Fast Li+ Transport via Silica Network-Driven Nanochannels in Ionomer-in-Framework for Lithium Metal Batteries

For the development of all-solid-state lithium metal batteries (LMBs), a high-porous silica aerogel (SA)-reinforced single-Li⁺ conducting nanocomposite polymer electrolyte (NPE) is prepared via two-step selective functionalization. The mesoporous SA is introduced as a mechanical framework for NPE as well as a channel for fast lithium cation migration. Two types of monomers containing weak-binding imide anions and Li⁺ cations are synthesized and used to prepare NPEs, where these monomers are grafted in SA to produce SA-based NPEs (SANPEs) as ionomer-in-framework. This hybrid SANPE exhibits high ionic conductivities (≈10⁻³ S cm⁻¹), high modulus (≈10⁵ Pa), high lithium transference number (0.84), and wide electrochemical window (>4.8 V). The resultant SANPE in the lithium symmetric cell possesses long-term cyclic stability without short-circuiting over 800 h under 0.2 mA cm⁻². Furthermore, the LiFePO₄|SANPE|Li solid-state batteries present a high discharge capacity of 167 mAh g⁻¹ at 0.1 C, good rate capability up to 1 C, wide operating temperatures (from −10 to 40 °C), and a stable cycling performance with 97% capacity retention and 100% coulombic efficiency after 75 cycles at 1 C and 25 °C. The SANPE demonstrates a new design principle for solid-state electrolytes, allowing for a perfect complex between inorganic silica and organic polymer, for high-energy-density LMBs.     

Aggregative and stochastic model of main path identification: a case study on graphene

This paper suggests a new method to search main path, as a knowledge trajectory, in the citation network. To enhance the performance and remedy the problems suggested by other researchers for main path analysis (Hummon and Doreian, Social Networks 11(1): 39–63, 1989), we applied two techniques, the aggregative approach and the stochastic approach. The first technique is used to offer improvement of link count methods, such as SPC, SPLC, SPNP, and NPPC, which have a potential problem of making a mistaken picture since they calculate link weights based on a individual topology of a citation link; the other technique, the second-order Markov chains, is used for path dependent search to improve the Hummon and Doreian’s priority first search method. The case study on graphene that tested the performance of our new method showed promising results, assuring us that our new method can be an improved alternative of main path analysis. Our method’s beneficial effects are summed up in eight aspects: (1) path dependent search, (2) basic research search rather than applied research, (3) path merge and split, (4) multiple main paths, (5) backward search for knowledge origin identification, (6) robustness for indiscriminately selected citations, (7) availability in an acyclic network, (8) completely automated search.     

Experiment and analysis of the effect of BCB sealing ring flatness on BCB cap transfer packaging

Microsystem Technologies - This paper presents the effect of BCB sealing ring flatness on BCB bonding for wafer-scale BCB cap transfer packaging. BCB sealing ring has shown partial bonding or full...     

Constrained isogeometric design optimization of lattice structures on curved surfaces: computation of design velocity field

This paper presents an immersed boundary approach for level set topology optimization considering stress constraints. A constraint agglomeration technique is used to combine the local stress constraints into one global constraint. The structural response is predicted by the eXtended Finite Element Method. A Heaviside enrichment strategy is used to model strong and weak discontinuities with great ease of implementation. This work focuses on low-order finite elements, which given their simplicity are the most popular choice of interpolation for topology optimization problems. The predicted stresses strongly depend on the intersection configuration of the elements and are prone to significant errors. Robust computation of stresses, regardless of the interface position, is essential for reliable stress constraint prediction and sensitivities. This study adopts a recently proposed fictitious domain approach for penalization of displacement gradients across element faces surrounding the material interface. In addition, a novel XFEM informed stabilization scheme is proposed for robust computation of stresses. Through numerical studies the penalized spatial gradients combined with the stabilization scheme is shown to improve prediction of stresses along the material interface. The proposed approach is applied to the benchmark topology optimization problem of an L-shaped beam in two and three dimensions using material-void and material-material problem setups. Linear and hyperelastic materials are considered. The stress constraints are shown to be efficient in eliminating regions with high stress concentration in all scenarios considered.     

