Grazing-incidence X-ray diffraction (GIXD) is a widely used technique for the crystallographic characterization of thin films. The identification of a specific phase or the discovery of an unknown polymorph always requires indexing of the associated diffraction pattern. However, despite the importance of this procedure, only a few approaches have been developed so far. Recently, an advanced mathematical framework for indexing of these specific diffraction patterns has been developed. Here, the successful implementation of this framework in the form of an automated indexing software, named GIDInd, is introduced. GIDInd is based on the assumption of a triclinic unit cell with six lattice constants and a distinct contact plane parallel to the substrate surface. Two approaches are chosen: (i) using only diffraction peaks of the GIXD pattern and (ii) combining the GIXD pattern with a specular diffraction peak. In the first approach the six unknown lattice parameters have to be determined by a single fitting procedure, while in the second approach two successive fitting procedures are used with three unknown parameters each. The output unit cells are reduced cells according to approved crystallographic conventions. Unit-cell solutions are additionally numerically optimized. The computational toolkit is compiled in the form of a MATLAB executable and presented within a user-friendly graphical user interface. The program is demonstrated by application on two independent examples of thin organic films.

tion of homogeneous films onto solid supports. [9] The presence of a surface during framework formation can strongly influence the crystallization process, even inducing the formation of unknown crystal phases. Such unique polymorphs also appear in thin films of purely organic or inorganic compounds [10] and are commonly referred to as substrate-induced polymorphs or thin-film phases. [11] Detailed knowledge of these crystal structures is essential since thin-film phases can have considerably different properties than bulk polymorphs. [12] MOF thin films and specially surface-coordinated MOFs (SURMOFs) [13,14] are known to feature unprecedented polymorphs [15,16] that are not accessible in bulk form and can strongly impact the performance of MOF-based devices. [17,18] Thin-film phases can occur when the MOF building blocks interact with surface groups, such as selfassembled monolayers, [19,15] or when the substrate is covered with a precursor layer (i.e., a metal oxide or hydroxide). [20,21] In addition, the physical state of the reagents that interact with the solid surface (i.e., in solution or the vapor phase) can be a determining factor. [22][23][24] Unfortunately, standard crystal structure solution methods are unsuitable for unknown MOF thin-film phases. In contrast to single-crystal or powder X-ray diffraction (XRD) methods, [25] the number of diffraction peaks observed in MOF thin films is The preparation of thin films is often associated with the appearance of unknown polymorphs, as both the substrate and deposition method can heavily influence crystallization processes. Here, chemical vapor deposition is used to obtain thin films of a copper-isonicotinate (Cu-INA) metal-organic framework (MOF). Starting from copper-based precursor layers (copper oxide and hydroxide), a solid-vapor conversion with vaporized isonicotinic acid in either a dry or humidified atmosphere, yields a new Cu-INA MOF polymorph. It is found that the crystalline order of the precursor layer has a strong impact on the texture of Cu-INA thin films. Furthermore, a novel methodology is introduced to determine the structure of a previously unknown thin-film phase of Cu-INA. Although only a few diffraction peaks are found via synchrotron grazing incidence X-ray diffraction (GIXRD), a triclinic unit cell can be determined, and Patterson functions can be calculated. The latter reveals the position of the copper atoms within the unit cell and the alignment of the INA linkers defining the coordination network structure. This work introduces how the combination of GIXRD data with Patterson functions can be used to identify the structure of an unknown thin-film MOF polymorph.

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Understanding and characterizing the mechanical and structural properties of brain tissue is essential for developing and calibrating reliable material models. Based on the Theory of Porous Media, a novel nonlinear poro-viscoelastic computational model was recently proposed to describe the mechanical response of the tissue under different loading conditions. The model contains parameters related to the time-dependent behavior arising from both the viscoelastic relaxation of the solid matrix and its interaction with the fluid phase. This study focuses on the characterization of these parameters through indentation experiments on a tailor-made polyvinyl alcohol-based hydrogel mimicking brain tissue. The material behavior is adjusted to ex vivo porcine brain tissue. An inverse parameter identification scheme using a trust region reflective algorithm is introduced and applied to match experimental data obtained from the indentation with the proposed computational model. By minimizing the error between experimental values and finite element simulation results, the optimal constitutive model parameters of the brain tissue-mimicking hydrogel are extracted. Finally, the model is validated using the derived material parameters in a finite element simulation.