Development of a novel beam profiling prototype with laser self-mixing via the knife-edge approach

Laser is widely used in industry, biomedical and other kinds of fields. Beam size is the most important parameter among the laser variables. Typical state-of-the-art profiling techniques employ either a scanning-based or camera-based system, using photodiodes or image sensors as the signal receiver. Despite their profiling capabilities, these systems do not tend to be budget-friendly and easy to operate. In this paper, a novel cost-effective beam profiling prototype based on self-mixing interference was developed to measure the Full Width Half Maximum (FWHM) of a range of laser diodes by the knife-edge approach. The difference between our prototype and other systems is that the photodiode is placed behind the laser source, and beam size is calculated by analyzing the feedback signal. A commercial camera beam profiler was used to benchmark our prototype. Results show that though there is a variation of 45.29% between the measured beam size and the integrated beam size in the x directions due to diffuse and specular reflection, our USD 200 prototype has a high accuracy on the prediction of laser beam sizes. Our prototype could provide accurate predicted beam size for Gaussian-alike beam. This is the very first study to explore the application of self-mixing interference in laser beam profiling. It is believed that our proposed approach has contributed to the on-going development of laser beam profiling methodology

Study on laser spot size measurement by scanning-slit method based on back-injection interferometry

Spot size is an important parameter of the laser, which not only represents the resolution of laser, but is also involved in the calculation of other parameters. Nowadays, CCD imaging systems, scanning imaging systems, and other sensors are used to measure the laser spot size. But they are all lacking flexibility when measuring the spot size in different locations, not to mention their high cost. In this study, a new spot size measurement device based on laser back-injection interferometry was presented. The photodiode integrated with the laser diode was used to collect the feedback laser, then the laser spot size was calculated by the feedback current. A commercial CCD imaging system was used to provide the laser spot size as a reference. Results show that our spot size measurement device could measure the spot size (Full Width Half Maximum) of 5 laser diode modules both in the x (Gaussian-like profile) and y (top-hat-like profile) direction through scanning-slit. Though there are variations between the scanning-slit results and spot sizes from the CCD imaging system due to the diffuse and specular reflection, the accuracy of the spot size measurement device ranges from 96.07 % to 99.46 %, which proves the reliability of our device. It is believed that our device could provide an alternate method for laser spot size measurement, which is cost-effective, easy to operate, and accurate

Phenoxazine Based Units- Synthesis, Photophysics and Electrochemistry

A few new phenoxazine-based conjugated monomers were synthesized, characterized, and successfully used as semiconducting materials. The phenoxazine-based oligomers have low ionization potentials or high-lying HOMO levels (~4.7 eV), which were estimated from cyclic voltammetry. Conjugated oligomers offer good film—forming, mechanical and optical properties connected with their wide application. These results demonstrate that phenoxazine-based conjugated mers are a promising type of semiconducting and luminescent structures able to be used as thin films in organic electronics

Synthesis of a Dual Functional Anti-MDR Tumor Agent PH II-7 with Elucidations of Anti-Tumor Effects and Mechanisms

Multidrug resistance mediated by P-glycoprotein in cancer cells has been a major issue that cripples the efficacy of chemotherapy agents. Aimed for improved efficacy against resistant cancer cells, we designed and synthesized 25 oxindole derivatives based on indirubin by structure-activity relationship analysis. The most potent one was named PH II-7, which was effective against 18 cancer cell lines and 5 resistant cell lines in MTT assay. It also significantly inhibited the resistant xenograft tumor growth in mouse model. In cell cycle assay and apoptosis assay conducted with flow cytometry, PH II-7 induced S phase cell cycle arrest and apoptosis even in resistant cells. Consistently revealed by real-time PCR, it modulates the expression of genes related to the cell cycle and apoptosis in these cells, which may contributes to its efficacy against them. By side-chain modification and FITC-labeling of PH II-7, we were able to show with confocal microscopy that not only it was not pumped by P-glycoprotein, it also attenuated the efflux of Adriamycin by P-glycoprotein in MDR tumor cells. Real-time PCR and western blot analysis showed that PH II-7 down-regulated MDR1 gene via protein kinase C alpha (PKCA) pathway, with c-FOS and c-JUN as possible mediators. Taken together, PH II-7 is a dual-functional compound that features both the cytotoxicity against cancer cells and the inhibitory effect on P-gp mediated drug efflux

Prognostic value of EZH2 in non-small-cell lung cancers : a meta-analysis and bioinformatics analysis

