Drop-On-Demand Microdroplet Generation under Charge Injection by Corona Discharge.

In printing, microreactors, and bioassays, the precise control of micrometer-scale droplet generation is essential but challenging, often restricted by the equipment and nozzles used in traditional methods. We introduce a needle-plate electrode corona discharge technique that injects charges into an oil layer, enabling the precise manipulation of droplet polarization and splitting. This method allows for meticulous adjustment of microdroplet formation regarding location, size, and quantity by modulating the discharge voltage, discharge time, and electrode positioning. It enables the immediate initiation and cessation of droplet production, thereby facilitating on-demand droplet generation. Our study on the voltage-dependent droplet stretch coefficient shows that as the voltage increases, the droplets transition from controlled splitting to regular Taylor cone-like ejections, eventually reaching the Rayleigh limit and fully breaking apart. These advancements significantly improve microfluidic droplet manipulation, offering considerable benefits for applications in targeted drug delivery, rapid disease diagnostics, and precise environmental monitoring.

Dynamical Janus-Like Behavior Excited by Passive Cold-Heat Modulation in the Earth-Sun/Universe System: Opportunities and Challenges.

The utilization of solar-thermal energy and universal cold energy has led to many innovative designs that achieve effective temperature regulation in different application scenarios. Numerous studies on passive solar heating and radiation cooling often operate independently (or actively control the conversion) and lack a cohesive framework for deep connections. This work provides a concise overview of the recent breakthroughs in solar heating and radiation cooling by employing a mechanism material in the application model. Furthermore, the utilization of dynamic Janus-like behavior serves as a novel nexus to elucidate the relationship between solar heating and radiation cooling, allowing for the analysis of dynamic conversion strategies across various applications. Additionally, special discussions are provided to address specific requirements in diverse applications, such as optimizing light transmission for clothing or window glass. Finally, the challenges and opportunities associated with the development of solar heating and radiation cooling applications are underscored, which hold immense potential for substantial carbon emission reduction and environmental preservation. This work aims to ignite interest and lay a solid foundation for researchers to conduct in-depth studies on effective and self-adaptive regulation of cooling and heating.

Continuous Tunable Energy Band Tailoring Boosts Extending the Sensing of the Waveband Based on (InxGa1-x)2O3 Solar-Blind Photodetectors.

Rising wide bandgap semiconductor gallium oxide (Ga2O3) displays huge potential in performing solar-blind photodetection, with constraint in narrow detection wavebands in nature, whereas bandgap modulation through the introduction of exotic atoms into Ga2O3 has an essential effect on the tunable performance of photodetectors and the detection waveband. Here, a novel method for the preparation of (InxGa1-x)2O3 alloy films is proposed, and the continuous tuning of the bandgap in the range of 3.70-4.99 eV is achieved by varying the In-doping content. Alloy-based metal-semiconductor-metal photodetectors were fabricated, achieving a peak responsivity between 254 and 295 nm, superior performance compared to Ga2O3 photodetectors, with a photo-to-dark current ratio as high as 106, and a better optical image-sensing capability. This study offers new insight for high-performance detection of full solar-blind waveband ultraviolet light.

Enhanced Absolute Recovered Energy under Low Electric Field in All-Inorganic 0-3 Nanocomposition Thick Films.

Inorganic thick-film dielectric capacitors with ultrahigh absolute recovered energy at low electric fields are extremely desired for their wide application in pulsed power systems. However, a long-standing technological bottleneck exists between high absolute energy and large recovered energy density. A new strategy is offered to fabricate selected all-inorganic 0-3 composite thick films up to 10 µm by a modified sol-slurry method. Here, the ceramic powder is dispersed into the sol-gel matrix to form a uniform suspension, assisted by powder, therefore, the 2 µm-thickness after single layer spin coating. To enhance the energy-storage performances, the composites process is thoroughly optimized by ultrafine powder (<50 nm) technique based on a low-cost coprecipitation method instead of the solid-state and sol-gel methods. 0D coprecipitation powder has a similar dielectric constant to the corresponding 3D films, thus uneven electrical field distributions is overcome. Moreover, the increase of interfacial polarization is realized due to the larger specific surface area. A maximum recoverable energy density of 14.62 J cm-3 is obtained in coprecipitation thick films ≈2.2 times that of the solid-state powder and ≈1.3 times for sol-gel powder. This study provides a new paradigm for further guiding the design of composite materials.

