Search Results (63)

An ultrafast laser system combined with an optical delay line allowed ablation and in-scription at 1 kHz and 1 GHz pulse burst within transparent polyimide films. The two-photon-induced absorption results in clean surface ablation, while inscription results in polymer decomposition, creating carbonised regions within the polymer. Three pulse bursts at 1 GHz increased the observed coupling to the material significantly. Modified regions (with linewidths down to a few microns) were investigated using optical microscopy, white light interferometry, SEM and Raman spectroscopy, supporting the increasing carbon density relative to the pristine polymer. As depth of field was only a few microns at high NA, 3D micro-structuring was achieved. Polymer decomposition produces gaseous products, resulting in internal stress and thus affecting inscription fidelity. An inscribed subsurface electrode with dimensions of 5 mm × 0.3 mm × 3 μm connected to conducting vias had a resistance of R = 10.6 ± 0.2 kΩ, along with resistivity of ρ ~ 0.19 Ω cm; hence, it had DC conductivity, σ ~ 5.3 Scm⁻¹. This conductivity is similar to that of bulk graphite and could well form the basis of future flexible sensors, demonstrating single-step 3D subsurface inscription of carbon or laser-induced graphene structures. Full article

The development of photonic-integrated circuits (PICs) for data communication, sensing, and quantum computing is hindered by the high complexity and cost of traditional fabrication methods, which rely on expensive equipment, limiting accessibility for research and prototyping. This study introduces a Direct Laser Writing (DLW) system designed as a low-cost alternative, utilizing an XY platform for precise substrate movement and an optical system comprising a collimator and lens to focus the laser beam. Operating on a single layer, the system employs SU-8 photoresist to fabricate polymer-based structures on substrates such as ITO-covered glass. Preparation involves thorough cleaning, spin coating with photoresist, and pre- and post-baking to ensure material stability. This approach reduces dependence on costly infrastructure, making it suitable for academic settings and enabling rapid prototyping. A user interface and custom slicer process standard .dxf files into executable commands, enhancing operational flexibility. Experimental results demonstrate a resolution of 10 µm, with successful patterning of structures, including diffraction grids, waveguides, and multimode interference devices. This system aims to transform PIC prototype fabrication into a cost-effective, accessible process. Full article

Background: Differences in total daily energy expenditure (TDEE) across sports, sex, and skill level support the need for sport- and athlete-specific energy intake recommendations. The purpose of the current review was to examine TDEE and related markers of energy expenditure across field-based team sports. A secondary aim was to evaluate physical activity levels (PAL), calculated as TDEE divided by resting metabolic rate (RMR), and their utility in estimating energy needs within team sports. Methods: The review was limited to studies that included the field-based team sports of rugby or soccer and reported energy expenditure data using doubly labeled water (DLW). A literature review identified 11 studies meeting criteria. Weighted means (Xw) and standard deviations (SDw) were calculated for each variable when pooled across each sport category. Results: Rugby (4417 ± 654 kcal·d⁻¹) had a higher average TDEE than soccer (3157 ± 331 kcal/day; p < 0.001). When normalized to body mass, rTDEE was similar between sports (rugby: 49.5 ± 1.3 kcal·kg⁻¹·day⁻¹; soccer: 49.3 ± 1.8 kcal·kg⁻¹·day⁻¹; p = 0.967). PAL values were significantly higher in rugby (2.2 ± 0.4) compared to soccer (1.7 ± 0.2; p = 0.004). RMR was also greater in rugby (2136 ± 322 kcal·d⁻¹) compared to soccer (1835 ± 208 kcal·d⁻¹; p = 0.04). Conclusions: Rugby athletes exhibited higher TDEE values than soccer athletes, reflecting greater absolute energy demands. However, similar relative TDEE values suggest that differences in body size and composition likely contribute to the observed differences in absolute expenditure. These findings underscore the importance of individualized nutrition strategies within team sports and highlight PAL as a useful metric to contextualize energy requirements. Full article

