Pub Date : 2024-03-21DOI: 10.1016/j.mtadv.2024.100484
Shubhangi D. Shirsat, Prajkta V. Londhe, Ashwini P. Gaikwad, Muhammad Rizwan, Suvra S. Laha, Vishwajeet M. Khot, Varenyam Achal, Tanveer A. Tabish, Nanasaheb D. Thorat
Several evolving therapies depend on the delivery of therapeutic cargo into the cytoplasm. Engineered magnetic nanoparticles (MNPs) have played a pivotal role in advancing and modernizing cancer theranostics, vaccination and gene therapies. The main advantages of MNP-based delivery approaches are due to their potential to decrease the side effects by targeting specific cell types, shielding delicate therapeutics from early degradation, increasing the solubility of hard-to-deliver drugs and long-sustained and precise release of these drugs. Like other nanoparticles (NPs), MNPs enter cells by endocytosis and are frequently stuck inside endocytic vesicles, which mature into early and late endosomes and accumulate in the lysosome. Endocytosed MNPs are ultimately degraded in lysosomes or recycled towards the cell membrane. Thereby, they must escape endocytic vesicles on a priority basis. Endosomal escape is highly important for the effectiveness of nanoparticle-based treatments. This review is concerned with the use of magnetic nanoparticles (MNPs) as functional nano-objects to enhance the therapeutic effects by disrupting or rupturing the endocytic vesicles in terms of endosomal escape. The current strategies and future challenges concerning an efficient endosomal escape of MNPs are discussed in this review.
{"title":"Endosomal escape in magnetic nanostructures: Recent advances and future perspectives","authors":"Shubhangi D. Shirsat, Prajkta V. Londhe, Ashwini P. Gaikwad, Muhammad Rizwan, Suvra S. Laha, Vishwajeet M. Khot, Varenyam Achal, Tanveer A. Tabish, Nanasaheb D. Thorat","doi":"10.1016/j.mtadv.2024.100484","DOIUrl":"https://doi.org/10.1016/j.mtadv.2024.100484","url":null,"abstract":"Several evolving therapies depend on the delivery of therapeutic cargo into the cytoplasm. Engineered magnetic nanoparticles (MNPs) have played a pivotal role in advancing and modernizing cancer theranostics, vaccination and gene therapies. The main advantages of MNP-based delivery approaches are due to their potential to decrease the side effects by targeting specific cell types, shielding delicate therapeutics from early degradation, increasing the solubility of hard-to-deliver drugs and long-sustained and precise release of these drugs. Like other nanoparticles (NPs), MNPs enter cells by endocytosis and are frequently stuck inside endocytic vesicles, which mature into early and late endosomes and accumulate in the lysosome. Endocytosed MNPs are ultimately degraded in lysosomes or recycled towards the cell membrane. Thereby, they must escape endocytic vesicles on a priority basis. Endosomal escape is highly important for the effectiveness of nanoparticle-based treatments. This review is concerned with the use of magnetic nanoparticles (MNPs) as functional nano-objects to enhance the therapeutic effects by disrupting or rupturing the endocytic vesicles in terms of endosomal escape. The current strategies and future challenges concerning an efficient endosomal escape of MNPs are discussed in this review.","PeriodicalId":48495,"journal":{"name":"Materials Today Advances","volume":"66 1","pages":""},"PeriodicalIF":10.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140314089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-19DOI: 10.1016/j.mtadv.2024.100483
Isaiah A. Moses, Chengyin Wu, Wesley F. Reinhart
Materials characterization remains a labor-intensive process, with a large amount of expert time required to post-process and analyze micrographs. As a result, machine learning has become an essential tool in materials science, including for materials characterization. In this study, we perform an in-depth analysis of the prediction of crystal coverage in WSe thin film atomic force microscopy (AFM) height maps with supervised regression and segmentation models. Regression models were trained from scratch and through transfer learning from a ResNet pretrained on ImageNet and MicroNet to predict monolayer crystal coverage. Models trained from scratch outperformed those using features extracted from pretrained models, but fine-tuning yielded the best performance, with an impressive 0.99 value on a diverse set of held-out test micrographs. Notably, features extracted from MicroNet showed significantly better performance than those from ImageNet, but fine-tuning on ImageNet demonstrated the reverse. As the problem is natively a segmentation task, the segmentation models excelled in determining crystal coverage on image patches. However, when applied to full images rather than patches, the performance of segmentation models degraded considerably, while the regressors did not, suggesting that regression models may be more robust to scale and dimension changes compared to segmentation models. Our results demonstrate the efficacy of computer vision models for automating sample characterization in 2D materials while providing important practical considerations for their use in the development of chalcogenide thin films.