Total scalp irradiation: A study comparing multiple types of bolus and VMAT optimization techniques.

PURPOSE: To investigate bolus design and VMAT optimization settings for total scalp irradiation. METHODS: Three silicone bolus designs (flat, hat, and custom) from .decimal were evaluated for adherence to five anthropomorphic head phantoms. Flat bolus was cut from a silicone sheet. Generic hat bolus resembles an elongated swim cap while custom bolus is manufactured by injecting silicone into a 3D printed mold. Bolus placement time was recorded. Air gaps between bolus and scalp were quantified on CT images. The dosimetric effect of air gaps on target coverage was evaluated in a treatment planning study where the scalp was planned to 60 Gy in 30 fractions. A noncoplanar VMAT technique based on gEUD penalties was investigated that explored the full range of gEUD alpha values to determine which settings achieve sufficient target coverage while minimizing brain dose. ANOVA and the t-test were used to evaluate statistically significant differences (threshold = 0.05). RESULTS: The flat bolus took 32 ± 5.9 min to construct and place, which was significantly longer (p < 0.001) compared with 0.67 ± 0.2 min for the generic hat bolus or 0.53 ± 0.10 min for the custom bolus. The air gap volumes were 38 ± 9.3 cc, 32 ± 14 cc, and 17 ± 7.0 cc for the flat, hat, and custom boluses, respectively. While the air gap differences between the flat and custom boluses were significant (p = 0.011), there were no significant dosimetric differences in PTV coverage at V57Gy or V60Gy. In the VMAT optimization study, a gEUD alpha of 2 was found to minimize the mean brain dose. CONCLUSIONS: Two challenging aspects of total scalp irradiation were investigated: bolus design and plan optimization. Results from this study show opportunities to shorten bolus fabrication time during simulation and create high quality treatment plans using a straightforward VMAT template with simple optimization settings.

Free-Standing Electrode and Fixed Surface Tiny Electrode Implemented Triboelectric Nanogenerator with High Instantaneous Current.

Conventional triboelectric nanogenerators (TENGs) face challenges pertaining to low output current density at low working frequencies and high internal impedance. While strategies, such as surface modification to enhance surface charge density, permittivity regulation of materials, and circuit management, have partially mitigated these issues. However, they have also resulted in increased complexity in the fabrication process. Therefore, there is an urgent demand for a universal and simplified approach to address these challenges. To fulfill this need, this work presents a free-standing electrode and fixed surface tiny electrode implemented triboelectric nanogenerator (FFI-TENG). It is fabricated by a straightforward yet effective method: introducing a tiny electrode onto the surface of the tribo-negative material. This approach yields substantial enhancements in performance, notably a more than tenfold increase in output current density, a reduction in effective working frequencies, and a decrease in matching resistance as compared to vertical contact-separation TENGs (CS-TENGs) or single-electrode TENGs (SE-TENGs). Simultaneously, a comprehensive examination and proposition regarding the operational mechanism of FFI-TENG, highlighting its extensive applicability are also offered. Significantly, FFI-TENG excels in mechanical energy harvesting even under ultra-low working frequencies (0.1 Hz), outperforming similar contact-separation models. This innovation positions it as a practical and efficient solution for the development of low-entropy energy harvesters.

Self-powered solar-blind detector array based on ε-Ga2O3 Schottky photodiodes for dual-mode binary UV communication.