Waveguide bends are critical components for compact routing in integrated photonic circuits, yet their design in air-clad polymer waveguides fabricated by two-photon polymerization direct laser writing (2PP-DLW) is challenging due to multimode behavior. We address this by systematically modeling Bézier-shaped 90° bends and S-bends using a variational FDTD solver, exploring bend span, curvature, and waveguide dimensions. Our results show that smaller waveguides (widths 2–4 µm) and lower Bézier parameters (B = 0–0.2) consistently yield superior performance, enabling sharper bends with minimal loss. For 90° bends, spans as small as 20–30 µm achieve near-unity transmission, while for S-bends, aspect ratios below 1 are feasible, allowing highly compact layouts. Although all configurations transmit energy to the fundamental mode, wider waveguides exhibit stronger higher-order mode excitation and greater sensitivity to fabrication imperfections. Smaller waveguides reduce these effects but approach the resolution limits of 2PP-DLW. Thus, a 2 µm wide waveguide represents an optimal compromise between fabrication feasibility and optical performance. Experimental demonstrations confirm the practicality of these design rules, illustrating trends predicted by simulations. These findings establish clear guidelines for designing low-loss, space-efficient 3D photonic circuits and highlight the critical role of simulation-driven optimization in fully exploiting 2PP-DLW technology, while providing deeper insight for future device architectures. Full article

Studying the luminescent properties and the light amplification capabilities are fundamental investigations for newly synthesized organic compounds intended to act as chromophores. These studies are conducted for compounds in the form of solutions, solids, and also molecules stabilized with the aid of polymers. One of the methods used to study amplification is the generation of amplified spontaneous emission (ASE) using stripe-shaped light beam excitation. This process can lead to the generation of ASE, but also, with the coexistence of microcrystals and scatterers, to the generation of laser action with random feedback, known as random lasing (RL). However, when the degree of light scattering is too high, it can lead to the inhibition of laser emission. Therefore, as an alternative in studying amplification properties, we developed a protocol that allows the investigation of laser action generation using rapidly prototyped polymer waveguides with an embedded dye. The setup used was based on Direct Laser Writing (DLW), which enables the controlled fabrication of multimode optical waveguides. We demonstrated that the use of this technique will allow for the study of the performance of dyes from strictly structured resonators, enabling measurements of gain and lasing threshold. This allowed us to lower the lasing thresholds while maintaining the directionality of emission. Full article

Background/Objectives: Persons with multiple sclerosis (MS) are interested in diet as a second-line approach for disease management. This study examined potential variables that correlate with inaccuracy of self-reported energy intake (EI) in adults with MS. Methods: Twenty-eight participants completed two assessment appointments within a 14-day period that included a standard doubly labeled water (DLW) protocol for estimating total energy expenditure (TEE). The participants reported their EI using the Automated Self-Administered 24 h (ASA24) Dietary Assessment Tool. The primary variables of interest for explaining the discrepancy between TEE and ASA24 EI (i.e., inaccuracy) included cognition (processing speed, visuospatial memory, and verbal memory), hydration status (total body water), and device-measured physical activity. Pearson’s correlations assessed the association between absolute and percent inaccuracy in reporting of EI with outcomes of interest, followed by linear regression analyses for identifying independent correlates. Results: California Verbal Learning Test—Second Edition (CVLT-II) z-scores and light physical activity (LPA) were significantly associated with mean absolute difference in EI (r = –0.53 and r = 0.46, respectively). CVLT-II z-scores and LPA were the only variables significantly associated with mean percent difference in EI (r = –0.48 and r = 0.42, respectively). The regression analyses indicated that both CVLT-II and LPA significantly explained variance in mean absolute difference in EI, and only CVLT-II explained variance for percent difference in EI. Conclusions: The results from this study indicate that verbal learning and memory and LPA are associated with inaccuracy of self-reported EI in adults with MS. This may guide timely research identifying appropriate protocols for assessment of diet in MS. Full article

