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A comprehensive review relevant to the design and fabrication of nanopositioning stages based on additive manufacturing (AM) technology has been for the first time introduced in the academic society. With the development of AM technology, AM has been applied in many engineering design areas such as aerospace, automotive, consumer electronics, and so on. Due to current limitations of AM tolerance (surface quality, form error), process obscurity (melting pool, layer adhesion), and cost (especially for metals or composites), there are only a small number of AM-applied devices that are currently available either in the market or in many industry sectors above. The flexure mechanisms that are typically employed in nanopositioning applications can provide a sub-nanometer resolution motion; however, their current manufacturing methods (milling, electric discharge machining, water jet machining) not only limit complicated flexure mechanisms in 3D geometries but also prevent designers from challenging novel topology optimizations. AM can overcome those limitations of fabrication and material distribution. Furthermore, it can allow new design approaches to topology optimizations. This review presents current and future AM-based precision motion device applications. Here, both design and fabrication of flexure mechanisms applied with current AM technology and the potential of further developments were discussed.

We investigated the microstructural characteristics and mechanical properties of stainless-steel 316L parts fabricated by directed energy deposition (DED) process, which is one of the additive manufacturing (AM) technologies. In this research, the 316L parts were fabricated by DED process by varying three process parameters: Laser power, scanning speed and mass flow rate of powder. A total of eight experimental cases were sorted out, and the DED parts from each experimental case were characterized in views of composition, defects, geometrical height, micro-hardness, friction and modulus. The analysis showed that the mechanical properties–micro-hardness, friction and modulus–of the 316L parts can be maximized in the case of the low laser power (400 W), high scanning speed (10 mm/s) and low mass flow rate of powder (10 g/min). In addition, the defects such as blowholes and cracks can be minimized under the condition of the low laser power (400 W) and low mass flow rate (10 g/min), respectively.

This paper explores the characteristics of a new lightweight thermoplastic composite filament filled with hollow glass beads developed for material extrusion additive manufacturing. Compounding experiments, which mix hollow glass beads with neat acrylonitrile butadiene styrene matrix, were conducted using a twin-screw extruder to prepare composite filaments. Two different types of hollow glass beads were selected as the fillers of composite filament due to their varying densities. In order to characterize the final components produced using composite filament, various specimens were fabricated by a material extrusion additive manufacturing process. In order to characterize the physical properties of the specimens, measurements of density and flexural testing were performed. To identify the thermomechanical effects of hollow glass beads on the neat acrylonitrile butadiene styrene matrix, thermal diffusivity and specific heat were obtained. Consequently, the thermal conductivity of the specimen was derived from its density, thermal diffusivity, and specific heat capacity. The microstructures of the fractured interfaces of the specimens were also observed by scanning electron microscopy. The experimental results revealed that most of the hollow glass beads survived, thus bringing about lighter weight (lower density) and thermal insulation (lower thermal conductivity), which can be useful for numerous potential applications.

Purpose. To investigate the relationship between macular thickness and axial length (AL) in myopic eyes. Methods. We included 441 myopic eyes in this study and measured macular thickness at the fovea and in other macular regions, using optical coherence tomography. We got thickness difference indices (TDIs) which by definition are the values of thickness difference obtained by subtracting the foveal thickness from that of each macula sector to evaluate macular contour. We then analyzed the relationships between AL and foveal thickness and AL and the TDIs of each macular sector. Results. In polynomial regression analyses, foveal thickness slope was relatively flat up to an AL of 25.5 mm and began to rise from 25.5–26.0 mm. The TDIs were also relatively flat up to AL of 25.5mm and started to show steepened negative slopes from around AL of 25.5 mm. When grouping myopia participants as high myopia or non-high myopia based on AL of 25.5mm, all macular indices of the high myopia group showed significant correlation with AL (all p values Conclusions. Average macular thickness profiles showed that appreciable changes started at an AL of 25.5mm.

