Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Spatial predictions of soil macro and micro-nutrient content across Sub-Saharan Africa at 250 m spatial resolution and for 0–30 cm depth interval are presented. Predictions were produced for 15 target nutrients: organic carbon (C) and total (organic) nitrogen (N), total phosphorus (P), and extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), aluminum (Al) and boron (B). Model training was performed using soil samples from ca. 59,000 locations (a compilation of soil samples from the AfSIS, EthioSIS, One Acre Fund, VitalSigns and legacy soil data) and an extensive stack of remote sensing covariates in addition to landform, lithologic and land cover maps. An ensemble model was then created for each nutrient from two machine learning algorithms—random forest and gradient boosting, as implemented in R packages ranger and xgboost—and then used to generate predictions in a fully-optimized computing system. Cross-validation revealed that apart from S, P and B, significant models can be produced for most targeted nutrients (R-square between 40–85%). Further comparison with OFRA field trial database shows that soil nutrients are indeed critical for agricultural development, with Mn, Zn, Al, B and Na, appearing as the most important nutrients for predicting crop yield. A limiting factor for mapping nutrients using the existing point data in Africa appears to be (1) the high spatial clustering of sampling locations, and (2) missing more detailed parent material/geological maps. Logical steps towards improving prediction accuracies include: further collection of input (training) point samples, further harmonization of measurement methods, addition of more detailed covariates specific to Africa, and implementation of a full spatio-temporal statistical modeling framework.     

Modeling Solar Drying Rate of Wastewater Sludge

Efficient solar drying requires that the drying rate is quantitatively known as a function of the environment and the control. To develop a drying-rate model for wastewater sludge, data were collected at a solar drying installation in Füssen, Germany. In this solar dryer, wet sludge is uniformly spread over a concrete floor under a greenhouse-like transparent cover. The sludge is mixed mechanically several times a day by an autonomous robot (electric mole®), the structure is fan-ventilated horizontally, and the indoor air is mixed by electric fans. Data of evaporation rate, environmental conditions, and control operations were collected over three drying cycles. Evaporation rate via sludge sampling and via vapor balance across the structure compared favorably, justifying the use of hourly vapor-balance data. Four types of prediction models were considered: physical, additive, multiplicative, and neural network. The multiplicative model has been selected for potential implementation. The most important predictors of evaporation rate, for the conditions under consideration, were (1) solar radiation, (2) outdoor temperature, (3) ventilation rate, and (4) dry solids content of the sludge. Air mixing is an order of magnitude less effective (per unit of air discharge) than ventilation.     

Ahp Cyclodepsipeptides: The Impact of the Ahp Residue on the “Canonical Inhibition” of S1 Serine Proteases

S1 serine proteases are by far the largest and most diverse family of proteases encoded in the human genome. Although recent decades have seen an enormous increase in our knowledge, the biological functions of most of these proteases remain to be elucidated. Chemical inhibitors have proven to be versatile tools for studying the functions of proteases, but this approach is hampered by the limited availability of inhibitor scaffold structures with the potential to allow rapid discovery of selective, noncovalent small‐molecule protease inhibitors. The natural product class of Ahp cyclodepsipeptides is an unusual class of small‐molecule canonical inhibitors; the incorporation of protease cleavage sequences into their molecular scaffolds enables the design of specific small‐molecule inhibitors that simultaneously target the S and S′ subsites of the protease through noncovalent mechanisms. Their synthesis is tedious, however, so in this study we have investigated the relevance of the Ahp moiety for achieving potent inhibition. We found that although the Ahp residue plays an important role in inhibition potency, appropriate replacement with β‐hydroxy amino acids results in structurally less complex derivatives that inhibit serine proteases in the low micromolar range.     