In modern knitted garment production, accurate identification of fabric texture is crucial for enabling automation and ensuring consistent quality control. Traditional manual recognition methods not only demand considerable human effort but also suffer from inefficiencies and are prone to subjective errors. Although machine learning-based approaches have made notable advancements, they typically rely on manual feature extraction. This dependency is time-consuming and often limits recognition accuracy. To address these limitations, this paper introduces a novel model, called the Differentiated Leaning Weighted DenseNet (DLW-DenseNet), which builds upon the DenseNet architecture. Specifically, DLW-DenseNet introduces a learnable weight mechanism that utilizes channel attention to enhance the selection of relevant channels. The proposed mechanism reduces information redundancy and expands the feature search space of the model. To maintain the effectiveness of channel selection in the later stages of training, DLW-DenseNet incorportes a differentiated learning strategy. By assigning distinct learning rates to the learnable weights, the model ensures continuous and efficient channel selection throughout the training process, thus facilitating effective model pruning. Furthermore, in response to the absence of publicly available datasets for fabric texture recognition, we construct a new dataset named KF9 (knitted fabric). Compared to the fabric recognition network based on the improved ResNet, the recognition accuracy has increased by five percentage points, achieving a higher recognition rate. Experimental results demonstrate that DLW-DenseNet significantly outperforms other representative methods in terms of recognition accuracy on the KF9 dataset. Full article

Enhancing the production capacity of natural gas hydrates (NGHs) is critical for its commercial development. Complex structure wells may efficiently increase drainage areas while enhancing exploitation efficiency. Based on the field data of China’s first offshore NGH test production, the numerical method was used to analyze the production performance of different complex structure well types by continuous depressurization production for 360 days under the preconditions of fixed effective completion length of 300 m and a pressure difference of 6 MPa. Results indicated that the complex structure well types deployed at the three-phase layer demonstrated superior production performance within 240 days of production; the DLW2 and HW2 well types stood out, with an average gas production rate Q_g reaching 43,333 m³/d and a specific production index J of 24.1. After 360 days of production, benefiting from multi-layer combined production, the Cluster vertical well deployed at the multi-layer had the best production performance, with an average Q_g of 34,444 m³/d and a J-index of 19.1. The research results provided insights into the complex structure well-type selection strategy for NGH depressurization in this sea area. Full article

Dye-sensitized solar cells (DSSCs) can be used as greenhouse glazing materials in agrivoltaic systems because they are translucent, have different colors, and can produce electricity. However, the light quality of DSSCs differs from that of sunlight, and the visible light transmittance is low. Therefore, we compared the plant shape, growth, and leaf color of coleus, a highly photosensitive plant, under transparent glass and red-colored DSSCs. Coleus ‘Highway Rose’ was grown in transparent (T, the control), shaded (S), and DSSC (D) chambers maintained at 23 ± 2 °C. The DSSC chambers were additionally illuminated with blue (B), green (G), white (W), B+G, and R+B+W light-emitting diodes (LEDs) (D+L) at 60 μmol·m⁻²·s⁻¹ photosynthetic photon flux density for 15 h from 05:00 to 20:00. The coleus generally exhibited good growth under the T treatment. However, the light quality of DSSCs differed from that of sunlight, and the visible light transmittance decreased. Coleus exhibited increased growth and leaf color characteristics under the supplemental B lighting treatments (D+L_(RBW), D+L_(B), D+L_(BG), and D+L_(W)). Supplemental lighting with B LEDs using DSSCs improved plant morphology growth and leaf color. On the other hand, supplemental G lighting reinforced the shade avoidance syndrome. Moreover, DSSCs could aid in reducing the energy required to control the environment. Full article

Adults with severe cerebral palsy (CP) are susceptible to malnutrition and metabolic disorders due to limited daily physical activity and challenges related to eating. We hypothesized that the condition of being underweight arises from inadequate energy intake due to difficulties in eating, rather than heightened total energy expenditure or an elevated resting metabolic rate. The present study encompassed 17 adults with severe CP (classified as GMFSC III–V). Energy intake, utilization, and expenditure were gauged via thorough dietary recordings and double-labeled water (DLW) analyses. Resting metabolic rates were assessed through indirect calorimetry, and metabolic health was investigated via blood samples. Oral motor function, eating assessment during meals, and weight fluctuations throughout the experimental period were also evaluated. We found significant correlations between weight, oral impairments (p < 0.01), and eating difficulties (p < 0.05). While total energy expenditure and daily consumption were similar between underweight (UW) and overweight (OW) individuals, significant variability in both expenditure and intake was evident within the UW group. Particularly, those with lower BMIs experienced heightened mealtime impairments and complications. Our present findings indicate that eating difficulties are the central concern for UW status in this population. Full article

