Reaction mechanism of nickel sulfide atomic layer deposition using bis(N,N'-di-tert-butylacetamidinato)nickel(II) and hydrogen sulfide.

As a unique nanofabrication technology, atomic layer deposition (ALD) has been used in the microelectronics, catalysis, environmental and energy fields. As an energy and catalytic material, nickel sulfide has excellent electrochemical and catalytic activities and has attracted extensive attention. In this work, the reaction mechanism for nickel sulfide ALD from an amidine metal precursor was investigated using density functional theory (DFT) calculations. The results show that the first amidine ligand of bis(N,N'-di-tert-butylacetamidinato)nickel(II) [Ni(tBu-MeAMD)2] can be easily eliminated on the sulfhydrylated surface. The second amidine ligand can also react with the adjacent sulfhydryl group to generate the N,N'-di-tert-butylacetamidine (tBu-MeAMD-H) molecule, which can strongly interact with the Ni atom on the surface and be difficult to be desorbed. In the subsequent H2S reaction, the tBu-MeAMD-H molecule can be exchanged with the H2S precursor. Ultimately, the tBu-MeAMD-H molecule can be desorbed and H2S can be dissociated to form two sulfhydrylated groups on the surface. Meanwhile, the -SH of a H2S molecule can be exchanged with the second tBu-MeAMD ligand. These insights into the reaction mechanism of nickel sulfide ALD can provide theoretical guidance to design the metal amidinate precursors and improve the ALD process for metal sulfides.

Neural Network-Based Active Load-Sensing Scheme and Stiffness Adjustment for Pneumatic Soft Actuators for Minimally Invasive Surgery Support.

To provide a stable surgical view in Minimally Invasive Surgery (MIS), it is necessary for a flexible endoscope applied in MIS to have adjustable stiffness to resist different external loads from surrounding organs and tissues. Pneumatic soft actuators are expected to fulfill this role, since they could feed the endoscope with an internal access channel and adjust their stiffness via an antagonistic mechanism. For that purpose, it is essential to estimate the external load. In this study, we proposed a neural network (NN)-based active load-sensing scheme and stiffness adjustment for a soft actuator for MIS support with antagonistic chambers for three degrees of freedom (DoFs) of control. To deal with the influence of the nonlinearity of the soft actuating system and uncertainty of the interaction between the soft actuator and its environment, an environment exploration strategy was studied for improving the robustness of sensing. Moreover, a NN-based inverse dynamics model for controlling the stiffness of the soft actuator with different flexible endoscopes was proposed too. The results showed that the exploration strategy with different sequence lengths improved the estimation accuracy of external loads in different conditions. The proposed method for external load exploration and inverse dynamics model could be used for in-depth studies of stiffness control of soft actuators for MIS support.

Reaction mechanism of atomic layer deposition of aluminum sulfide using trimethylaluminum and hydrogen sulfide.

Atomic layer deposition (ALD) is a nanopreparation technique for materials and is widely used in the fields of microelectronics, energy and catalysis. ALD methods for metal sulfides, such as Al2S3 and Li2S, have been developed for lithium-ion batteries and solid-state electrolytes. In this work, using density functional theory calculations, the possible reaction pathways of the ALD of Al2S3 using trimethylaluminum (TMA) and H2S were investigated at the M06-2X/6-311G(d, p) level. Al2S3 ALD can be divided into two consecutive and complementary half-reactions involving TMA and H2S, respectively. In the TMA half-reaction, the methyl group can be eliminated through the reaction with the sulfhydryl group on the surface. This process is a ligand exchange reaction between the methyl and sulfhydryl groups via a four-membered ring transition state. TMA half-reaction with the sulfhydrylated surface is more difficult than that with the hydroxylated surface. When the temperature increases, the reaction requires more energy, owing to the contribution of the entropy. In the H2S half-reaction, the methyl group on the surface can further react with the H2S precursor via a four-membered ring transition state. The orientation of H2S and more molecules have minimal effect on the H2S half-reaction. The reaction involving H2S through a six-membered ring transition state is unfavorable. In addition, the methyl and sulfhydryl groups on the surface can both react with the adjacent sulfhydryl group on the subsurface to form and release CH4 or H2S in the two half-reactions. Furthermore, sulfhydryl elimination occurs more easily than methyl elimination on the surface. These findings for the TMA and H2S half-reactions of Al2S3 ALD may be used for studying precursor chemistry and improvements in the preparation of other metal sulfides for emerging applications.

Solving variability: Accurately extracting feature components from ballistocardiograms.