In this article, a data-driven approach is applied to develop a health monitoring and diagnosis framework for a fused deposition modeling process based on a machine learning algorithm. For the data-driven approach, three accelerometers, an acoustic emission sensor, and three thermocouples are installed, and associated data are collected from those sensors. The collected data are processed to obtain root mean square values, and they are used for constructing health monitoring and diagnosis models for the fused deposition modeling process based on a support vector machine algorithm, which is one of machine learning algorithms. Among various root mean square values, those of acceleration data from the frame were most effective for diagnosing health states of the fused deposition modeling process with the non-linear support vector machine–based model.

Recently, it has become more important to screen candidates that undergo corneal refractive surgery to prevent complications. Until now, there is still no definitive screening method to confront the possibility of a misdiagnosis. We evaluate the possibilities of machine learning as a clinical decision support to determine the suitability to corneal refractive surgery. A machine learning architecture was built with the aim of identifying candidates combining the large multi-instrument data from patients and clinical decisions of highly experienced experts. Five heterogeneous algorithms were used to predict candidates for surgery. Subsequently, an ensemble classifier was developed to improve the performance. Training (10,561 subjects) and internal validation (2640 subjects) were conducted using subjects who had visited between 2016 and 2017. External validation (5279 subjects) was performed using subjects who had visited in 2018. The best model, i.e., the ensemble classifier, had a high prediction performance with the area under the receiver operating characteristic curves of 0.983 (95% CI, 0.977–0.987) and 0.972 (95% CI, 0.967–0.976) when tested in the internal and external validation set, respectively. The machine learning models were statistically superior to classic methods including the percentage of tissue ablated and the Randleman ectatic score. Our model was able to correctly reclassify a patient with postoperative ectasia as an ectasia-risk group. Machine learning algorithms using a wide range of preoperative information achieved a comparable performance to screen candidates for corneal refractive surgery. An automated machine learning analysis of preoperative data can provide a safe and reliable clinical decision for refractive surgery.

To compare the achieved vault using the conventional manufacturer's nomogram and the predicted vault using machine learning, in a large cohort of eyes undergoing posterior chamber phakic intraocular lens (EVO implantable collamer lens [ICL]; STAAR Surgical) implantation.Reliability and validity assessment and comparison.Our study comprised a total of 1,745 eyes of 1,745 consecutive patients (mean age ± standard deviation, 26.2 ± 6.8 years) undergoing ICL implantation. At 1 month postoperatively, we quantitatively measured the actual vault, and compared it with the predicted vault using machine leaning of anterior segment optical coherence tomography metrics.All machine learning methods, such as support vector regressor (SVR), gradient boost regressor (GBR), random forest regressor (RFR), and linear regressor (LR), showed significantly less mean absolute error and higher percentages of eyes within 50-200 µm of the targeted ICL vault than the conventional nomogram (P.001). The RFR provided the lowest mean absolute errors and the highest percentages of eyes within 50 to 200 µm, followed by the GBR, the LR, and the SVR (P.01).Machine learning of the preoperative anterior segment optical coherence tomography metrics, especially the RFR, provided significantly higher predictability of the ICL vault than the conventional manufacturer's nomogram, suggesting that it will become an aid for predicting the ICL vault and subsequently selecting the proper ICL size in daily practice.

We retrospectively compared the central corneal thickness (CCT) obtained by ultrasound pachymetry (USP; SP-3000, Tomey Corp., Nagoya, Japan), non-contact tonopachy (TP) (NT-530P, Nidek Co., Ltd., Gamagori, Japan), Pentacam HR (OCULUS Inc., Wetzlar, Germany), and RTVue optical coherence tomography (OCT) (Optovue Inc., Fremont, CA, USA) in 78 eyes of 78 healthy subjects with myopia. Agreement between the measurement methods was evaluated using 95% confidence intervals for the limits of agreement (LoA). The mean CCT values were 546.9 ± 34.7, 548.1 ± 33.5, 559.2 ± 34.0, and 547.2 ± 34.8 μm for USP, non-contact TP, Pentacam, and RTVue, respectively. The thickest and the thinnest mean CCT values corresponded to those obtained by Pentacam HR and USP, respectively. Plots of the differences against the means showed the best agreement between USP and RTVue (LoA, 10.14-10.70 μm), while the largest discrepancy was observed between RTVue and Pentacam systems (LoA, -25.47-1.44 μm). Our data showed that CCT measurements using these 4 instruments were well correlated. However, the results from Pentacam differed significantly from those of the other instruments.

