Büchi Labortechnik

<strong class="journal-contentHeaderColor">Abstract.</strong> Deforestation for cropland expansion in tropical sloping landscapes causes severe soil erosion and thus the loss of fertile, organic rich topsoil. The effects of land degradation on tropical soils developed from different parent materials, which may influence soil fertility and soil organic carbon (SOC) content, are still largely unknown. Here, we compared SOC and other soil fertility indicators in undisturbed tropical forest topsoils with cleared hillslope topsoils (cropland, abandoned, cropland, and reforestation with <em>Eucalyptus</em> monocultures) along the East African rift system using soil chronosequences after deforestation on both mafic and felsic parent material. In the mafic region, we found a consistent decrease of SOC, nitrogen, and phosphorus content with time after deforestation (relative changes of contents up to &minus;69 % SOC, &minus;72 % nitrogen, and &minus;92 % phosphorus). SOC was strongly stabilized by reactive metal phases with little to no benefits to general soil fertility. Consequently, cropland was frequently abandoned by farmers due to the combination of low pH, high Al³⁺ mobility, and low available nutrient status at a relatively high average SOC content of 14&ndash;29 g kg^&minus;1 in topsoils. In the felsic region, mid-Holocene carbonate volcanism mitigated soil degradation to some extent. In both geochemical regions, SOC content did not or only weakly positively correlate with clay content and cation exchange capacity. These results emphasize that soil organic matter, as well as clay content, appear to be unreliable indicators for soil fertility in degraded tropical cropland soils. Additionally, no significant improvement of soil fertility or SOC stocks was observed after replanting degraded fields with <em>Eucalyptus </em>monocultures. The estimated lifespan of croplands on hillslopes in our study area, approximately 100&ndash;170 years, underscores the severity of soil degradation for food production and forest protection in the upcoming decades, especially considering that many soils are already approaching this age.

Since the dawn of agitated brewing in the Paleolithic era, effective mixing has enabled efficient reactions. Emerging catalytic chemical polyolefin recycling processes present unique challenges, considering that the polymer melt has a viscosity three orders of magnitude higher than that of honey. The lack of protocols to achieve effective mixing may have resulted in suboptimal catalyst effectiveness. In this study, we have tackled the hydrogenolysis of commercial-grade high-density polyethylene and polypropylene to show how different stirring strategies can create differences of up to 85% and 40% in catalyst effectiveness and selectivity, respectively. The reaction develops near the H2–melt interface, with the extension of the interface and access to catalyst particles the main performance drivers. Leveraging computational fluid dynamics simulations, we have identified a power number of 15,000–40,000 to maximize the catalyst effectiveness factor and optimize stirring parameters. This temperature- and pressure-independent model holds across a viscosity range of 1–1,000 Pa s. Temperature gradients may quickly become relevant for reactor scale-up.

Soil spectroscopy with machine learning (ML) can estimate soil properties. Extensive soil spectral libraries (SSLs) have been developed for this purpose. However, general models built with those SSLs do not generalize well on new ‘unseen’ local data. The main reason is the different characteristics of the observations in the SSL and the local data, which cause their conditional and marginal distributions to differ. This makes the modelling of soil properties with spectra challenging. General models developed using large ‘global’ SSLs offer broad, systematic information on the soil-spectra relationships. However, to accurately generalize in a local situation, they must be adjusted to capture the site-specific characteristics of the local observations. Most current methods for ‘localizing’ spectroscopic modelling report inconsistent results. An understanding of spectroscopic ‘localization’ is lacking, and there is no framework to guide further developments. Here, we review current localization methods and propose their reformulation as a transfer learning (TL) undertaking. We then demonstrate the implementation of instance-based TL with rs-local 2.0 for modelling the soil organic carbon (SOC) content of 12 sites representing fields, farms and regions from 10 countries on the seven continents. The method uses a small number of instances or observations (measured soil property values and corresponding spectra) from the local site to transfer relevant information from a large and diverse global SSL (GSSL 2.0) with more than 50,000 records. We found that with ≤30 local observations, rs-local 2.0 produces more accurate and stable estimates of SOC than modelling with only the local data. Using the information in the GSSL 2.0 and reducing the number of samples for laboratory analysis, the method improves the cost-efficiency and practicality of soil spectroscopy. We interpreted the transfer by analysing the data, models, and soil and environmental relationships of the local and the ‘transferred’ data to gain insight into the approach. Transferring instances from the GSSL 2.0 to the local sites helped to align their conditional and marginal distributions, making the spectra-SOC relationships in the models more robust. Finally, we propose directions for future research. The guiding principle for developing practical and cost-effective spectroscopy should be to think globally but fit locally. By reformulating the localization problem within a TL framework, we hope to have acquainted the soil science community with a set of methodologies that can inspire the development of new, innovative algorithms for soil spectroscopic modelling.