Iterative Antimicrobial Candidate Selection from Informed D‐/L‐Peptide Dimer Libraries

Growing resistance to antibiotics, as well as newly emerging pathogens, stimulate the investigation of antimicrobial peptides (AMPs) as therapeutic agents. Here, we report a new library design concept based on a stochastic distribution of natural AMP amino acid sequences onto half‐length synthetic peptides. For these compounds, a non‐natural motif of alternating D ‐ and L ‐backbone stereochemistry of the peptide chain predisposed for β‐helix formation was explored. Synthetic D ‐/L ‐peptides with permuted half‐length sequences were delineated from a full‐length starter sequence and covalently recombined to create two‐dimensional compound arrays for antibacterial screening. Using the natural AMP magainin as a seed sequence, we identified and iteratively optimized hit compounds showing high antimicrobial activity against Gram‐positive and Gram‐negative bacteria with low hemolytic activity. Cryo‐electron microscopy characterized the membrane‐associated mechanism of action of the new D ‐/L ‐peptide antibiotics.     

Entwicklung und Erprobung eines Wäschersystems zur CO2‐Abreinigung aus Rauchgasen mithilfe alkalischer Reststoffe

An innovative, technical approach for the reduction of CO₂ emissions is presented that utilizes alkaline wastes to capture CO₂ from flue gases in stable mineral form. Comprehensive pilot‐scale experiments were conducted with the developed flue gas scrubbing system at a power plant site. By optimizing the process parameters gas flux, CO₂ partial pressure, circulation flux and suspension liquid‐to‐solid ratio, a CO₂ binding of 40 – 90 g kg^–1 waste could be reached and up to 25 % of the CO₂ could be captured. The new technique is economically advantageous especially when both alkaline waste and CO₂ are produced on site and when the carbonated products can be used as secondary resources.     

Effect of fluoride varnish with added casein phosphopeptide-amorphous calcium phosphate on bond strength to enamel

To investigate the effect of fluoride varnish with added casein phosphopeptide-amorphous calcium phosphate on the shear bond strength (SBS) of two adhesive systems to enamel. Specimens obtained from permanent teeth were randomly distributed among four groups for enamel pretreatment [Control (no treatment, CNT), Duraphat varnish (DV), Clinpro White varnish (CWV), MI Varnish (MIV)], and each group was further divided into two subgroups according to adhesive [Etch&rinse (Adper Single Bond, ASB), self-etch (Clearfil SE Bond, CSE)]. Specimens were stored in distilled water at 37 °C for 24 h. Cylindrical composite specimens (2.3 mm in diameter, 3.0 mm in height) were then bonded to the enamel surfaces. SBS tests were performed and data were analyzed with two-way ANOVA and Tukey’s tests. For both CSE and ASB, SBS values of the CNT groups were significantly higher than those of all the enamel pretreatment groups (p < 0.05). Among the enamel pretreatment groups, SBS values with both adhesive systems were lowest in the MIV groups, followed by CWV and DV groups. In conclusion, pretreatment of enamel surfaces with fluoride-containing varnishes reduced bonding performance of adhesive systems to enamel. MIV appeared to cause greater enamel surface alterations and precipitation, which interfered with adhesive bonding mechanisms.     

A Detailed Model for the Sintering of Polydispersed Nanoparticle Agglomerates

In this study the coagulation, condensation, and sintering of nanoparticles is investigated using a stochastic particle model. Each stochastic particle consists of interacting polydisperse primary particles that are connected to each other. In the model sintering occurs between each individual pair of neighboring primary particles. This is important for particles in which the range of the size of the primary particles varies significantly. The sintering time is obtained from the viscous flow model. The model is solved using a stochastic particle algorithm. The particles are represented in a binary tree that contains the connectivity as well as the degree of sintering information. Particles are forme, coagulate, sinter, and experience condensation according to known rate laws. The particle binary tree, along with it the degree of sintering, is updated after each time step according to the rates of the different processes. The stochastic particle method uses the technique of fictitious jumps and linear process deferment. The theoretical results are fitted against experimental values for the formation of SiO ₂ nanoparticles and computer generated TEM pictures are presented and compared to experiments.     