Purpose Recently, laser refractive surgery options, including laser epithelial keratomileusis, laser in situ keratomileusis, and small incision lenticule extraction, successfully improved patients' quality of life. Evidence-based recommendation for an optimal surgery technique is valuable in increasing patient satisfaction. We developed an interpretable multiclass machine learning model that selects the laser surgery option on the expert level. Methods A multiclass XGBoost model was constructed to classify patients into four categories including laser epithelial keratomileusis, laser in situ keratomileusis, small incision lenticule extraction, and contraindication groups. The analysis included 18,480 subjects who intended to undergo refractive surgery at the B&VIIT Eye center. Training (n = 10,561) and internal validation (n = 2640) were performed using subjects who visited between 2016 and 2017. The model was trained based on clinical decisions of highly experienced experts and ophthalmic measurements. External validation (n = 5279) was conducted using subjects who visited in 2018. The SHapley Additive ex-Planations technique was adopted to explain the output of the XGBoost model. Results The multiclass XGBoost model exhibited an accuracy of 81.0% and 78.9% when tested on the internal and external validation datasets, respectively. The SHapley Additive ex-Planations explanations for the results were consistent with prior knowledge from ophthalmologists. The explanation from one-versus-one and one-versus-rest XGBoost classifiers was effective for easily understanding users in the multicategorical classification problem. Conclusions This study suggests an expert-level multiclass machine learning model for selecting the refractive surgery for patients. It also provided a clinical understanding in a multiclass problem based on an explainable artificial intelligence technique. Translational relevance Explainable machine learning exhibits a promising future for increasing the practical use of artificial intelligence in ophthalmic clinics.

In this paper, a new hybrid manufacturing process was proposed to fabricate biomimetic intestinal villi. The hybrid process consists of two steps, i.e., a micro-drilling process to make steep holes and a laser-based machining process to smoothen their geometries. Polycarbonate (PC) was used as a candidate material for mold, and villi’s scaffold was fabricated using polydimethylsiloxane (PDMS). For the hybrid manufacturing process, conditions of the micro-drilling process were optimized first and then a series of laser-based machining experiments were performed using the pre-produced micro-drilled molds. A full factorial design of experiments was conducted to investigate the effects of laser power, air, and the objective lens, and the shape of villi was observed as an output to evaluate the process performance. Experimental results demonstrated that the proposed hybrid manufacturing process is able to fabricate villi whose geometry is consistent and suitable for cell culturing. Consequently, villi scaffold with various dimensions could be conveniently built using different parameters, and it could be applied to personalized organ-on-a-chip applications in the future.

Fused deposition modeling (FDM), one of representative additive manufacturing (AM) technologies, has been widely used for fabricating functional parts with geometrical complexity. However, it has suffered from degraded part quality and low process reliability and controllability. Therefore, it is of much significance to develop a monitoring and diagnosis system for the FDM process to overcome such drawbacks. In this paper, a data-driven FDM process monitoring and diagnosis system is developed by using two types of sensors – an accelerometer and an acoustic emission (AE) sensor. A large number of experimental data, collected from the accelerometers and AE sensor under healthy and faulty process states, are processed to obtain a critical feature – a root mean square (RMS). The RMS values are then used for training the FDM process monitoring and diagnosis models based on a support vector machine (SVM) algorithm and a k-fold cross validation approach. In particular, the SVM-based models for the odd- and evennumbered layers of one FDM specimen are developed. For a real-time validation in a factory floor, the non-linear SVM-based models using the acceleration signals are used for the software development. The diagnosis accuracy is better than 87.5%, and an applicability of the models is verified.