Objective: A ballistocardiogram (BCG) is a vibration signal generated by the ejection of the blood in each cardiac cycle. The BCG has significant variability in amplitude, temporal aspects, and the deficiency of waveform components, attributed to individual differences, instantaneous heart rate, and the posture of the person being measured. This variability may make methods of extracting J-waves, the most distinct components of BCG less generalizable so that the J-waves could not be precisely localized, and further analysis is difficult. This study is dedicated to solving the variability of BCG to achieve accurate feature extraction. Methods: Inspired by the generation mechanism of the BCG, we proposed an original method based on a profile of second-order derivative of BCG waveform (2ndD-P) to capture the nature of vibration and solve the variability, thereby accurately localizing the components especially when the J-wave is not prominent. Results: In this study, 51 recordings of resting state and 11 recordings of high-heart-rate from 24 participants were used to validate the algorithm. Each recording lasts about 3 min. For resting state data, the sensitivity and positive predictivity of proposed method are: 98.29% and 98.64%, respectively. For high-heart-rate data, the proposed method achieved a performance comparable to those of low-heart-rate: 97.14% and 99.01% for sensitivity and positive predictivity, respectively. Conclusion: Our proposed method can detect the peaks of the J-wave more accurately than conventional extraction methods, under the presence of different types of variability. Higher performance was achieved for BCG with non-prominent J-waves, in both low- and high-heart-rate cases.

Addressing data imbalance in Sim2Real: ImbalSim2Real scheme and its application in finger joint stiffness self-sensing for soft robot-assisted rehabilitation.

The simulation-to-reality (sim2real) problem is a common issue when deploying simulation-trained models to real-world scenarios, especially given the extremely high imbalance between simulation and real-world data (scarce real-world data). Although the cycle-consistent generative adversarial network (CycleGAN) has demonstrated promise in addressing some sim2real issues, it encounters limitations in situations of data imbalance due to the lower capacity of the discriminator and the indeterminacy of learned sim2real mapping. To overcome such problems, we proposed the imbalanced Sim2Real scheme (ImbalSim2Real). Differing from CycleGAN, the ImbalSim2Real scheme segments the dataset into paired and unpaired data for two-fold training. The unpaired data incorporated discriminator-enhanced samples to further squash the solution space of the discriminator, for enhancing the discriminator's ability. For paired data, a term targeted regression loss was integrated to ensure specific and quantitative mapping and further minimize the solution space of the generator. The ImbalSim2Real scheme was validated through numerical experiments, demonstrating its superiority over conventional sim2real methods. In addition, as an application of the proposed ImbalSim2Real scheme, we designed a finger joint stiffness self-sensing framework, where the validation loss for estimating real-world finger joint stiffness was reduced by roughly 41% compared to the supervised learning method that was trained with scarce real-world data and by 56% relative to the CycleGAN trained with the imbalanced dataset. Our proposed scheme and framework have potential applicability to bio-signal estimation when facing an imbalanced sim2real problem.

Effects of chamber shapes on maneuverability and control property of endoscope-support soft actuators.

Introduction: Minimally Invasive Surgery (MIS) offers targeted surgical access with reduced invasiveness; however, the maneuverability challenges of traditional instruments in this domain underscore the need for innovative solutions. Soft actuators activated by fluids or gases present a promising strategy for augmenting endoscopic capabilities, thereby enhancing the surgical precision in MIS. This study aimed to explore the intricate dynamics of the interactions between soft actuators and endoscopes, with an emphasis on the pivotal role of cross-sectional chamber shapes. While previous studies have touched on the influence of chamber shapes on bending properties, we provide a comprehensive exploration. We explore how these shapes modulate friction forces, which in turn influence the interactions governing bending, response, and stiffness adjustability, all of which are essential for enhancing endoscope maneuverability in MIS contexts. Methods: A novel bilateral symmetrical air chamber design was adopted to investigate various chamber shapes. We employed finite element analysis (FEA) simulations followed by prototype testing to evaluate the interactions driven by these chamber shapes and to discern their impact on actuator properties. Recognizing the pivotal role of friction in these interactions, we conducted dedicated friction experiments. These experiments further deepened our understanding of the relationship between chamber shape and friction, and how this synergy influences the properties of the actuator. Results: Our findings showed that actuators with wider chambers generate larger friction forces, thereby enhancing the interaction and improving the bending, response, and stiffness adjustability. Additionally, the soft actuator significantly improved the maneuverability and bending radius of the endoscope, demonstrating enhanced navigation capabilities in complex environments. Discussion: The shape of a cross-sectional chamber plays a pivotal role in designing soft actuators for MIS applications. Our research emphasizes the importance of this design component, offering key insights for the development of endoscope-supporting soft actuators that can effectively handle intricate actuator-endoscope interactions, thereby enhancing surgical outcomes.

REC-NN: A reconstruction error compensation neural network for Magnetic Resonance Electrical Property Tomography (MREPT).

Electrical properties (EPs) are expected as biomarkers for early cancer detection. Magnetic resonance electrical properties tomography (MREPT) is a technique to non-invasively estimate the EPs of tissues from MRI measurements. While noise sensitivity and artifact problems of MREPT are being solved progressively through recent efforts, the loss of tissue contrast emerges as an obstacle to the clinical applications of MREPT. To solve the problem, we propose a reconstruction error compensation neural network scheme (REC-NN) for a typical analytic MREPT method, Stab-EPT. Two NN structures: one with only ResNet blocks, and the other hybridizing ResNet blocks with an encoder-decoder structure. Results of experiments with digital brain phantoms show that, compared with Stab-EPT, and conventional NN based reconstruction, REC-NN improves both reconstruction accuracy and tissue contrast. It is found that, the encoder-decoder structure could improve the compensation accuracy of EPs in homogeneous region but showed worse reconstruction than only ResNet structure for tumorous tissues unseen in the training samples. Future research is required to address overcompensation problems, optimization of NN structure and application to clinical data.

