Micro molding for double-sided micro structuring of SU-8 resist

Advances in micro and nano fabrication technologies for MEMS require high-level measurement techniques with regard to sampling and sensitivity. For this purpose at the Institute of Microtechnology (IMT) highly sensitive piezoresistive 3D force sensors based on SU-8 polymer have been developed. In this paper we present an improved micro fabrication process for a double-sided micro structured design. The sensors are produced by multilayer processing techniques such as UV lithography and coating methods. The double-sided micro structured design demands a photoresist application method which simultaneously features a top side structuring and a casting from a mold. We use a new micro molding process to meet the demands. The micro fabrication technology is described, focusing on the development of the molding structure for shaping of the bottom side and a capable release process for the detachment of the molded structures. The fabrication process of the SU-8 mold layer is optimized to fabricate molding structures with heights from a few μm up to 350 μm. Therefore different SU-8 formulations, namely with classification numbers 5, 25, 50, and 100, have been used. The fundamental limitations for the mold design result from the lithography process, which defines the smallest lateral resolution, and from the characteristics of a molding process, e.g. the impossibility to realize an undercut. To allow for reliable release, the molding structures have to be coated with a sacrificial layer. Silicon nitride is deposited onto the substrate with accompanying monitoring of the deposition temperature during the PECVD process.     

Genotyping and phenotyping of Fusarium graminearum isolates from Germany related to their mycotoxin biosynthesis

Fusarium graminearum is the most important pathogen causing Fusarium head blight (FHB) of small cereal grains worldwide responsible for quantitative and qualitative yield losses. The presence in crops is often associated with mycotoxin contamination of foodstuff limiting its use for human and animal consumption. A collection of isolates of F. graminearum from Germany was characterized genetically and chemically for their potential to produce the B trichothecenes deoxynivalenol (DON) and nivalenol (NIV). Molecular methods with eight PCR assays were implemented based on functional Tri7 and Tri13 genes and on the tri5-tri6 intergenic region to differentiate between chemotaxonomic groups DON and NIV, resulting in a marked majority (61/63) of DON chemotypes. Mycotoxins produced on rice kernels were quantified by means of LC-MSMS including DON, NIV, 3-acetyl-DON (3-ADON), 15-acetyl-DON (15-ADON), DON-3-glucoside, fusarenon X, as well as zearalenone; all of them proving to be present in high concentration among the isolates. All DON-chemotype isolates also produced lower amounts of NIV with the amount being positively correlated (R² = 0.89) to the DON amount. 15-ADON and 3-ADON are reported to be produced simultaneously by the isolates, the former dominating over the latter in all but one isolate. Fungal biomass, was quantified via ergosterol amount on rice. It was used to calculate specific mycotoxin production per biomass of isolates, ranging from 0.104 to 1.815 mg DON mg-1 ergosterol, presenting a Gaussian distribution. Genotype and phenotype characterization revealed discrepancies with respect to mycotoxin production potential of the fungi, i.e. isolates from one chemotype were able to produce mycotoxins from other chemotypes in considerable amounts.     

Generation and application of hyperspectral 3D plant models: methods and challenges

Hyperspectral imaging sensors have been introduced for measuring the health status of plants. Recently, they also have been used for close-range sensing of plant canopies with a highly complex architecture. However, the complex geometry of plants and their interaction with the illumination setting severely affect the spectral information obtained. Furthermore, the spatial component of analysis results gain in importance as higher plants are represented by multiple plant organs as leaves, stems and seed pods. The combination of hyperspectral images and 3D point clouds is a promising approach to face these problems. We present the generation and application of hyperspectral 3D plant models as a new, interesting application field for computer vision with a variety of challenging tasks. We sum up a geometric calibration method for hyperspectral pushbroom cameras using a reference object for the combination of spectral and spatial information. Furthermore, we show exemplarily new calibration and analysis methods enabled by the hyperspectral 3D models in an experiment with sugar beet plants. An improved normalization, a comparison of image and 3D analysis and the density estimation of infected surface points underline some of the new capabilities gained using this new data type. Based on such hyperspectral 3D models the effects of plant geometry and sensor configuration can be quantified and modeled. In future, reflectance models can be used to remove or weaken the geometry-related effects in hyperspectral images and, therefore, have the potential to improve automated plant phenotyping significantly.     

Quantifying empirical relations between planted species mixtures and canopy reflectance with PROTEST

Mapping plant species composition of mixed vegetation stands with remote sensing is a complicated task. Uncertainties may arise from similar spectral signatures of different plant species as well as from variable influences of prevailing plant states (e.g., growth stages, vigor, or stress levels). Despite these uncertainties, empirical approaches may often be able to take up the challenge. However, their performance is likely to be affected by the temporal variability of empirical relations between reflectance and plant species composition. To assess some aspects of this temporal variability, we performed a greenhouse study. Three mixed stands of grassland species were planted with defined spatial variation in species proportions. The canopy reflectance of these mixed stands was measured with a field spectrometer over a period of three months. Confounding external influences on plant states apart from maturation were minimized.The suitability of canopy reflectance and derivative reflectance to draw conclusions on differences in qualitative species mixtures between the stands was tested with a classification approach (Spectral Angle Mapper, SAM). Procrustean randomization test (PROTEST), which is to our knowledge new to the field of remote sensing, was applied in combination with Isometric Feature Mapping to quantify the spectral variation caused by within-stand spatial variation in species proportions. Model fits in both analyses increased with progressing plant development; further, utilization of derivative reflectance improved the model fits. Regardless of the within-stand variation, SAM enabled a successful discrimination of the three stands with an average overall accuracy of 85% (reflectance) and 92% (derivative reflectance). In PROTEST analysis, spatial variation in reflectance was successfully related to within-stand variation in species proportions. However, observed influences of variable growth stages and health states on these relations were considerable. The temporal variation of these relations (r = 0.27-0.73 for reflectance and 0.48-0.73 for derivative reflectance) was quantified for the first time under controlled conditions.