This paper discusses the optimization of nanofluid MQL micro-drilling process of titanium alloy (Ti-6Al-4V) using nanodiamond particles based on a response surface methodology (RSM) and desirability function (DF). In order to obtain regression models of drilling torques, thrust forces and edge radii in terms of process parameters such as drill diameter, feed rate, spindle speed and nanofluid weight concentration, a series of micro-drilling experiments are performed by using a design of experiment (DOE) approach. Then, the multi-objective optimization for minimizing drilling torques, thrust forces and edge radii is carried out by introducing DF, and the optimal values of the process factors are obtained. The micro-drilling experiments with the optimal process factors are conducted, and the experimental results of drilling torque, thrust force and edge radius are similar to calculated ones. Thus, the validity of the regression models of drilling torques, thrust forces and edge radii are demonstrated. The developed regression models can be used to find dominant parameters influencing the drilling performances and to practically guide operators to choose optimal values for the enhanced drilling performances.

Nanoliposomal irinotecan (nal-IRI) is a liposomal formulation of irinotecan with a longer half-life (t1/2 ), higher plasma total irinotecan (tIRI), and lower SN-38 maximum concentration (Cmax ) compared with nonliposomal irinotecan. Population pharmacokinetic (PK) analysis of nal-IRI was performed for tIRI and total SN-38 (tSN38) using patient samples from six studies. PK-safety association was evaluated for neutropenia and diarrhea in 353 patients. PK-efficacy association was evaluated from a phase III study in pancreatic cancer NAPOLI1. Efficacy was associated with longer duration of unencapsulated SN-38 (uSN38) above a threshold and higher Cavg of tIRI, tSN38, and uSN38. Neutropenia was associated with uSN38 Cmax and diarrhea with tIRI Cmax . Baseline predictive factors were race, body surface area, and bilirubin. Analysis identified PK factors associated with efficacy, safety, and predictive baseline factors. The results support the benefit of nal-IRI dose of 70 mg/m2 (free-base; equivalent to 80 mg/m2 salt base) Q2W over 100 mg/m2 Q3W.

The objective of this research is to experimentally characterize the micro-end-milling process of titanium alloy (Ti-6Al-4V) using both nanofluid minimum quantity lubrication (MQL) and chilly gas. A series of micro-end-milling experiments are conducted by considering nanodiamond particles for nanofluid MQL. In the case of chilly gas cooling, the chilly CO2 gas having the temperature of −25 °C is provided. During the experiments, the milling forces, coefficients of friction, surface roughness and tool wear are analysed according to types of nanofluids and their weight concentrations. Overall, the experimental results show that the nanofluid MQL with chilly CO2 gas cooling is effective for reducing milling forces, coefficients of friction, tool wear and surface roughness. It is also demonstrated that the lower concentration (0.1 wt.%) of nanodiamond nanofluid is more effective to reduce the milling force, coefficient of friction and tool wear. On the other hand, it is found that the higher concentration (1.0 wt.%) is more advantageous for reducing the surface roughness.

This study was aimed to evaluate refractive and visual outcomes after micro-monovision small incision lenticule extraction (SMILE) in patients with presbyopia and myopia. In total, 72 patients (144 eyes) with a mean age of 46.0 ± 4.9 years were included in this study. The dominant eye was treated for distance vision and the nondominant eye for near vision by targeting between −0.50 and −1.75 diopters (D). Treatment efficacy, safety, and refractive stability were calculated from postoperative data including refraction, binocular uncorrected distance visual acuity (UDVA), binocular uncorrected near visual acuity, monocular uncorrected distance visual acuity, and monocular corrected distance visual acuity (CDVA). Six months post-surgery, binocular UDVA was better than or equal to 20/20 in 88% of patients. No loss in 2 or more lines was observed in the Snellen lines of corrected distance visual acuity. Mean spherical equivalent (SE) for the distance eye was −0.18 ± 0.37 D, whereas the attempted and achieved SE in the near eye were −0.90 ± 0.44 and −0.99 ± 0.54 D, respectively. In total, 79% of eyes were within ±0.50 D, and 98% within ±1.00 D, of the intended refraction. A UDVA of 0.0 logMAR (20/20) or better, and an uncorrected near visual acuity of Jaeger (J) of 3 (20/32) or better, were observed in 83% of patients. Micromonovision refractive surgery using SMILE enhanced functional near vision in presbyopic patients.