In this work, a solar-blind UV metal-semiconductor Schottky photodiode array is constructed by using metalorganic chemical vapor deposition grown ε-Ga2O3 thin film, possessing high-performance and self-powered characteristics, toward dual-mode (self-powered and biased modes) binary light communication. For the array unit, the responsivity, specific detectivity, and external quantum efficiency are 30.8 A/W/6.3 × 10-2 A/W, 1.51 × 104%/30.9%, 1.28 × 1014/5.4 × 1012 Jones for biased (-10 V)/self-powered operation. The rise and decay time are 0.19 and 7.96 ms at biased modes, respectively, suggesting an ability to trace fast light signal. As an array, the deviation of photocurrent is only 4.3%, highlighting the importance of accurate information communication. Through certain definition of "1/0" binary digital information, the "NY" and "IC" characters are communicated to illustrate the self-powered and biased modes by right of ASCII codes, based on the prepared ε-Ga2O3 solar-blind UV Schottky photodiode array. This work made dual-mode binary deep-UV light communication come true and may well guide the development of UV optoelectronics.

Reinforcing alginate matrixes by tea polysaccharide conjugates or their stabilized nanoemulsion for probiotics encapsulation: Characterization, survival after gastrointestinal digestion and ambient storage.

Tea polysaccharide conjugates (TPC) were used as fillers in the form of biopolymer or colloidal particles (TPC stabilized nanoemulsion, NE) for reinforcing alginate (ALG) beads to improve the probiotic viability. Results demonstrated that adding TPC or NE to ALG beads significantly enhanced the gastrointestinal viability of encapsulated probiotics when compared to free cells. Moreover, the survivability of free and ALG encapsulated probiotics markedly decreased to 2.03 ± 0.05 and 2.26 ± 0.24 log CFU/g, respectively, after 2 weeks ambient storage, indicating pure ALG encapsulation had no effective storage protective capability. However, adding TPC or NE could greatly enhance the ambient storage viability of probiotics, with ALG + NE beads possessing the best protection (8.93 ± 0.06 log CFU/g) due to their lower water activity and reduced porosity. These results suggest that TPC and NE reinforced ALG beads have the potential to encapsulate, protect and colonic delivery of probiotics.

Plan Quality Analysis of Automated Treatment Planning Workflow With Commercial Auto-Segmentation Tools and Clinical Knowledge-Based Planning Models for Prostate Cancer.

This study evaluated the feasibility of using artificial intelligence (AI) segmentation software for volume-modulated arc therapy (VMAT) prostate planning in conjunction with knowledge-based planning to facilitate a fully automated workflow. Two commercially available AI software programs, Radformation AutoContour (Radformation, New York, NY) and Siemens AI-Rad Companion (Siemens Healthineers, Malvern, PA) were used to auto-segment the rectum, bladder, femoral heads, and bowel bag on 30 retrospective clinical cases (10 intact prostate, 10 prostate bed, and 10 prostate and lymph node). Physician-segmented target volumes were transferred to AI structure sets. In-house RapidPlan models were used to generate plans using the original, physician-segmented structure sets as well as Radformation and Siemens AI-generated structure sets. Thus, there were three plans for each of the 30 cases, totaling 90 plans. Following RapidPlan optimization, planning target volume (PTV) coverage was set to 95%. Then, the plans optimized using AI structures were recalculated on the physician structure set with fixed monitor units. In this way, physician contours were used as the gold standard for identifying any clinically relevant differences in dose distributions. One-way analysis of variation (ANOVA) was used for statistical analysis. No statistically significant differences were observed across the three sets of plans for intact prostate, prostate bed, or prostate and lymph nodes. The results indicate that an automated volumetric modulated arc therapy (VMAT) prostate planning workflow can consistently achieve high plan quality. However, our results also show that small but consistent differences in contouring preferences may lead to subtle differences in planning results. Therefore, the clinical implementation of auto-contouring should be carefully validated.

Evaluating Pareto optimal tradeoffs for hippocampal avoidance whole brain radiotherapy with knowledge-based multicriteria optimization.

