The electrochemical conversion of CO2 to CO in membrane electrode assembly (MEA) electrolyzers using gas diffusion electrodes (GDEs) offers a sustainable and scalable pathway for carbon utilization. Here, we present a one-step atomic layer deposition (ALD) approach to prepare ZnO-based GDEs with tunable loadings and high selectivity toward CO. Increasing the number of ALD cycles raises the ZnO loading but progressively reduces the pore accessibility within the GDE. An optimal balance is achieved at 200 ALD cycles, delivering a peak CO faradaic efficiency (FECO) of 88% and a full-cell energy efficiency of 38% at −100 mA cm−2. Crucially, the scalability of ALD is demonstrated through stable long-term testing, achieving 85% FECO in a 5 cm2 MEA after 30 h, and 80% FECO in a 100 cm2 MEA after 24 h. These results establish ALD as an effective and versatile strategy for fabricating high-performance ZnO electrodes for CO2 electrolysis.
Non-invasive brain–computer interfaces (BCIs) aim to restore communication by decoding intended messages directly from neural activity, even without audible speech. However, evidence remains limited that non-invasive signals can support subject-held-out decoding of predefined semantic intentions from covert inner speech. We investigate whether a semantic-aware multimodal framework can generalize from overt spoken commands to covert inner speech using scalp electroencephalography (EEG), auxiliary electromyography (EMG), and overt-speech audio supervision. Ten healthy participants produced four everyday commands (water, toilet, light, pain) in overt and covert phases. Overt trials included synchronized EEG, EMG, and audio, whereas covert trials were evaluated without audio using EEG with auxiliary EMG. The proposed model learns a shared latent representation from overt multimodal supervision and covert training trials from non-held-out subjects, combining supervised contrastive learning, ArcFace classification, semantic prototype alignment, and overt–covert regularization. Evaluation follows a subject-held-out covert protocol with target-subject overt calibration: covert trials from the held-out participant are never used for training, model selection, or calibration. Across subjects, the model achieves a mean overt-validation accuracy of 0.54 and a mean covert-test accuracy of 0.42 on the four-class task, above the 0.25 chance level. Latent-space and semantic-retrieval analyses indicate that the learned representations retain class-related structure and alignment with text-based semantic prototypes. Ablations reveal that multimodal overt information supports within-subject decoding and representational analysis, whereas EEG-only overt supervision is more robust under held-out-subject covert transfer. These results identify subject variability, modality transfer, and calibration as key challenges for future semantic BCIs.
Springs represent ecotones between groundwater and surface freshwater habitats. Recent research suggested that springs can be more important than expected for stygobiont (i.e., adapted to live in groundwater) species, still information on habitat exploitation and activity of stygobionts in springs is far from complete. The aims of this study are: (i) to identify environmental factors promoting the exploitation of ecotone habitats by the stygobiont shrimp Troglocaris planinensis, commonly found in spring environments in northeastern Italy; and (ii) to experimentally evaluate whether this species exhibits differential behavioral responses to light and to subterranean and surface predator cues based on its habitat of origin (spring versus cave). From June 2020 to January 2025, we started multiple day and night surveys of T. planinensis in 64 springs of the Classic Karst (NE-Italy). Each site has been characterized with respect to abiotic and biotic features. In the laboratory, shrimps from both cave and spring populations were tested to assess behavioral differences in response to light stimuli and predatory cues, as potential adaptations to the contrasting conditions of their respective habitats. In springs, T. planinensis density reached up to 116 shrimps/m2, with significantly higher counts at night and lower densities at sites with greater fish predator abundance. Laboratory tests showed that predator cues, but not light exposure, influenced shrimp behavior regardless of their cave or spring origin. This study suggests that stygobiont crustaceans can represent a significant portion of biomass in surface waters and exploit these environments in response to changes in abiotic and biotic conditions and stimuli. However, further research is necessary to determine how stygobionts perceive surface conditions and how ecotonal pressures may drive adaptive shifts in typically groundwater-dwelling animals.
Ti–6Al–2Sn–4Zr–2Mo (Ti6242) is a near-α titanium alloy developed for high-temperature applications, yet its tribological behavior in additively manufactured conditions remains insufficiently understood. This study aimed to investigate the influence of build orientation on the microstructure and tribological performance of Ti6242 components produced via electron beam powder bed fusion (EB-PBF) across temperatures from room temperature to 450 °C. The outcomes revealed that a higher fraction of equiaxed grains was observed in the horizontally oriented samples compared to the vertically oriented ones, reflecting the influence of build orientation on the microstructural anisotropy. Abrasive wear emerged as the predominant mechanism in EB-PBF-Ti6242 and Al2O3 ball interactions under all tested conditions. The tribological behavior at 450 °C exhibited characteristics comparable to those at room temperature, with a friction coefficient of approximately 0.5 and a wear volume about 10
Augmented Reality (AR) is increasingly being adopted in surgical procedures. Identifying accurate and reliable tracking systems can enhance the effectiveness of AR assisted surgery. The study presents a benchmarking platform to evaluate the performance of different optical surgical tools tracking systems for AR applications, addressing the need for standardized comparison of tracking systems’ accuracy, repeatability, and reliability. A custom-built, cost-effective benchmarking platform was developed, and different quantitative evaluation metrics were employed to assess performances of Marker-Based (MB) tracking systems. Two types of measurements were performed: static, where metrics such as Target Registration Error (TRE) and tip stability as a measure of jitter were estimated, and dynamic, involving a measure of the deviation between the tracked tooltip trajectory and its GT. Three AR MB-tracking methods were tested: method 1, mono-RGB sensor tracking a planar image target; method 2, RGB-depth sensor tracking passive spherical markers; and method 3, mono-IR sensor tracking IR active markers. Significant performance differences were observed. Method 3 achieved the lowest TRE value of 3.578 ± 2.836 mm. Method 1 exhibited the best tip stability with a jitter value of 1.435 ± 0.824 mm. Method 2 obtained the minimum trajectory distance in dynamic tests. The benchmarking platform demonstrated its effectiveness in evaluating and comparing different AR tracking methods. This study enables the optimization of tracking system selection for use in the operating room, and facilitating the integration of AR as a supportive technology in surgical practice.
