Niederrhein University of Applied Sciences

Upconversion materials (UCm)-assisted near-infrared (NIR) photopolymerization (UCAP) has gained increasing attention for its ability to convert NIR light into UV-vis light to initiate polymerization. In this perspective, we first present the mechanisms of stepwise multiphoton absorption in UCm and the composition of UCAP, emphasizing the design of efficient photoinitiation systems. The current state of UCAP research is reviewed across different polymerization mechanisms, including free-radical, cationic, and reversible-deactivation free-radical polymerization. In addition, key factors affecting UCAP efficiency─such as UC material design, photoinitiator properties, laser power, and formulation parameters─are systematically analyzed. UCAP shows broad potential in advanced coatings, multiscale additive manufacturing, and biomedical engineering. Despite being in its early stages and facing many challenges, UCAP provides a transformative pathway for designing and fabricating next-generation functional materials.

After anterior cruciate ligament reconstruction (ACLR), patients have been found to have reduced plantar sensation, which may result in reduced afferent input to the central nervous system and thus contribute to motor deficits. Textured surfaces are thought to have a beneficial neurosensory effect. The aim of this cross-sectional study was to compare plantar sensation and leg muscle activity while stepping on different textured surfaces between patients after ACLR and healthy controls.Plantar cutaneous thresholds to light touch were measured in 10 patients at least 6 months after ACLR and in 10 healthy controls. Patients or controls were asked to step forward on the centre of a force plate with the affected (ACLR) or randomly assigned (healthy controls) leg and maintain the single-legged stance for 10 seconds (floor condition). They were instructed to perform the same task on a balance board with a textured surface, the same balance board with a smooth surface, and a balance pad in random order. Muscle activity of four leg muscles was recorded using surface electromyography. The significance of differences in plantar sensation and mean muscle activity within three time frames between and within ACLR patients and healthy controls was analysed using non-parametric statistical tests with Bonferroni correction (p < 0.05).There were no significant differences between patients with ACLR and healthy controls in plantar sensation and muscle activity for all unstable surface conditions (p > 0.05). Friedman tests revealed significant differences in the activities of all muscles between surface conditions at the first peak of the vertical ground reaction force (vGRF) after the rapid increase in the force-time curve (transition from early lifting phase to late lifting phase) within both groups (p < 0.01). Post-hoc Wilcoxon signed-rank tests showed significantly altered activity for most muscles between the smooth and textured balance board conditions only at the first vGRF peak (p ≤ 0.01) in both patients and healthy controls.Although plantar sensation and muscle activity did not differ between patients with ACLR and healthy controls, altered muscle activity in both groups, especially during the transition from the early to the late lifting phase of stepping on a textured unstable surface, may indicate an acute change in the afferent input of plantar mechanoreceptors in response to the surface stimulus. In addition, it may indicate an acute change in motor output caused by a beneficial neurosensory effect. This effect should be considered with caution due to the small sample size.

Process analytical technology (PAT) plays a key role in enhancing the efficiency and resulting quality of chemical processes. Hitherto, suitable methods enable real-time analysis and provide meaningful and robust data and models. Spectroscopic techniques, e.g., vibrational or absorption, offer in situ insight into reaction progress but may require advanced data analysis to interpret the complex spectra. In this study, inline and online monitoring by spectroscopic techniques was applied to a Schiff base formation as an illustrative example and enhanced by data analysis. Two-dimensional heterocorrelation spectroscopy was used to identify and select relevant spectral regions. The results allowed data reduction and data fusion for model building and process description. First, qualitative process representation was achieved through principal component analysis (PCA). Quantitative prediction models were then developed using multivariate curve resolution-alternating least squares (MCR-ALS) with evolving factor analysis (EFA), partial least squares (PLS), and supporting vector regression (SVR) analysis. The low- and mid-level data fusion based on the spectroscopic data and the multivariate models enabled the development of accurate predictive models, with the best prediction achieved by PLS models from low-level data fusion. The results demonstrate the strength of the combination of spectroscopy, multivariate data analysis, and—in the field of PAT rarely exploited—heterocovariance transformation and data fusion to obtain process understanding and reaction models. The methodology may provide further contributions to automatable process control in industrial applications.Graphical Abstract