The goal of this study is to investigate the Pareto optimal tradeoffs between target coverage and hippocampal sparing using knowledge-based multicriteria optimization (MCO). Ten prior clinical cases were selected that were treated with hippocampal avoidance whole brain radiotherapy (HA-WBRT) using VMAT. A new, balanced plan was generated for each case using an in-house RapidPlan model in the Eclipse V16.1 treatment planning system. The MCO decision support tool was used to create 4 Pareto optimal plans. The Pareto optimal plans were created using PTV Dmin and hippocampus Dmax as tradeoff criteria. The tradeoff plans were generated for each patient by adjusting PTV Dmin from the value achieved by the corresponding balanced plan in fixed intervals as follows: -4 Gy, -2 Gy, +2 Gy, and +4 Gy. All plans were normalized so that 95% of the PTV was covered by the prescription dose. A 1-way ANOVA, with Geisser-Greenhouse correction, was used for statistical analysis. When evaluating the achieved PTV Dmin and D98%, the results showed the dose to the hippocampus decreased as coverage lowered and in comparison, D98% was higher when the PTV coverage was increased. When comparing multiple tradeoffs, the p-value for PTV D98% was 0.0026, and the p-values for PTV D2%, PTV Dmin, Hippocampus Dmax, Dmin, and Dmean were all less than 0.0001, indicating that the tradeoff plans achieved statistically significant differences. The results also showed that Pareto optimal plans failed to reduce hippocampal dose beyond a certain point, indicating more limited achievability of the MCO-navigated plans than the interface suggested. This study presents valuable data for planning results for HA-WBRT using MCO. MCO has shown to be mostly effective in adjusting the tradeoff between PTV coverage and hippocampal dose.

A Flexible Piezocapacitive Pressure Sensor with Microsphere-Array Electrodes.

Flexible pressure sensors that emulate the sensation and characteristics of natural skins are of great importance in wearable medical devices, intelligent robots, and human-machine interfaces. The microstructure of the pressure-sensitive layer plays a significant role in the sensor's overall performance. However, microstructures usually require complex and costly processes such as photolithography or chemical etching for fabrication. This paper proposes a novel approach that combines self-assembled technology to prepare a high-performance flexible capacitive pressure sensor with a microsphere-array gold electrode and a nanofiber nonwoven dielectric material. When subjected to pressure, the microsphere structures of the gold electrode deform via compressing the medium layer, leading to a significant increase in the relative area between the electrodes and a corresponding change in the thickness of the medium layer, as simulated in COMSOL simulations and experiments, which presents high sensitivity (1.807 kPa-1). The developed sensor demonstrates excellent performance in detecting signals such as slight object deformations and human finger bending.

Triboelectric Nanogenerators Based on 2D Materials: From Materials and Devices to Applications.

Recently, there has been an increasing consumption of fossil fuels such as oil and natural gas in both industrial production and daily life. This high demand for non-renewable energy sources has prompted researchers to investigate sustainable and renewable energy alternatives. The development and production of nanogenerators provide a promising solution to address the energy crisis. Triboelectric nanogenerators, in particular, have attracted significant attention due to their portability, stability, high energy conversion efficiency, and compatibility with a wide range of materials. Triboelectric nanogenerators (TENGs) have many potential applications in various fields, such as artificial intelligence (AI) and the Internet of Things (IoT). Additionally, by virtue of their remarkable physical and chemical properties, two-dimensional (2D) materials, such as graphene, transition metal dichalcogenides (TMDs), hexagonal boron nitride (h-BN), MXenes, and layered double hydroxides (LDHs), have played a crucial role in the advancement of TENGs. This review summarizes recent research progress on TENGs based on 2D materials, from materials to their practical applications, and provides suggestions and prospects for future research.

pH Effect on the Structure, Rheology, and Electrospinning of Maize Zein.