Background and objective Retinal imaging has two major modalities, traditional fundus photography (TFP) and ultra-widefield fundus photography (UWFP). This study demonstrates the feasibility of a state-of-the-art deep learning-based domain transfer from UWFP to TFP. Methods A cycle-consistent generative adversarial network (CycleGAN) was used to automatically translate the UWFP to the TFP domain. The model was based on an unpaired dataset including anonymized 451 UWFP and 745 TFP images. To apply CycleGAN to an independent dataset, we randomly divided the data into training (90%) and test (10%) datasets. After automated image registration and masking dark frames, the generator and discriminator networks were trained. Additional twelve publicly available paired TFP and UWFP images were used to calculate the intensity histograms and structural similarity (SSIM) indices. Results We observed that all UWFP images were successfully translated into TFP-style images by CycleGAN, and the main structural information of the retina and optic nerve was retained. The model did not generate fake features in the output images. Average histograms demonstrated that the intensity distribution of the generated output images provided a good match to the ground truth images, with an average SSIM level of 0.802. Conclusions Our approach enables automated synthesis of TFP images directly from UWFP without a manual pre-conditioning process. The generated TFP images might be useful for clinicians in investigating posterior pole and for researchers in integrating TFP and UWFP databases. This is also likely to save scan time and will be more cost-effective for patients by avoiding additional examinations for an accurate diagnosis.

Germanium-based multimetallic-oxide materials have attracted significant attention as high-capacity anodes for next generation lithium-ion batteries (LIBs). However, they suffer from poor cyclic stability due to extreme volume expansion and reduced electrical conductivity after repeated cycles. To circumvent these issues, we propose that Ge-based multimetallic-oxide nanowires can be synthesized with electrically conductive carbon to significantly enhance the cyclic stability of the Ge-based anodes. We prepare conformal-carbon-coated Zn 2 GeO 4 nanowires (NWs) using a microwave-induced hydrothermal method with subsequent thermal decomposition. The obtained carbon-coated Zn 2 GeO 4 -NW anode exhibits a discharge capacity of 485 mAh/g and a Coulombic efficiency (CE) of 98.4% after 900 cycles at 0.6 C. Furthermore, these anodes exhibit outstanding rate-capability characteristics, even with an increased C-rate of 17.7 C. This excellent electrochemical performance can be ascribed to the improved electron and ion transport provided and the structurally reinforced conductive layer comprising a conformal carbon layer. Therefore, it is expected that our approach can also be applied to other multimetallic-oxide materials, resulting in large, reversible capacities; excellent cyclic stabilities; and good rate capabilities for high-performance LIBs.

The commercialization of Sn-based anodes for lithium ion batteries is still hindered due to the inherent volume change leading to a rapid capacity fading during the electrochemical cycle. Inspired by echeveria , a plant that stores sufficient water in its hierarchical leaves to survive in a drought, we report a breakthrough by designing the hierarchical and nanoporous SnO 2 electrode encapsulated with ultrathin carbon layer (∼2 nm). As evidently captured by in situ transmission electron microscopy, the conformal carbon coating on the surface of anode may provide an elastic cover that suppresses the cracks due to severe volume change, and increases both electrical and ionic conductivity, allowing the cells to exhibit excellent lithium storage performance with more than 800 cycles even with relatively high-rate of current densities.