Design of a SMA-based soft composite structure for wearable rehabilitation gloves.

The combination of smart soft composite structure based shape memory alloy (SMA) and exoskeleton technology has the advantages of light weight, energy saving, and great human-exoskeleton interaction. However, there are no relevant studies on the application of SMA-based soft composite structure (SSCS) in hand exoskeletons. The main difficulty is that directional mechanical properties of SSCS need to comply with fingers movement, and SSCS can deliver enough output torque and displacement to the relevant joints. This paper aims to study the application of SSCS for wearable rehabilitation gloves and explore its bionic driving mechanism. This paper proposes a soft wearable glove (Glove-SSCS) for hand rehabilitation actuated by the SSCS, based on finger force analysis under different drive modes. The Glove-SSCS can support five-finger flexion and extension, weighs only 120 g, and adopts modular design. Each drive module adopts a soft composite structure. And the structure integrates actuation, sensing and execution, including an active layer (SMA spring), a passive layer (manganese steel sheet), a sensing layer (bending sensor) and connection layers. To obtain a high-performance SMA actuators, the performance of SMA materials was tested in terms of temperature and voltage, temperature at the shortest length, pre-tensile length and load. And the human-exoskeleton coupling model of Glove-SSCS is established and analyzed from force and motion. The results show that the Glove-SSCS can realize bidirectional movements of fingers flexion and extension, with ranges of motion are 90-110° and 30-40°, and their cycles are 13-19 s and 11-13 s. During the use of Glove-SSCS, the temperature of gloves is from 25 to 67°C, and the surface temperature of hands is from 32 to 36°C. The temperature of Glove-SSCS can be kept at the lowest temperature of SMA operation without much impact on the human body.

Corrigendum: Design of a SMA-based soft composite structure for wearable rehabilitation gloves.

[This corrects the article DOI: 10.3389/fnbot.2023.1047493.].

Reaction mechanism of atomic layer deposition of zirconium oxide using zirconium precursors bearing amino ligands and water.

As a unique nanofabrication technology, atomic layer deposition (ALD) has been widely used for the preparation of various materials in the fields of microelectronics, energy and catalysis. As a high-κ gate dielectric to replace SiO2, zirconium oxide (ZrO2) has been prepared through the ALD method for microelectronic devices. In this work, through density functional theory calculations, the possible reaction pathways of ZrO2 ALD using tetrakis(dimethylamino)zirconium (TDMAZ) and water as the precursors were explored. The whole ZrO2 ALD reaction could be divided into two sequential reactions, TDMAZ and H2O reactions. In the TDMAZ reaction on the hydroxylated surface, the dimethylamino group of TDMAZ could be directly eliminated by substitution and ligand exchange reactions with the hydroxyl group on the surface to form dimethylamine (HN(CH3)2). In the H2O reaction with the aminated surface, the reaction process is much more complex than the TDMAZ reaction. These reactions mainly include ligand exchange reactions between the dimethylamino group of TDMAZ and H2O and coupling reactions for the formation of the bridged products and the by-product of H2O or HN(CH3)2. These insights into surface reaction mechanism of ZrO2 ALD can provide theoretical guidance for the precursor design and improving ALD preparation of other oxides and zirconium compounds, which are based ALD reaction mechanism.

Corrigendum: Reaction mechanism of atomic layer deposition of zirconium oxide using zirconium precursors bearing amino ligands and water.

[This corrects the article DOI: 10.3389/fchem.2022.1035902.].

First-Principles Molecular Dynamics Simulations on Water-Solid Interface Behavior of H2O-Based Atomic Layer Deposition of Zirconium Dioxide.

As an important inorganic material, zirconium dioxide (ZrO2) has a wide range of applications in the fields of microelectronics, coating, catalysis and energy. Due to its high dielectric constant and thermodynamic stability, ZrO2 can be used as dielectric material to replace traditional silicon dioxide. Currently, ZrO2 dielectric films can be prepared by atomic layer deposition (ALD) using water and zirconium precursors, namely H2O-based ALD. Through density functional theory (DFT) calculations and first-principles molecular dynamics (FPMD) simulations, the adsorption and dissociation of water molecule on the ZrO2 surface and the water-solid interface reaction were investigated. The results showed that the ZrO2 (111) surface has four Lewis acid active sites with different coordination environments for the adsorption and dissociation of water. The Zr atom on the surface can interacted with the O atom of the water molecule via the p orbital of the O atom and the d orbital of the Zr atom. The water molecules could be dissociated via the water-solid interface reaction of the first or second layer of water molecules with the ZrO2 (111) surface. These insights into the adsorption and dissociation of water and the water-solid interface reaction on the ZrO2 surface could also provide a reference for the water-solid interface behavior of metal oxides, such as H2O-based ALD.