<strong class="journal-contentHeaderColor">Abstract.</strong> <span id="page694"/>Information on soil properties is crucial for soil preservation, the improvement of food security, and the provision of ecosystem services. In particular, for the African continent, spatially explicit information on soils and their ability to sustain these services is still scarce. To address data gaps, infrared spectroscopy has achieved great success as a cost-effective solution to quantify soil properties in recent decades. Here, we present a mid-infrared soil spectral library (SSL) for central Africa (CSSL) that can predict key soil properties, allowing for future soil estimates with a minimal need for expensive and time-consuming wet chemistry. Currently, our CSSL contains over 1800 soil samples from 10 distinct geoclimatic regions throughout the Congo Basin and along the Albertine Rift. For the analysis, we selected six regions from the CSSL, for which we built predictive models for total carbon (TC) and total nitrogen (TN) using an existing continental SSL (African Soil Information Service, AfSIS SSL; <span class="inline-formula"><i>n</i>=1902</span>) that does not include central African soils. Using memory-based learning (MBL), we explored three different strategies at decreasing degrees of geographic extrapolation, using models built with (1) the AfSIS SSL only, (2) AfSIS SSL combined with the five remaining central African regions, and (3) a combination of AfSIS SSL, the remaining five regions, and selected samples from the target region (spiking). For this last strategy we introduce a method for spiking MBL models. We found that when using the AfSIS SSL only to predict the six central African regions, the root mean square error of the predictions (RMSE<span class="inline-formula">_pred</span>) was between 3.85–8.74 and 0.40–1.66 <span class="inline-formula">g kg^−1</span> for TC and TN, respectively. The ratio of performance to the interquartile distance (RPIQ<span class="inline-formula">_pred</span>) ranged between 0.96–3.95 for TC and 0.59–2.86 for TN. While the effect of the second strategy compared to the first strategy was mixed, the third strategy, spiking with samples from the target regions, could clearly reduce the RMSE<span class="inline-formula">_pred</span> to 3.19–7.32 <span class="inline-formula">g kg^−1</span> for TC and 0.24–0.89 <span class="inline-formula">g kg^−1</span> for TN. RPIQ<span class="inline-formula">_pred</span> values were increased to ranges of 1.43–5.48 and 1.62–4.45 for TC and TN, respectively. In general, predicted TC and TN for soils of each of the six regions were accurate; the effect of spiking and avoiding geographical extrapolation was noticeably large. We conclude that our CSSL adds valuable soil diversity that can improve predictions for the Congo Basin region compared to using the continental AfSIS SSL alone; thus, analyses of other soils in central Africa will be able to profit from a more diverse spectral feature space. Given these promising results, the library comprises an important tool to facilitate economical soil analyses and predict soil properties in an understudied yet critical region of Africa. Our SSL is openly available for application and for enlargement with more spectral and reference data to further improve soil diagnostic accuracy and cost-effectiveness.

Abstract. Information on soil properties is crucial for soil preservation, improving food security, and the provision of ecosystem services. Especially, for the African continent, spatially explicit information on soils and their ability to sustain these services is still scarce. To address data gaps, infrared spectroscopy has gained great success as a cost-effective solution to quantify soil properties in recent decades. Here, we present a mid-infrared soil spectral library (SSL) for central Africa (CSSL) that can predict key soil properties allowing for future soil estimates with a minimal need for expensive and time-consuming wet chemistry. Currently, our CSSL contains over 1,800 soils from ten distinct geo-climatic regions throughout the Congo Basin and wider African Great Lakes region. We selected six hold-out core regions from our SSL, augmented them with the continental AfSIS SSL, which does not cover central African soils. We present three levels of geographical extrapolation, deploying Memory-based learning (MBL) to accurately predict carbon (TC) and nitrogen (TN) contents in the selected regions. The Root Mean Square Error of the predictions (RMSEpred) values were between 0.38–0.86 % and 0.04–0.17 % for TC and TN, respectively, when using the AfSIS SSL only to predict the six regions. Prediction accuracy could be improved for four out of six regions when adding central African soils to the AfSIS SSL. This reduction of extrapolation resulted in RMSEpred ranges of 0.41–0.89 % for TC and 0.03–0.12 % for TN. In general, MBL leveraged spectral similarity and thereby predicted the soils in each of the six regions accurately; the effect of avoiding geographical extrapolation and forcing regional samples in the local neighborhood (MBL-spiking) was small. We conclude that our CSSL adds valuable soil diversity that can improve predictions for the regions compared to using the continental scale AfSIS SSL alone; thus, analyses of other soils in central Africa will be able to profit from a more diverse spectral feature space. Given these promising results, the library comprises an important tool to facilitate economical soil analyses and predict soil properties in an understudied yet critical region of Africa. Our SSL is openly available for application and for enlargement with more spectral and reference data to further improve soil diagnostic accuracy and cost-effectiveness.

ABSTRACTSpray drying technology is a suitable process applicable for large-scale production of various advanced materials. Recent laboratory scale research focuses on the development of (1) nano/microstructured electrode materials for next-generation lithium ion batteries with enhanced cell capacity and superior electrochemical performance, (2) transparent materials for laser ceramics, and (3) bioceramics, such as bone substitutes, dental implants, and cements with improved bioactivity and therapeutic effects. This paper reviews the current research progress in these application fields and highlights the importance of laboratory scale spray drying in the corresponding advance materials processing routes.

Abstract trans-Fatty acid contents of lipids extracted from several foods by pressurized fluid extraction were analyzed and compared with those extracted by the conventional methods, Folch method, acid hydrolysis, and Soxhlet method. Extraction efficiency of lipids from commercially available cookies and brown rice by the pressurized fluid extraction was equivalent to those extracted by the other extraction methods. Furthermore, neither oxidative degradation nor new trans isomerization of unsaturated fatty acids during the pressurized fluid extraction could be found. These results suggest that the pressurized fluid extraction could be applicable as a useful lipid extraction method for determining trans-fatty acids in food.