Open Access

Collaboration between civil society and state authorities in preventing radicalization has often been met with scepticism in academic debates, particularly due to concerns about power asymmetries, structural inequalities, and the risk of co-optation. While existing literature offers critical insights into national institutional frameworks and their implications for civil society, it frequently overlooks the local dynamics that shape prevention practices on the ground. The paper addresses this gap by examining cooperation between municipal officials and civil society organizations (CSOs) in local preventive efforts, focusing on the relatively understudied case of Germany. Specifically, this pilot study centres on the perspectives of municipal actors rather than directly capturing CSO voices. Drawing on 14 semi-structured interviews with municipal officers in Germany, it explores how collaboration is influenced not only by their formal mandates, but also by individual professional trajectories and locally embedded networks. The findings show that local cooperation in the prevention field is more contingent and negotiated than commonly acknowledged. Enabling factors and persistent challenges - such as lack of trust, divergent expectations, and the limited capacities of smaller CSOs - affect the quality and sustainability of these partnerships. By foregrounding lived experience and first-person perspectives of municipal officials, the paper advances a more grounded and relational understanding of state-civil society collaboration, one that accounts for the hybrid arrangements and interpersonal dynamics through which preventive efforts unfold at the local level.

Silicon nanowire field emitters were fabricated on top of microtubes on a silicon chip (dimensions including contact pads: 8 × 8 mm). There are 2437 microtubes (diameter: 8 μm, height: 30 μm, spacing: 40 μm) in the array over an area of 3.38 mm2. A process was developed to create an integrated metal gate for the extraction electrode. This involved coating the emitters with a uniform benzocyclobutene layer and fabricating a titanium/nickel extraction gate using lift-off. A plasma etching process was used to selectively remove the benzocyclobutene anisotropically around the microtubes, ensuring the preservation of the emitters and the extraction gate. The design reduces the capacitance between the cathode and gate and maximizes the length of the leakage paths between the cathode and gate. This reduces the overall risk of discharges between the two electrodes as well as the energy stored in the system for a given extraction voltage. The electron sources demonstrated transmission rates exceeding 96%, with currents reaching 0.4 mA at extraction voltages of 250 V for several hours under voltage-controlled operation. Investigations after the measurements showed that isolated discharges destroyed individual emitters during operation, but the electron sources remained functional. Consequently, these cathodes demonstrated resilience to failure caused by discharge-related damage due to the low capacity of the system. At higher voltages (300 V) and currents (2 mA); however, these samples exhibited catastrophic arcing, as at these voltages, the energy stored in the capacitance was large enough to result in the destruction of larger areas of the arrays and the formation of conductive paths between the cathode and the gate.

We introduce a compact model for the prediction of performance figures of CMUTs prospectively used in airborne applications such as flow meters. The model comprises both a mechanical and an electrical approach resulting in a set of four parameters: resonance frequency, Q-factor, static capacitance and pull-in voltage. A small set of exemplary CMUTs has been intensively characterized by means of LDV and LCR measurements, eventually confirming the precision of the proposed prediction model.

This work provides insight into the potential of camera-based surface plasmon resonance sensors when utilising the spatial sensor by modulating the sensor response through lateral modification of surface parameters or input illumination. These modifications have to be coupled with adequate signal processing. Modelling the sensor response using Fresnel formulae allows for accurate representation of the sensor surface state, increasing confidence in measured values. Alternatively, data-based modelling approaches can be utilised which eliminate the need for approximation of a physical model to observed data but are limited by the data they are trained on. It has been explored how, especially, deep learning models that are specialised in image processing (CNNs) can be utilised for qualitative or quantitative assessment of analytes with the help of a receptor array on an SPR surface. Notably, this approach demonstrates strong generalization capabilities, performing effectively on unseen sensors used for the same task. Preliminary experiments indicate that performance enhancements are feasible when data from different points in time during binding or debinding are used during processing. In conclusion, deep learning-assisted spatial SPR sensors hold immense potential for diverse applications, including online quality control, anomaly detection, and biofouling quantification. However, realizing this potential hinges on carefully designed experiments that yield high-quality data. Furthermore, incorporating orthogonal data sources is crucial for more precise determination of the surface state during calibration. Suitable surface-sensitive techniques include Raman spectroscopy, ellipsometry, and impedance spectroscopy. Coupling these methods can improve knowledge on the observed state during training. This information together with the presented methods can simplify sensor development and improve performance. Diese Arbeit gibt einen Einblick in das Potenzial kamerabasierter Oberflächenplasmonenresonanzsensoren, wenn der räumliche Sensor durch die Modulation von Sensorreaktionen durch laterale Modifikation der Oberflächenparameter oder der Eingangsbeleuchtung genutzt wird. Diese Modifikationen erzeugen eine komplexe Sensorantwort und müssen daher mit einer geeigneten Signalverarbeitung gekoppelt werden. Die Modellierung der Sensorantwort mit Hilfe von Fresnel-Formeln ermöglicht eine genaue Bestimmung des Sensoroberflächenzustands und erhöht so die Konfidenz in gemessene Werte. Alternativ können datenbasierte Modellierungsansätze angewandt werden, die eine Annäherung eines physikalischen Modells an aufgenommene Daten ersetzen können, aber durch die Daten, auf denen sie trainiert werden, begrenzt sind. Es wurde untersucht, wie insbesondere tiefe neuronale Netze, die auf die Bildverarbeitung spezialisiert sind (CNNs), für die qualitative oder quantitative Bewertung von Analyten mit Hilfe eines Rezeptor-Arrays auf einer SPR-Oberfläche eingesetzt werden können. Insbesondere zeigt dieser Ansatz starke Generalisierungsfähigkeiten, die auch bei unbekannten (unkalibrierten) Sensoren, die für dieselbe Aufgabe verwendet werden, wirksam sind. Vorläufige Experimente deuten darauf hin, dass Genauigkeitssteigerungen möglich sind, wenn bei der Verarbeitung Daten von verschiedenen Zeitpunkten während der Bindung verwendet werden. Zusammenfassend lässt sich sagen, dass räumliche SPR-Sensoren mit Hilfe von tiefen neuronalen Netzen immenses Potenzial für verschiedene Anwendungen haben, darunter Online-Qualitätskontrolle, Erkennung von Anomalien und Quantifizierung von Biofouling. Die Realisierung dieses Potenzials hängt jedoch von sorgfältig geplanten Experimenten ab, die qualitativ hochwertige Daten liefern. Darüber hinaus ist die Einbeziehung orthogonaler Datenquellen entscheidend für eine genauere Bestimmung des Oberflächenzustands während der Kalibrierung. Zu geeigneten oberflächensensitiven Techniken gehören Raman-Spektroskopie, Ellipsometrie und Impedanzspektroskopie. Die Kopplung dieser Methoden kann das Wissen über den beobachteten Zustand während des Trainings verbessern. Diese Informationen können zusammen mit den vorgestellten Methoden die Sensorentwicklung vereinfachen und die Leistung des Sensors in der Anwedung verbessern.