As a simple and convenient technology to fabricate micron-to-nanoscale fibers with controllable structure, electrostatic spinning has produced fiber films with many natural advantages, including a large specific surface area and high porosity. Maize zein, as a major storage protein in corn, showed high hydrophobicity and has been successfully applied as a promising carrier for encapsulation and controlled release in the pharmaceutical and food areas. Proteins exhibit different physical and chemical properties at different pH values, and it is worth investigating whether this change in physical and chemical properties affects the properties of electrospun fiber films. We studied the pH effects on zein solution rheology, fiber morphology, and film properties. Rotational rheometers were used to test the rheology of the solutions and establish a correlation between solution concentration and fiber morphology. The critical concentrations calculated by the cross-equation fitting model were 17.6%, 20.1%, 20.1%, 17.1%, and 19.5% (w/v) for pH 4, 5, 6, 7, and 8, respectively. The secondary structure of zein changed with the variation in solution pH. Furthermore, we analyzed the physical properties of the zein films. The contact angles of the fiber membranes prepared with different pH spinning solutions were all above 100, while zein films formed by solvent evaporation showed hydrophilic properties. The results indicated that the rheological properties of zein solutions and the surface properties of the film were affected by the pH value. This study showed that zein solutions can be stabilized to form electrospun fibers at a variety of pH levels and offered new opportunities to further enhance the encapsulation activity of zein films for bioactive materials.

Lung SBRT treatment planning: a study of VMAT arc selection guided by collision check software.

To evaluate the effects of arc geometry on lung stereotactic body radiation therapy (SBRT) plan quality, using collision check software to select safe beam angles. Thirty lung SBRT cases were replanned 10Gy x 5 using 4 volumetric modulated arc therapy (VMAT) geometries: coplanar lateral (cpLAT), coplanar oblique (cpOBL), noncoplanar lateral (ncpLAT) and noncoplanar oblique (ncpOBL). Lateral arcs spanned 180° on the affected side whereas the 180° oblique arcs crossed midline to spare healthy tissues. Couch angles were separated by 30° on noncoplanar plans. Clearance was verified with Radformation CollisionCheck software. Optimization objectives were the same across the four plans for each case. Planning target volume (PTV) coverage was set to 95% and then plans were evaluated for dose conformity, healthy tissue doses, and monitor units. Clinically treated plans were used to benchmark the results. The volumes of the 25%, 50% and 75% isodoses were smaller with noncoplanar than coplanar arcs. The volume of the 50% isodose line relative to the PTV (CI50%) was as follows: clinical 3.75±0.72, cpLAT 3.39 ± 0.37, cpOBL 3.36 ± 0.34, ncpLAT 3.02 ± 0.21 and ncpOBL 3.02 ± 0.22. The Wilcoxon signed rank test with Bonferroni correction showed p < 0.005 in all CI50% comparisons except between the cpLat and cpObl arcs and between the ncpLat and ncpObl arcs. The best lung sparing was achieved using ncpObl arcs, which was statistically significant (p < 0.001) compared with the other four plans at V12.5Gy, V13.5Gy and V20Gy. Chest wall V30Gy was significantly better using noncoplanar arcs in comparison to the other plan types (p < 0.001). The best heart sparing at V10Gy from the ncpOBL arcs was significant compared with the clinical and cpLat plans (p < 0.005). Arc geometry has a substantial effect on lung SBRT plan quality. Noncoplanar arcs were superior to coplanar arcs at compacting the dose distribution at the 25%, 50% and 75% isodose levels, thereby reducing the dose to healthy tissues. Further healthy tissue sparing was achieved using oblique arcs that minimize the pathlength through healthy tissues and avoid organs at risk. The dosimetric advantages of the noncoplanar and oblique arcs require careful beam angle selection during treatment planning to avoid collisions during treatment, which may be facilitated by commercial software.

Reinforcement learning in medical image analysis: Concepts, applications, challenges, and future directions.