Impact of Particle Generation Method on the Apparent Hygroscopicity of Insoluble Mineral Particles

Calcite (CaCO ₃ ) mineral particles are commonly generated by atomization techniques to study their heterogeneous chemistry, hygroscopicity, and cloud nucleation properties. Here we investigate the significant artifact introduced in generating calcium mineral particles through the atomization of a saturated suspension of the powder in water, by measuring particle hygroscopicity via CCN activation curves. Particles produced from atomization displayed hygroscopicities as large as κ_app > 0.1, 100 times more hygroscopic than that obtained for dry-generated calcite, κ_app = 0.0011. The hygroscopicity of the wet-generated particles increased as a function of time the calcite powder spent in water, and with decreasing particle size. Wet-generated calcium oxalate was more hygroscopic through wet- (κ_app = 0.34) versus dry-generation (κ_app = 0.048). Atomized calcium sulfate particles, however, were only slightly more hygroscopic (κ_app = 0.0045) than those generated dry (κ_app = 0.0016). Single-particle analysis by ATOFMS and SEM/EDX, and bulk analysis of the calcite powders by ICP-MS and IC revealed no significant soluble contaminants. The atomized particles were likely composed of components that dissolved from the powder and then re-precipitated, and appeared to contain little of the original mineral powder. The increased hygroscopicity of atomized calcite may have been caused by aqueous carbonate chemistry producing Ca(OH) ₂ , Ca(HCO ₃ ) ₂ , and metastable hydrates with increased solubility. Surface water adsorption may have also played a role, in addition to uncharacterized soluble components produced by wet-generation, and the precipitation of amorphous phases including glassy states. This study suggests that using wet-generation methods to suspend mineral dust samples will not produce particles with the correct physicochemical properties in laboratory studies, a finding which has important implications for past and future laboratory studies focusing on understanding relationships between the hygroscopicity and chemistry of mineral dust particles.     

Investigation of the phase separation of PNIPAM using infrared spectroscopy together with multivariate data analysis

The use of vibrational spectroscopy to investigate complex structural changes in polymers yields chemically rich data, but interpretation can be challenging and subtle but meaningful spectral changes may be missed through visual inspection alone. Multivariate analysis is an efficient approach to gain an oversight of small but systematic spectral differences anywhere within the spectra, providing further insight into structural changes and associated transformation mechanisms. In this study, the novel analytical approach of infrared spectroscopy combined with principal component analysis and Gaussian peak fitting was used to investigate the structural changes in aqueous solutions of a polymer, using poly(N-isopropyl acrylamide) (PNIPAM) in the atactic form and with controlled tacticity as a model system. Subtle spectral changes associated with the dehydration and phase separation upon heating included peak shifts, an area ratio change of the amide I band to the amide II band and formation of a new peak in the amide I band were efficiently detected. Dehydration and phase separation of PNIPAM occurred in two temperature ranges, one for the atactic and one for isotactic rich part, both involving a complex re-organization of the hydrogen bonds and change of the hydration layer. The changes agreed with existing results from other techniques, and new insights were gained into the effect of controlled tacticity on phase transformation behaviour. The study demonstrates that infrared spectroscopy combined with the multivariate analytical method principal component analysis and Gaussian peak fitting is an efficient approach to probing structural change in polymers during heating. The simplicity of the presented approach could find excellent use in analysing and understanding the molecular environment of a range of stimuli-responsive polymers, for instance block or grafted types of polymers, as well as those with controlled tacticity.     

Dynamic modeling of CO2 absorption from coal-fired power plants into an aqueous monoethanolamine solution

Among carbon capture and storage (CCS), the post-combustion capture of carbon dioxide (CO₂) by means of chemical absorption is actually the most developed process. Steady state process simulation turned out as a powerful tool for the design of such CO₂ scrubbers. Besides steady state modeling, transient process simulations deliver valuable information on the dynamic behavior of the system. Dynamic interactions of the power plant with the CO₂ separation plant can be described by such models. Within this work a dynamic process simulation model of the absorption unit of a CO₂ separation plant was developed. For describing the chemical absorption of CO₂ into an aqueous monoethanolamine solution a rate based approach was used. All models were developed within the Aspen Custom Modeler^® simulation environment. Thermo physical properties as well as transport properties were taken from the electrolyte non-random-two-liquid model provided by the Aspen Properties^® database. Within this work two simulation cases are presented. In a first simulation the inlet temperature of the flue gas and the lean solvent into the absorber column was changed. The results were validated by using experimental data from the CO₂SEPPL test rig located at the Dürnrohr power station. In a second simulation the flue gas flow to the separation plant was increased. Due to the unavailability of experimental data a validation of the results from the second simulation could not be achieved.