Improving Korean verb-verb morphological disambiguation using lexical knowledge from unambiguous unlabeled data and selective web counts

This paper deals with verb-verb morphological disambiguation of two different verbs that have the same inflected form. The verb-verb morphological ambiguity (VVMA) is one of the critical Korean parts of speech (POS) tagging issues. The recognition of verb base forms related to ambiguous words highly depends on the lexical information in their surrounding contexts and the domains they occur in. However, current probabilistic morpheme-based POS tagging systems cannot handle VVMA adequately since most of them have a limitation to reflect a broad context of word level, and they are trained on too small amount of labeled training data to represent sufficient lexical information required for VVMA disambiguation.In this study, we suggest a classifier based on a large pool of raw text that contains sufficient lexical information to handle the VVMA. The underlying idea is that we automatically generate the annotated training set applicable to the ambiguity problem such as VVMA resolution via unlabeled unambiguous instances which belong to the same class. This enables to label ambiguous instances with the knowledge that can be induced from unambiguous instances. Since the unambiguous instances have only one label, the automatic generation of their annotated corpus are possible with unlabeled data.In our problem, since all conjugations of irregular verbs do not lead to the spelling changes that cause the VVMA, a training data for the VVMA disambiguation are generated via the instances of unambiguous conjugations related to each possible verb base form of ambiguous words. This approach does not require an additional annotation process for an initial training data set or a selection process for good seeds to iteratively augment a labeling set which are important issues in bootstrapping methods using unlabeled data. Thus, this can be strength against previous related works using unlabeled data. Furthermore, a plenty of confident seeds that are unambiguous and can show enough coverage for learning process are assured as well.We also suggest a strategy to extend the context information incrementally with web counts only to selected test examples that are difficult to predict using the current classifier or that are highly different from the pre-trained data set.As a result, automatic data generation and knowledge acquisition from unlabeled text for the VVMA resolution improved the overall tagging accuracy (token-level) by 0.04%. In practice, 9-10% out of verb-related tagging errors are fixed by the VVMA resolution whose accuracy was about 98% by using the Naïve Bayes classifier coupled with selective web counts.     

Low temperature solid–liquid interdiffusion wafer and die bonding based on PVD thin Sn/Cu films

Microsystem Technologies - Cu–Sn intermetallic diffusion has been studied with thin Sn and Cu films deposited by physical vapor deposition. It was found that annealing time is critical to the...     

Adjoint design sensitivity analysis of constant temperature molecular dynamics

In this research, we proposed an efficient design sensitivity analysis (DSA) method for constant temperature molecular dynamics (MD). A Nose–Hoover thermostat is utilized to represent the possible state of a system that is in thermal equilibrium using a heat bath to maintain temperature constant. The design sensitivity of general performance measures is derived using an adjoint variable method. Since the adjoint system is path-dependent and derived in the form of a terminal value problem, the path of original MD analysis should be kept to be used with in the adjoint sensitivity computation. The time reversibility of the MD system with Nose–Hoover thermostat is investigated. The accuracy and efficiency of the developed adjoint DSA method are verified through demonstrative numerical examples.     

A study on effect of wafer bow in wafer-level BCB cap transfer packaging

This paper investigates the effects of wafer bow of Si carrier wafer to achieve high-yield BCB cap transfer in wafer-scale packaging. BCB caps are built-up on Si carrier wafer and then they are bonded and transferred to a target wafer. The height of BCB cap is 25 μm and the thickness of Si carrier wafer is 380 μm. Through several experiments, it is found that BCB cap transfer rate is mainly dependent on wafer bow of Si carrier wafer rather than that of the target wafer due to relatively large thickness of BCB caps. Therefore, Si carrier wafer bow with the BCB layers is investigated as a function of temperature. It is to figure out the effect of the wafer bow at certain temperature and applying pressure on BCB cap transfer rate. Through the study, it is found that zero wafer bow is very important for the cap transfer. Hence, aluminum metal layer is introduced to compensate the existing wafer bow of the Si carrier wafer.     

A globally convergent sequential convex programming using an enhanced two-point diagonal quadratic approximation for structural optimization

In this study, we propose a sequential convex programming (SCP) method that uses an enhanced two-point diagonal quadratic approximation (eTDQA) to generate diagonal Hessian terms of approximate functions. In addition, we use nonlinear programming (NLP) filtering, conservatism, and trust region reduction to enforce global convergence. By using the diagonal Hessian terms of a highly accurate two-point approximation, eTDQA, the efficiency of SCP can be improved. Moreover, by using an appropriate procedure using NLP filtering, conservatism, and trust region reduction, the convergence can be improved without worsening the efficiency. To investigate the performance of the proposed algorithm, several benchmark numerical examples and a structural topology optimization problem are solved. Numerical tests show that the proposed algorithm is generally more efficient than competing algorithms. In particular, in the case of the topology optimization problem of minimizing compliance subject to a volume constraint with a penalization parameter of three, the proposed algorithm is found to converge well to the optimum solution while the other algorithms tested do not converge in the maximum number of iterations specified.