent wave dynamics of the pandemic. A model variant that distinguishes four demographic groups with different infection rates and mortality rates was developed to test the hypothesis that behavioral divergence between groups can explain both the larger incidence and lower mortality rate of COVID-19’s second wave. A model version that incorporates immunity loss was developed to test the hypothesis that immunity loss can explain the second wave. Simulations indicate that of the two hypotheses, only the former is consistent with observed trends. Nevertheless, loss of immunity can significantly increase total number of deaths in the long run, particularly in cases where vaccine distribution is barely sufficient to reach herd immunity. The observed trends are illustrated with detailed simulations of the progression of COVID-19 in the United Kingdom, including the appearance of new strains. The U.K. case study indicates the extent to which NPI can be relaxed during the distribution of the vaccine.

An epidemiological model for COVID-19 was developed and implemented in MATLAB/GNU Octave for use by public health practitioners, policy makers, and the general public. The model distinguishes four stages in the disease: infected, sick, seriously sick, and better. The model was preliminarily parameterized based on observations of the spread of the disease. The model assumes a case mortality rate of 1.5%. Preliminary simulations with the model indicate that concepts such as “herd immunity” and containment (“flattening the curve”) are highly misleading in the context of this virus. Public policies based on these concepts are inadequate to protect the population. Only reducing the R₀ of the virus below 1 is an effective strategy for maintaining the death burden of COVID-19 within the normal range of seasonal flu. The model is illustrated with the cases of Italy, France, and Iran and is able to describe the number of deaths as a function of time in all these cases although future projections tend to slightly overestimate the number of deaths when the analysis is made early on. The model can also be used to describe reopenings of the economy after a lockdown. The case mortality rate is still prone to large uncertainty, but modeling combined with an investigation of blood donations in The Netherlands imposes a lower limit of 1%.

The Drake equation has been used many times to estimate the number of observable civilizations in the galaxy. However, the uncertainty of the outcome is so great that any individual result is of limited use, as predictions can range from a handful of observable civilizations in the observable universe to tens of millions per Milky Way-sized galaxy. A statistical investigation shows that the Drake equation, despite its uncertainties, delivers robust predictions of the likelihood that the prevalent form of intelligence in the universe is artificial rather than biological. The likelihood of artificial intelligence far exceeds the likelihood of biological intelligence in all cases investigated. This conclusion is contingent upon a limited number of plausible assumptions. The significance of this outcome for the Fermi paradox is discussed.