Efficient mode conversion is crucial for hybrid photonic systems. We present efficient light transition from a standard single-mode fiber (SMF) to a subwavelength-diameter microfiber via a relatively short tapered fiber. Numerical simulations were performed to design the tapered morphology with high transmittance (approximately 86%) for the fundamental modes. The designed tapered fiber was successfully fabricated on the top of a cleaved SMF tip by the direct laser writing (DLW) method. For the 1550 nm wavelength, the transmittance from the standard SMF to the subwavelength-diameter microfiber was determined to be 77%, accompanied by a change in the effective mode area from 38 μm² to 0.47 μm² within a very short length of 150 μm. Our result demonstrated the versatility of the DLW technique for boosting the mode conversion efficiency of fiber-to-chip devices, enabling various applications in the future. Full article

In this paper, research was conducted on Deep Learning Wavefront Sensing (DLWS) neural networks using simulated atmospheric turbulence datasets, and a novel DLWS was proposed based on attention mechanisms and Convolutional Neural Networks (CNNs). The study encompassed both indoor experiments and kilometer-range laser transmission experiments employing DLWS. In terms of indoor experiments, data were collected and training was performed on the platform built by us. Subsequent comparative experiments with the Shack-Hartmann Wavefront Sensing (SHWS) method revealed that our DLWS model achieved accuracy on par with SHWS. For the kilometer-scale experiments, we directly applied the DLWS model obtained from the indoor platform, eliminating the need for new data collection or additional training. The DLWS predicts the wavefront from the beacon light PSF in real time and then uses it for aberration correction of the emitted laser. The results demonstrate a substantial improvement in the average peak intensity of the light spot at the target position after closed-loop correction, with a remarkable increase of 5.35 times compared to the open-loop configuration. Full article

Microlens arrays (MLAs) which are increasingly popular micro-optical elements in compact integrated optical systems were fabricated using a femtosecond direct laser write (fs-DLW) technique in the low-shrinkage SZ2080^TM photoresist. High-fidelity definition of 3D surfaces on IR transparent CaF₂ substrates allowed to achieve ∼50% transmittance in the chemical fingerprinting spectral region 2–5 $\mathsf{\mu }$m wavelengths since MLAs were only ∼10 $\mathsf{\mu }$m high corresponding to the numerical aperture of 0.3 (the lens height is comparable with the IR wavelength). To combine diffractive and refractive capabilities in miniaturised optical setup, a graphene oxide (GO) grating acting as a linear polariser was also fabricated by fs-DLW by ablation of a 1 $\mathsf{\mu }$m-thick GO thin film. Such an ultra-thin GO polariser can be integrated with the fabricated MLA to add dispersion control at the focal plane. Pairs of MLAs and GO polarisers were characterised throughout the visible–IR spectral window and numerical modelling was used to simulate their performance. A good match between the experimental results of MLA focusing and simulations was achieved. Full article

Functional laser surface texturing (LST) arose in recent years as a very powerful tool for tailoring the surface properties of parts and components to their later application. As a result, self-cleaning surfaces with an improved wettability, efficient engine components with optimized tribological properties, and functional implants with increased biocompatibility can be achieved today. However, with increasing capabilities in functional LST, the prediction of resulting surface properties becomes more and more important in order to reduce the development time of those functionalities. Consequently, advanced approaches for the prediction of the properties of laser-processed surfaces—the so-called predictive modelling—are required. This work introduces the concept of predictive modelling with respect to LST by means of direct laser writing (DLW). Fundamental concepts for the prediction of surface properties are presented employing machine learning approaches, theoretical concepts, and statistical methods. The modelling takes into consideration the used laser parameters, the analysis of topographical, and other process-relevant information in order to predict the resulting surface roughness. For this purpose, two different algorithms, namely artificial neural network and random forest, were trained with experimental data for stainless steel and Stavax surfaces. Statistical results indicate that both models can predict the desired surface topography with high accuracy, despite the use of a small dataset for the training process. The approaches can be used to further optimize the laser process regarding the process efficiency, overall throughput, and other process outcomes. Full article