This work describes a self-assembly method of gold nanoparticles coating on the surface of silicon thin films for the anode material of lithium secondary batteries. The preparation of the silicon thin films was carried out by electron cyclotron resonance metal organic chemical vapor deposition (ECR-MOCVD) process. The obtained films were further coated with (3-aminopropyl)-trimethoxysilane (APTMS) which has a role to bind the oxygen functional groups on Si surface and the gold nanoparticles. The dispersed gold nanoparticles on the surface of silicon thin films could be prepared due to self-assembly phenomena which interact between attraction and repulsion in gold nanoparticles colloidal solution (GNCS). The use of reducing agent of sodium citrate and tannic acid in GNCS significantly affected the size of gold nanoparticle in our experimental range. Based on our experimental results, the higher reversible capacity was exhibited for the silicon that was immersed in the GNCS consisted of only sodium citrate. The GNCS consisted of both sodium citrate and tannic acid produced severe coagulated nanoparticles when deposited on the silicon surface and thus inhibited the lithium movement from electrolyte to silicon surface. Consequently, the reversible capacity of silicon anode material with coagulated gold nanoparticles coating showed the reduced performance.

Nanostructured silicon synthesis by surface modification of commercial micro-powder silicon was investigated in order to reduce the maximum volume change over cycle. The surface of micro-powder silicon was modified using an Ag metal-assisted chemical etching technique to produce nanostructured material in the form of bundle-type silicon nanorods. The volume change of the electrode using the nanostructured silicon during cycle was investigated using an in-situ dilatometer. Our result shows that nanostructured silicon synthesized using this method showed a self-relaxant characteristic as an anode material for lithium ion battery application. Moreover, binder selection plays a role in enhancing self-relaxant properties during delithiation via strong hydrogen interaction on the surface of the silicon material. The nanostructured silicon was then coated with carbon from propylene gas and showed higher capacity retention with the use of polyacrylic acid (PAA) binder. While the nano-size of the pore diameter control may significantly affect the capacity fading of nanostructured silicon, it can be mitigated via carbon coating, probably due to the prevention of Li ion penetration into 10 nano-meter sized pores.

Practical application of silicon anodes for lithium-ion batteries has been mainly hindered because of their low electrical conductivity and large volume change (ca. 300%) occurring during the lithiation and delithiation processes. Thus, the surface engineering of active particles (material design) and the modification of electrode structure (electrode design) of silicon are necessary to alleviate these critical limiting factors. Silicon/carbon core–shell particles (Si@C, material design) are prepared by the thermal decomposition and subsequent three-dimensional (3D) electrode structures (electrode design) with a channel width of 15 μm are incorporated using the laser ablation process. The electrochemical characteristics of 3D Si@C used as the anode material for lithium-ion batteries are investigated to identify the effects of material and electrode design. By the introduction of a carbon coating and the laser structuring, an enhanced performance of Si anode materials exhibiting high specific capacity (>1200 mAh g −1 over 300 cycles), good rate capability (1170 mAh g −1 at 8 A g −1 ), and stable cycling is achieved. The morphology of the core–shell active material combined with 3D channel architecture can minimize the volume expansion by utilizing the void space during the repeated cycling.

