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3D objects with integrated electronics are produced using an additive manufacturing approach relying on multiphoton fabrication (direct laser writing, (DLW)). Conducting polymer-based structures (with micrometer-millimeter scale features) are printed within exemplar matrices, including an elastomer (polydimethylsiloxane, (PDMS)) have been widely investigated for biomedical applications. The fidelity of the printing process in PDMS is assessed by optical coherence tomography, and the conducting polymer structures are demonstrated to be capable of stimulating mouse brain tissue in vitro. Furthermore, the applicability of the approach to printing structures in vivo is demonstrated in live nematodes (Caenorhabditis elegans). These results highlight the potential for such additive manufacturing approaches to produce next-generation advanced material technologies, notably integrated electronics for technical and medical applications (e.g., human-computer interfaces).

In-line terahertz pulsed imaging has been utilized to measure the film coating thickness of individual tablets during the coating process in a production-scale pan coater. A criteria-based waveform selection algorithm (WSA) was developed to select terahertz signals reflected from the surface of coating tablets and determine the coating thickness. Since the WSA uses many criteria thresholds to select terahertz waveforms of sufficiently high quality, it could reject some potential candidate tablet waveforms that are close but do not reach the threshold boundary. On the premise of the availability of large datasets, we aim to improve the efficiency of WSA with machine learning. This article presents a recurrent neural network approach to optimize waveform selection. In comparison with the conventional method of WSA, our approach allows more than double the number of waveforms to be selected while maintaining excellent agreement with offline thickness measurements. Moreover, the processing time of waveform selection decreases so that it can be applied for real-time coating monitoring in the pharmaceutical industry, which leads to more advancements in quality control for pharmaceutical film coating.

Seed germination and uniform plant stand in the field are the most critical crop growth stages determining the final yield. Pea (Pisum sativum L.) seeds production is often hampered due to the seed dormancy caused by the hard seed coat. Such effect is mainly attributed to poor or uneven germination and unsynchronised seedling emergence. Understanding the time course of water intake and several critical germination indicators can reveal many features of seed germination such as rate and uniformity. This paper used optical coherence tomography (OCT), a noninvasive and cross‐sectional imaging technique, to monitor the inner structural changes throughout the germination process. A sequence of cross‐sectional OCT images of pea (P. sativum L.) seeds, together with additional microscopic optical images, was recorded continuously and in situ for over 40 h. OCT and microscopic images revealed the changes in the internal structure and the external shape of the pea seeds during germination, respectively. It was found that the cross‐sectional OCT images helped to identify the critical indicators distinguishing the different phases of germination pea seeds. Therefore, the presented OCT approach offers a fast and nondestructive way to precisely measure the structural indicators in different germination phases.