MOTIVATION: Medical image analysis involves a series of tasks used to assist physicians in qualitative and quantitative analyses of lesions or anatomical structures which can significantly improve the accuracy and reliability of medical diagnoses and prognoses. Traditionally, these tedious tasks were finished by experienced physicians or medical physicists and were marred with two major problems, low efficiency and bias. In the past decade, many machine learning methods have been applied to accelerate and automate the image analysis process. Compared to the enormous deployments of supervised and unsupervised learning models, attempts to use reinforcement learning in medical image analysis are still scarce. We hope that this review article could serve as the stepping stone for related research in the future. SIGNIFICANCE: We found that although reinforcement learning has gradually gained momentum in recent years, many researchers in the medical analysis field still find it hard to understand and deploy in clinical settings. One possible cause is a lack of well-organized review articles intended for readers without professional computer science backgrounds. Rather than to provide a comprehensive list of all reinforcement learning models applied in medical image analysis, the aim of this review is to help the readers formulate and solve their medical image analysis research through the lens of reinforcement learning. APPROACH & RESULTS: We selected published articles from Google Scholar and PubMed. Considering the scarcity of related articles, we also included some outstanding newest preprints. The papers were carefully reviewed and categorized according to the type of image analysis task. In this article, we first reviewed the basic concepts and popular models of reinforcement learning. Then, we explored the applications of reinforcement learning models in medical image analysis. Finally, we concluded the article by discussing the reviewed reinforcement learning approaches' limitations and possible future improvements.

Effects of pile fermentation on the physicochemical, functional, and biological properties of tea polysaccharides.

This study investigated the influence of pile fermentation on the physicochemical, functional, and biological properties of tea polysaccharides (TPS). Results indicated that the extraction yield, uronic acid content, and polyphenol content of TPS greatly increased from 1.8, 13.1 and 6.3 % to 4.1, 27.9, and 7.8 %, respectively, but the molecular weight markedly decreased from 153.7 to 76.0 kDa after pile fermentation. Additionally, the interfacial, emulsion formation, and emulsion stabilization properties of TPS were significantly improved after pile fermentation. For instance, 1.0 wt% TPS isolated from dark tea (D-TPS) can fabricate 8.0 wt% MCT oil-in-water nanoemulsion (d32 ≈ 159 nm) with potent storage stability. Moreover, the antioxidant and α-glucosidase inhibitory activities of D-TPS was higher than that of TPS isolated from sun-dried raw tea (R-TPS). Overall, this study indicated that pile fermentation markedly affected the physicochemical and structural characteristics of TPS, thereby improving their functional and biological properties.

Evaluating the plan quality of a general head-and-neck knowledge-based planning model versus separate unilateral/bilateral models.

The implementation of knowledge-based planning (KBP) continues to grow in radiotherapy clinics. KBP guides radiation treatment design by generating clinically acceptable plans in a timely and resource-efficient manner. The role of multiple KBP models tailored for variations within a disease site remains undefined in part because of the substantial effort and number of training cases required to create a high-quality KBP model. In this study, our aim was to explore whether site-specific KBP models lead to clinically meaningful differences in plan quality for head-and-neck (HN) patients when compared to a general model. One KBP model was created from prior volumetric-modulated arc therapy (VMAT) cases that treated unilateral HN lymph nodes while another model was created from VMAT cases that treated bilateral HN nodes. Thirty cases from each model (60 cases total) were randomly selected to create a third, general model. These models were applied to 60 HN test cases - 30 unilateral and 30 bilateral - to generate 180 VMAT plans in Eclipse. Clinically relevant dose metrics were compared between models. Paired-sample t-tests were used for statistical analysis, with the threshold for statistical significance set a priori at 0.007, taking into consideration multiple hypothesis testing to avoid type I error. For unilateral test cases, the unilateral model-generated plans had significantly lower spinal cord maximum doses (12.1 Gy vs 19.3 Gy, p < 0.001) and oral cavity mean doses (20.8 Gy vs 23.0 Gy, p < 0.001), compared with the bilateral model-generated plans. The unilateral and general models generated comparable plans for unilateral HN test cases. For bilateral test cases, the bilateral model created plans had significantly lower brainstem maximum doses (10.8 Gy vs 12.2 Gy, p < 0.001) and parotid mean doses (24.0 Gy vs 25.5 Gy, p < 0.001) when compared to the unilateral model. Right parotid mean doses were lower for bilateral model plans compared to general model plans (23.8 Gy vs 24.4 Gy). The general model created plans with significantly lower brainstem maximum doses (10.3 Gy vs 10.8 Gy) and oral cavity mean doses (35.3 Gy vs 36.7 Gy) when compared with bilateral model-generated plans. The general model outperformed the bilateral model in several dose metrics but they were not deemed clinically significant. For both case sets, the unilateral and general model created plans had higher monitor units when compared to the bilateral model, likely due to more stringent constraint settings. All other dose metrics were comparable. This study demonstrates that a balanced general HN model created using carefully curated treatment plans can produce high quality plans comparable to dedicated unilateral and bilateral models.

