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Smart, responsive materials are required in various advanced applications ranging from anti‐counterfeiting to autonomous sensing. Colloidal crystals are a versatile material class for optically based sensing applications owing to their photonic stopband. A careful combination of materials synthesis and colloidal mesostructure rendered such systems helpful in responding to stimuli such as gases, humidity, or temperature. Here, an approach is demonstrated to simultaneously and independently measure the time and temperature solely based on the inherent material properties of complex colloidal crystal mixtures. An array of colloidal crystals, each featuring unique film formation kinetics, is fabricated. Combined with machine learning‐enabled image analysis, the colloidal crystal arrays can autonomously record isothermal heating events — readout proceeds by acquiring photographs of the applied sensor using a standard smartphone camera. The concept shows how the progressing use of machine learning in materials science has the potential to allow non‐classical forms of data acquisition and evaluation. This can provide novel insights into multiparameter systems and simplify applications of novel materials. The optical response of multicomponent photonic crystals contains intricate information regarding a sample's thermal history. In this work, a reproducible sensor fabrication method coupled with fast data acquisition via digital photography is presented. Machine learning assisted evaluation allows smartphone‐based sensing of thermal events. Time and temperature can thereby be obtained independently and without specialized equipment.

This thesis comprises four projects that present new accomplishments in colloid-based materials. The common theme throughout all projects is the realization of superordinate gradients in colloidal crystals and glasses, which provide new ways of making use of the photonic properties. Spherical polymer particles were used as a model system, and the two main parameters altered on a single-particle level were the size and the thermal properties. Complexity in the form of defined gradients was introduced by controlling and adjusting the spatial distribution in mixed systems. This was enabled by a range of new synthesis and self-assembly procedures for size and composition gradients in photonic colloidal assemblies. The focus here lies on establishing new concepts, and while every project concludes with an application, I expect these to be applied in neighboring fields in the future as well.The first project began with developing a novel coating procedure for lateral composition gradients in mixed colloidal crystals. This was achieved using a dual-syringe pump method, similar to dip-coating, and applying two particle types of the same size. The two particles differed regarding their thermal properties, which resulted in film formation kinetics that depended on the composition of the colloidal mixture. This aspect was studied first on discrete samples with defined mixtures and then transferred to gradient colloidal crystals. Since the film formation entailed a visible loss of structural coloration, this novel type of photonic crystal could be used as a time-temperature integrator with a very simple optical readout.The second project was a follow-up and also applied mixed colloidal crystals as timetemperature integrators with a more detailed readout. The system’s complexity was increased using four different particle types that could co-crystallize. Much faster and automated sample preparation was necessary here. This was made possible by a contact printing procedure that prepared several hundred identical samples comprising colloidal crystal arrays. The samples were characterized in-situ during the film formation with a smartphone camera to showcase the user-friendliness of this system. An artificial neural network could be developed that took pictures of a sample with an unknown thermal history as input and provided both time and temperature independently as output.The third project also applied the dual syringe pump method for lateral gradients in colloidal assemblies. However, the objective here was not to prepare a sensor but to use the composition gradient as a screening platform. Several binary combinations of particles with different sizes were applied, and the optical properties were shown to depend strongly on the diameter ratio and composition. Gradient samples, coupled with scanning microspectroscopy along the coating direction, provided large data sets. These described the optical properties of bidisperse mixtures over the entire composition range with a high resolution yet required only a few samples. An adjustable photonic stop band, as well as optimum light scattering, could, thereby, be distinguished.The fourth project in this line of functional photonic gradients also aimed to tailor the optical properties. Here, the gradient was directly used to increase the reflectivity in the visible range. Instead of a lateral composition gradient, the objective was to prepare a thin, particulate film with a continuous particle-size gradient orthogonal to the surface.

Genomic translocation events frequently underlie cancer development through generation of gene fusions with oncogenic properties. Identification of such fusion transcripts by transcriptome sequencing might help to discover new potential therapeutic targets. We developed TRUP (Tumor-specimen suited RNA-seq Unified Pipeline) (https://github.com/ruping/TRUP), a computational approach that combines split-read and read-pair analysis with de novo assembly for the identification of chimeric transcripts in cancer specimens. We apply TRUP to RNA-seq data of different tumor types, and find it to be more sensitive than alternative tools in detecting chimeric transcripts, such as secondary rearrangements in EML4-ALK-positive lung tumors, or recurrent inactivating rearrangements affecting RASSF8.