Engineered silicon and carbon composite materials have been demonstrated to be promising anode materials for lithium-ion batteries. In this paper, we demonstrate a facile approach to fabricating silicon/carbon composite electrodes via simple mixing of silicon nanoparticles (SNPs) and carbon nanospheres (CNSs). Size-monodisperse CNSs were obtained by the direct carbon conversion of polystyrene spheres. The mixture of SNPs with size-monodisperse CNSs allowed for a uniform dispersion with minimal SNP aggregation, which was evaluated through direct comparison of the SNP mixture with micrometer-sized carbon particles. This SNP/CNS composite electrode, containing 30 wt% SNPs, exhibited a reversible specific capacity of 1023 mA h g−1 at 100 mA g−1, which was more than three times higher than that of a bare CNS electrode. The capacity retention after 50 cycles was 73%, with a high Coulombic efficiency of 99%; in addition, the capacity retention was approximately 70% when the current density was increased tenfold. Specifically, the SNP/CNS electrode exhibited substantially less capacity fade than did SNPs dispersed in micrometer-sized carbon particles. Moreover, the SNP/CNS electrode exhibited improved capacity retention under high-current-density conditions compared to that of previously developed silicon/carbon composite anodes. We attributed this improved LIB performance to the uniform dispersion of SNPs in the CNS matrix, which resulted in stable SEI formation and effective accommodation of the volume change of silicon. We believe our CNS structures can be extended to other electrode-material systems that require a large volume change and good mass transport characteristics.

The confinement and uniform dispersion of Si nanoparticles (NPs) in a carbon matrix is a promising strategy to accommodate the problematic large volume change of high-capacity Si-based anodes during charging/discharging in lithium-ion batteries. Here, we introduced an inverse opal carbon (IOC) matrix for Si NPs dispersion. A macroporous, highly interconnected porous structure of IOCs allowed uniform dispersion of Si NPs by simple mixing, and large voids that surrounded the Si NPs. The Si NPs/IOC composite anode exhibited a specific capacity up to 1233 mA g−1 with 40 wt% Si NPs content, which was 4.1 times higher than the IOC anode. The capacity retention over 50 cycles for the Si NPs/IOC anode (40 wt% Si NPs/IOC) was as high as 81%, whereas the Si NPs anode revealed only 48% retention. The specific capacity was also maintained up to 70% as the current density increased 20-fold. These enhancement of the capacity retention and the current density were attributed to the efficient accommodation of the Si volume change in the IOC structure and facile charge transport in and through the IOC matrix. We believe this simple geometry-derived approach for the dispersion of nanoparticles may be advantageous in the practical application of high-capacitive nanomaterials for next-generation lithium ion battery (LIB) development.

This study investigates bundle-type silicon nanorods (BSNR) that are aimed at improving the discharge capacity and life cycle characteristics of secondary cells, by controlling the shape and etching depth of silicon thick-films produced by electroless etching. The prepared BSNR structure is composed of a columnar bundle, having a diameter of 100 nm and lengths of 1.5 and 3.5 μm. The etching depths of the nanorods have a significant effect on the electrochemical performance characteristics, including the capacity fading and coulombic efficiency. Using a BSNR electrode therefore allows for an anode with a high capacity and efficiency in lithium ion cells, and can help overcome the issues associated with conventional silicon thick-films. Furthermore, as a result of its unique self-relaxant structure, electrode deterioration is improved through mitigation of the volume change.

We hypothesized that the ocular dominance associated with brain asymmetry would influence macular structure. We assessed the structural asymmetry of the macula by ocular dominance.A total of 144 patients with no ophthalmic abnormalities were examined. After classifying eyes as dominant or non-dominant using the hole-in-a-card test, the macular ganglion cell inner plexiform layer and the macular thickness were measured in both groups using spectral domain optical coherence tomography, and we sought associations between ocular dominance and retinal thickness.Ocular dominance was more frequently found in right eyes than in left eyes (68.75% vs 31.25%, respectively; p0.001). Using conditioned logistic regression, the right eye and the temporal-to-nasal retinal thickness ratio of the macular ganglion cell inner plexiform layer were selected as a final model for the determination of ocular dominance (p0.001, p = 0.013 respectively).Macular vertical asymmetry was associated with ocular dominance. The macular structure and ocular dominance associated with visual cortex lateralization have functional and structural relationships.