Enhanced cardiac substructure sparing through knowledge-based treatment planning for non-small cell lung cancer radiotherapy.

Radiotherapy (RT) doses to cardiac substructures from the definitive treatment of locally advanced non-small cell lung cancers (NSCLC) have been linked to post-RT cardiac toxicities. With modern treatment delivery techniques, it is possible to focus radiation doses to the planning target volume while reducing cardiac substructure doses. However, it is often challenging to design such treatment plans due to complex tradeoffs involving numerous cardiac substructures. Here, we built a cardiac-substructure-based knowledge-based planning (CS-KBP) model and retrospectively evaluated its performance against a cardiac-based KBP (C-KBP) model and manually optimized patient treatment plans. CS-KBP/C-KBP models were built with 27 previously-treated plans that preferentially spare the heart. While the C-KBP training plans were created with whole heart structures, the CS-KBP model training plans each have 15 cardiac substructures (coronary arteries, valves, great vessels, and chambers of the heart). CS-KBP training plans reflect cardiac-substructure sparing preferences. We evaluated both models on 28 additional patients. Three sets of treatment plans were compared: (1) manually optimized, (2) C-KBP model-generated, and (3) CS-KBP model-generated. Plans were normalized to receive the prescribed dose to at least 95% of the PTV. A two-tailed paired-sample t-test was performed for clinically relevant dose-volume metrics to evaluate the performance of the CS-KBP model against the C-KBP model and clinical plans, respectively. Overall results show significantly improved cardiac substructure sparing by CS-KBP in comparison to C-KBP and the clinical plans. For instance, the average left anterior descending artery volume receiving 15 Gy (V15 Gy) was significantly lower (p < 0.01) for CS-KBP (0.69 ± 1.57 cc) compared to the clinical plans (1.23 ± 1.76 cc) and C-KBP plans (1.05 ± 1.68 cc). In conclusion, the CS-KBP model significantly improved cardiac-substructure sparing without exceeding the tolerances of other OARs or compromising PTV coverage.

Oscillatory Motion of Water Droplets Both in Oil and on Superhydrophobic Surface under Corona Discharge.

Charged droplets driven by Coulomb force are an important part of a droplet-based micro reactor. In this study, we realized the rapid oscillatory motion of droplets both in oil and on superhydrophobic surface by injecting charges through corona discharge. Distinct from the oscillatory motion of water droplets under a DC electric field, charge injection can make the motion of water droplets more flexible. A droplet in the oil layer can move up and down regularly under the action of corona discharge, and the discharge voltage can control the movement period and height of the droplet. In addition, the left-right translation of droplets on a superhydrophobic surface can be achieved by injecting charges into the hydrophobic film surface through corona discharge. Two kinds of droplet motion behaviors are systematically analyzed, and the mechanism of droplet motion is explained. The present results could help establish new approaches to designing efficient machines in microfluidics and micromechanical equipment.