Dermot Brabazon

An artificial neural network (ANN)-based digital twin feed controller, aiming to optimise bioprocess performance, was implemented in fed-batch bioprocesses of Lactobacillus rhamnosus LRH30 for the production of biomass. The online data from several fed-batch bioprocesses in dairy-based cultivations with L. rhamnosus were utilised to develop an ANN that predicts the biomass and growth rate of L. rhamnosus in the process. This growth rate estimator was first validated through unit testing before being applied in model predictive control to select the best control action to drive feed addition to the process. The controller was implemented in fed-batch bioprocesses and the growth rate was estimated in real time, leading to the highest cell dry weight biomass concentrations obtained for L. rhamnosus LRH30 of 36.23 g/L in this study, with growth-rate overshoot and increased feed addition also contributing to the biomass accumulation despite imperfect setpoint tracking. The dose-dependent effect of L. rhamnosus LRH30 samples on cytokine secretion from murine macrophages was further explored, and an anti-inflammatory effect was found at a dosage of 10 mg/mL of bacterial cell dry weight. These experiments demonstrate the first steps in the implementation phase that will ultimately lead to a better controlled bioprocess with higher yields of L. rhamnosus biomass. Future research will focus on the continuous development of the ANN through the incorporation of more datasets, while simultaneously exploring the functional potential of L. rhamnosus LRH30.

Breathomics has advanced into a non‐invasive diagnostic strategy capable of detecting disease‐specific volatile organic compounds (VOCs) linked to altered metabolic and inflammatory pathways. Since the first identification of over 200 VOCs in human breath, research has shown strong diagnostic potential across diabetes, lung cancer, chronic obstructive pulmonary disease, asthma, pulmonary arterial hypertension, tuberculosis, renal disease, liver cirrhosis, gastric cancer, and Helicobacter pylori infection (all covered herein). Key biomarkers such as acetone (elevated to >2 ppm in diabetes), 1‐octene (lung cancer), ammonia (>1500 ppb in chronic kidney disease), and nitric oxide (reduced in pulmonary arterial hypertension, PAH) demonstrate clinically relevant discrimination; for example, Au‐nanoparticle sensors have been shown to achieve a 92% accuracy distinguishing PAH patients from controls. Progress is driven by nanomaterials including graphene, carbon nanotubes, noble‐metal nanoparticles, metal‐organic frameworks, MoS ₂ , and metal oxides such as SnO ₂ and ZnO, which offer high sensitivity and response. Emerging fabrication techniques such as screen printing, inkjet printing, and aerosol jet printing enable scalable production of chemiresistive, colorimetric, electrochemical, and SERS‐based sensors. Combined with AI‐driven pattern recognition, these technologies position breath‐based diagnostics as a realistic pathway toward portable, real‐time, and affordable disease monitoring.

The aim of this special issue is to present recent developments and technological capabilities of additive manufacturing. Pre-processing, in-situ processing, and post-processing are important stages that impact the final properties of the produced parts. Experimental results, numerical modelling methods, data analytics, and optimisation of additive manufacturing processes are considered in this edition. The book will be helpful to researchers and practitioners in additive technologies.

This study provides a comprehensive investigation into the effects of different scanning parameter combinations—specifically scanning speed and hatch distance—on the material properties of IN939 fabricated using the powder bed fusion-laser beam (PBF-LB) process under a constant volumetric energy density (VED). Despite the fixed VED, the fabricated samples experienced different thermal cycles, resulting in distinct microstructural features and corresponding variations in material performance. In-situ infrared monitoring indicated that the sample with the narrowest hatch distance and highest scanning speed (Sample 1) reached the highest normalized temperatures with intense heat accumulation, whereas wider hatch distances (Sample 3) promoted lower and more stable temperature distributions. The results revealed that the intermediate parameter set (Sample 2) achieved the highest relative density (99.29%) and the lowest surface roughness. In contrast, both the narrowest and widest hatch spacing combinations promoted increased porosity, primarily consisting of lack-of-fusion (LoF) and gas pores. Electron backscatter diffraction (EBSD) analysis showed that the area-weighted average grain size increased from 29.5 µm to 36.7 µm as the hatch distance increased. Texture analysis indicated generally weak crystallographic texture development, with only slight intensification of //BD and //BD components, attributed to the 67 ^o rotation strategy. Furthermore, the microhardness values demonstrated negligible variation across the samples, ranging from 356.7 ± 14.3 HV1 to 360.1 ± 10.5 HV1. This limited variation indicates that the strengthening behavior was predominantly governed by the combined influence of defect density and matrix–defect interactions, rather than being directly correlated with grain size.

Nickel-Titanium (NiTi) shape memory alloy specimens were fabricated using Laser Powder Bed Fusion (L-PBF) to investigate the influence of processing parameters on localized mechanical response. Rather than relying solely on bulk-averaged properties, this study implements high-density nanoindentation mapping (up to 2500 indents per sample) to resolve spatial variations in Young’s modulus, hardness, and energy dissipation at the microscale. The mechanical maps were correlated with microstructural features identified by SEM and room-temperature XRD, enabling assessment of phase-dependent heterogeneity associated with B19′ martensite, retained B2 austenite, and defect-rich regions. While volumetric energy density (VED) was used as a comparative parameter, the results demonstrate that identical VED values can yield distinct local mechanical behavior due to differences in scan speed and hatch spacing. Intermediate energy input conditions promoted improved densification and reduced porosity, whereas insufficient or excessive energy input introduced mechanical variability linked to melt pool instability. Phase fractions were estimated using XRD peak integration and interpreted in conjunction with mechanical mapping. The findings establish a structure–property framework for understanding microscale mechanical heterogeneity in L-PBF NiTi alloys relevant to functional applications.

The elastocaloric (eC) effect, which harnesses the latent heat released during stress‐induced transformations of superelastic shape memory alloys (SMAs), offers a promising pathway toward solid‐state, environmentally friendly refrigeration technologies. However, the advancement of eC devices is constrained by the limited heat transfer surface area between SMAs and heat transfer fluids, as well as the high mechanical work input relative to the extracted latent heat. Among available SMAs, nickel titanium (NiTi) alloys are the most widely commercialized and exhibit strong potential for eC applications, yet their poor machinability and fabrication challenges hinder widespread implementation. Additive manufacturing (AM) provides a solution by enabling layer‐by‐layer fabrication of NiTi with complex geometries, thereby enhancing surface area and reducing work input through lattice structures. This review summarizes recent progress in AM‐fabricated NiTi for eC applications, with an emphasis on components produced by laser powder bed fusion (LPBF) and directed energy deposition (DED) techniques using both wire and powder feedstocks. Finally, future directions and opportunities for integrating AM NiTi into practical eC devices are discussed.