This paper investigates a new technology to create functionally graded material (FGM) by additive manufacturing (AM). In particular, this paper focuses on creating graphene-polymer composite FGM by laser-based sintering processes. Graphene-polymer composites have received high attention in AM due to their excellent electrical conductivity, thermal stability and mechanical strength. However, AM of the graphene-polymer composites has a huge challenge to overcome. The heterogeneous materials should be mixed properly, and it is not easy to achieve the desired composite characteristics solely by changing the mass ratio of graphene. This paper shows a newly developed laser-assisted AM system for the graphene-polymer composite FGM by laser-based sintering processes. The paper also describes two methods of material integration: mixing graphene and polyethylene powders before sintering, and depositing the different material powders separately and sintering them. This study identified that the two methods led to different mechanical and electrical properties of the created parts. Thus this paper demonstrates the possibility to create quite useful hybrid (mechanically and electrically) FGM composites.

Nuclear magnetic resonance (NMR) spectroscopy has provided a unique opportunity to study the characteristic smectic-A to chiral nematic phase transition in a dimesogenic liquid crystal (“KI-5S”). The order parameters in the liquid crystalline phases were obtained from the 2 H NMR quadrupole splitting and 13 C NMR chemical shift measurements, manifesting a first-order smectic-nematic phase transition.

Carbon coated prickle-like Si particles (PS@C) are prepared by metal-assisted chemical etching and subsequent coating with an amorphous carbon film carried out by thermal chemical vapor deposition (CVD). The electrochemical characteristics of PS@C employed as anode material for lithium-ion batteries are investigated in order to find a relationship between interfacial properties and electrochemical performance. The unique morphology of prickle-like Si (PS) having empty space can accommodate volume expansion during the lithiation and delithiation. Additionally, an amorphous carbon coating layer with a thickness of 10–15 nm deposited onto the PS prepared by thermal CVD is investigated as an effective way to enhance the cycle stability and rate capability of the PS electrode due to improved interfacial characteristics. The micro and nano hybrid structure of the PS material combined with the 12 wt.% amorphous carbon layer plays an important role in enhancing the electrochemical performance.

The electrochemical and compositional changes of a solid electrolyte interphase (SEI) layer formed on the surface of silicon thin film are investigated in order to determine the effect of the content of fluoroethylene carbonate (FEC) additive in the electrolyte. Comparisons are made with FEC-free electrolyte, in which the major components are (CH 2 OCO 2 Li) 2 and Li 2 CO 3 . The (CH 2 OCO 2 Li) 2 and Li 2 CO 3 of the SEI layer in the FEC-containing electrolyte decreases, and polycarbonate and LiF increase relatively with the repression of –OCO 2 Li groups. The additive affects the composition of the SEI layer, which leads to lower resistance. The electrochemical performance regarding cycle retention, coulombic efficiency, rate capability, and discharge capacity in the FEC-containing cell are significantly enhanced compared to that of the FEC-free electrolyte. The observed optimum FEC concentration in the electrolyte is 1.5%, due to the reduced charge transfer and SEI resistance in our experimental range.

Although Si is a promising high-capacity anode material for Li-ion batteries (LIB), it suffers from capacity fading due to excessively large volumetric changes upon Li insertion. Nanocarbon materials have been used to enhance the cyclic stability of LIB anodes, but they have an inherently low specific capacity. To address these issues, we present a novel ternary nanocomposite of Si, Mn, and reduced graphene oxide (rGO) for LIB anodes, in which the Si-Mn alloy offers high capacity characteristics and embedded rGO nanosheets confer structural stability. Si-Mn/rGO ternary nanocomposites were synthesized by mechanical complexation and subsequent thermal reduction of mixtures of Si nanoparticles, MnO2 nanorods, and rGO nanosheets. Resulting ternary nanocomposite anodes displayed a specific capacity of 600 mAh/g with ∼90% capacity retention after 50 cycles at a current density of 100 mA/g. The enhanced performance is attributed to facilitated Li-ion reactions with the MnSi alloy phase and the formation of a structurally reinforced electroconductive matrix of rGO nanosheets. The ternary nanocomposite design paradigm presented in this study can be exploited for the development of high-capacity and long-life anode materials for versatile LIB applications.