Background. The prognosis of non-small-cell lung cancer (NSCLC) has not been significantly improved. In the past several years, research on epigenetics is in full swing. There is a focus on the gene EZH2; however, its role as a predictor of the prognosis of NSCLC is in the debate. Objective. To clarify if the expression level of EZH2 can influence the prognosis of NSCLC and explain its prognostic value. Methods. We have systematically searched PubMed, Web of Science, and Cochrane library, screened relevant articles, and conducted a meta-analysis on the expression level of EZH2 in NSCLC. We collected the hazard ratio (HR) and the 95% confidence interval (CI) and used STATA 12.0 to calculate the combined result of EZH2 overall survival. In addition, we conducted subgroup analyses, a sensitivity analysis, and a funnel plot to test the reliability of the results. We further validated these meta-analysis results using the Kaplan-Meier plotter database and The Cancer Genome Atlas (TCGA) database. In addition, we have investigated the correlation between EZH2 expression and EGFR expression, KRAS expression, BRAF expression, and smoking in TCGA database to further explore the mechanism behind the influence of high EZH2 expression on lung cancer prognosis. Results. 13 studies including 2180 participants were included in the meta-analysis. We found that high expression of EZH2 indicates a poor prognosis of NSCLC (HR = 1:65 and 95% CI 1.16-2.35; p ≤ 0:001). Subgroup analyses showed high heterogeneity in stages I-IV (I2 = 85:1% and p ≤ 0:001) and stages I-III (I2 = 66:9% and p = 0:029) but not in stage I (I2 = 0:00% and p = 0:589). In the Kaplan-Meier plotter database, there was a high expression in 963 cases and low expression in 964 cases (HR = 1:31 and 95% CI 1.15-1.48; p < 0:05). Further analysis found that the high expression of EZH2 was statistically significant in lung adenocarcinoma (HR = 1:27and 95% CI 1.01−1.6; p = 0:045), but not in lung squamous cell carcinoma (HR = 1:03 and 95% CI 0.81−1.3; p = 0:820). The results of the TCGA database showed that the expression of EZH2 in normal tissues was lower than that in lung cancer tissues (p < 0:05). Smoking was associated with high expression of EZH2 (p < 0:001). EZH2 was also highly expressed in lung cancers with positive KRAS expression, and the correlation was positive in lung adenocarcinoma (r = 0:3129 and p < 0:001). The correlation was also positive in lung squamous cell carcinoma (r = 0:3567 and p < 0:001). EZH2 expression was positively correlated with BRAF expression (r = 0:2397 and p < 0:001), especially in lung squamous cell carcinoma (r = 0:3662 and p < 0:001). In lung squamous cell carcinoma, a positive yet weak correlation was observed between EZH2 expression and EGFR expression (r = 0:1122 and p < 0:001). Conclusions. The high expression of EZH2 indicates a poor prognosis of NSCLC, which may be related to tumor stage or cancer type. EZH2 may be an independent prognostic factor for NSCLC. EZH2 high expression or its synergistic action with KRAS and BRAF mutations affects the prognosis of non-small-cell lung cancer

Laser offline measurement method based on self-mixing interference for thin-wall fused deposition modelling component

Fused Deposition Modelling (FDM) is affected by various factors such as process parameters during the layer-by-layer printing process, and the parts will have serious defects and unstable mechanical properties. Therefore, the real-time monitoring of the processing process can better study the relationship between the parameters and defects. In particular, the direct measurement of thin-wall component dimensions and surface defects can better realize the adjustment of process parameters to improve the mechanical properties. However, existing monitoring methods either indirectly predict part quality by checking equipment health situation or directly inspect the component with inflexible field-of-view. Hence, this paper proposes a novel monitoring proof-of-concept prototype based on the phenomenon of laser self-mixing to directly inspect for part quality. The system primarily uses a laser diode with a self-encapsulated photodiode which is capable of collecting optical feedback signal once the laser hits a target. And data processing and calculation are performed on the feedback signal to obtain the geometric size and defect size of the target. The measurement object is FDM single-wall plates. The results show that the laser scanning monitoring system can successfully measure the target, and the measurement accuracy of the average size of waviness defects can reach up to 99.36 %. To the best of our knowledge, this study is the first to apply the self-mixing phenomenon as a metrology technique for the monitoring of an additive manufacturing process

Pilot design of experiment study: effect of stirring duration and guest particle loading on electrostatic adsorption of Ti-6Al-4V composite powder formation

AbstractIn recent years, materials science and engineering have increasingly focused on advanced composite powders. This study examines the preparation of micro–micro Ti-6Al-4V composite powders by electrostatic adsorption (EA). The necessity of this research lies in the demand for optimizing the Ti-6Al-4V composite powder formation process window for high-performance applications across industries. Achieving optimal EA parameters is crucial for enhancing the quality and efficiency of the powder formation process. In this study, the effect of stirring duration and guest particle loading on the EA process is investigated. The stirring time (1 to 25 min) and guest particle loading (10 to 60%) of the solution are varied to determine the ideal conditions for high adsorption efficiency. It was found that shorter stirring durations (1 min) and a lower guest particle load (10%) have a significant effect on adsorption efficiency. The results were analyzed using the DOE approach to guide future optimization of the process window. The study fills a research gap by utilizing the DOE approach to investigate stirring duration and guest particle loading, providing insights for optimizing the EA process for micro–micro Ti-6Al-4V composite powder. This approach has the potential to enhance cost-effective, durable composite powder production with broad applications in industries like aerospace and automotive. While our research currently focuses on stirring duration and guest particle loading, the application of the DOE approach lays the groundwork for future investigations into additional EA process parameters, such as pH value, particle size, and temperature to expand our understanding of efficient composite powder formation.</jats:p

Training Small Networks for Scene Classification of Remote Sensing Images via Knowledge Distillation

Scene classification, aiming to identify the land-cover categories of remotely sensed image patches, is now a fundamental task in the remote sensing image analysis field. Deep-learning-model-based algorithms are widely applied in scene classification and achieve remarkable performance, but these high-level methods are computationally expensive and time-consuming. Consequently in this paper, we introduce a knowledge distillation framework, currently a mainstream model compression method, into remote sensing scene classification to improve the performance of smaller and shallower network models. Our knowledge distillation training method makes the high-temperature softmax output of a small and shallow student model match the large and deep teacher model. In our experiments, we evaluate knowledge distillation training method for remote sensing scene classification on four public datasets: AID dataset, UCMerced dataset, NWPU-RESISC dataset, and EuroSAT dataset. Results show that our proposed training method was effective and increased overall accuracy (3% in AID experiments, 5% in UCMerced experiments, 1% in NWPU-RESISC and EuroSAT experiments) for small and shallow models. We further explored the performance of the student model on small and unbalanced datasets. Our findings indicate that knowledge distillation can improve the performance of small network models on datasets with lower spatial resolution images, numerous categories, as well as fewer training samples