{"title":"Crystal growth characterization of WSe2 thin film using machine learning","authors":"Isaiah A. Moses, Chengyin Wu, Wesley F. Reinhart","doi":"10.1016/j.mtadv.2024.100483","DOIUrl":"https://doi.org/10.1016/j.mtadv.2024.100483","url":null,"abstract":"Materials characterization remains a labor-intensive process, with a large amount of expert time required to post-process and analyze micrographs. As a result, machine learning has become an essential tool in materials science, including for materials characterization. In this study, we perform an in-depth analysis of the prediction of crystal coverage in WSe thin film atomic force microscopy (AFM) height maps with supervised regression and segmentation models. Regression models were trained from scratch and through transfer learning from a ResNet pretrained on ImageNet and MicroNet to predict monolayer crystal coverage. Models trained from scratch outperformed those using features extracted from pretrained models, but fine-tuning yielded the best performance, with an impressive 0.99 value on a diverse set of held-out test micrographs. Notably, features extracted from MicroNet showed significantly better performance than those from ImageNet, but fine-tuning on ImageNet demonstrated the reverse. As the problem is natively a segmentation task, the segmentation models excelled in determining crystal coverage on image patches. However, when applied to full images rather than patches, the performance of segmentation models degraded considerably, while the regressors did not, suggesting that regression models may be more robust to scale and dimension changes compared to segmentation models. Our results demonstrate the efficacy of computer vision models for automating sample characterization in 2D materials while providing important practical considerations for their use in the development of chalcogenide thin films.","PeriodicalId":48495,"journal":{"name":"Materials Today Advances","volume":"39 1","pages":""},"PeriodicalIF":10.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140199186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the context of the growing research interest in multi-component alloys (MAs) and their exceptional performance under extreme environments, the high-temperature oxidation resistance and applications of MAs have attracted significant attention in the field of metallic materials. While the cost-effective and mechanical properties of Co-free MAs are of great importance, their oxidation resistance remains insufficiently understood. In this work, we designed multiple heterogeneous structures within a cast dual-phase CrFeNiAlTi MA by tailoring the Al and Ti ratio, which consists of body-centered-cubic (BCC) grains reinforced by multi-scale nanoprecipitates (i.e., L2, B2, and phase) and an L1-strengthened face-centered cubic (FCC) skeleton. Isothermal oxidation experiments at 800 °C, 1000 °C, and 1200 °C with varying exposure durations were conducted. The oxidation kinetics at 800 °C and 1000 °C followed a parabolic law, while both low weight increment and oxidation rate confirm remarkable oxidation resistance. At 800 °C, the oxides mainly consist of CrO and AlO, while are dominated by (TiO + CrO) and the mixed oxides of AlO, TiO and TiO above 1000 °C. Importantly, the inability to form a continuous AlO oxide scale at higher temperatures led to a deterioration in oxidation resistance. These findings offer valuable insights into underlying mechanisms contributing to oxidation resistance for Co-free MAs.
随着人们对多组分合金（MAs）及其在极端环境下的优异性能的研究兴趣日益浓厚，多组分合金的高温抗氧化性及其应用在金属材料领域引起了极大关注。虽然无 Co MAs 的成本效益和机械性能非常重要，但人们对其抗氧化性的了解仍然不够。在这项工作中，我们通过调整铝和钛的比例，在铸造的双相铬铁镍铝钛 MA 中设计了多种异质结构，其中包括由多尺度纳米沉淀物（即 L2、B2 和相）强化的体心立方（BCC）晶粒和 L1 强化的面心立方（FCC）骨架。在 800 ℃、1000 ℃ 和 1200 ℃ 温度条件下进行了不同暴露时间的等温氧化实验。800 °C 和 1000 °C 下的氧化动力学遵循抛物线规律，而低重量增量和氧化率都证实了其显著的抗氧化性。在 800 ℃ 时，氧化物主要由氧化铬和氧化铝组成，而在 1000 ℃ 以上则主要由（氧化钛 + 氧化铬）以及氧化铝、氧化钛和氧化钛的混合氧化物组成。重要的是，在较高温度下无法形成连续的氧化铝氧化物鳞片会导致抗氧化性下降。这些发现为了解无钴砷化镓抗氧化性的基本机制提供了宝贵的见解。
{"title":"High-temperature oxidation behaviors of Co-free Cr30Fe30Ni30Al5Ti5 dual-phase multi-component alloys with multi-scale nanoprecipitates","authors":"Qingwei Gao, Yingying Wang, Jianhong Gong, Changshan Zhou, Jiyao Zhang, Xiaoming Liu, Junlei Tang, Pingping Liu, Xiangyan Chen, Dong Chen, Wenquan Lv, Konda Gokuldoss Prashanth, Kaikai Song","doi":"10.1016/j.mtadv.2024.100482","DOIUrl":"https://doi.org/10.1016/j.mtadv.2024.100482","url":null,"abstract":"In the context of the growing research interest in multi-component alloys (MAs) and their exceptional performance under extreme environments, the high-temperature oxidation resistance and applications of MAs have attracted significant attention in the field of metallic materials. While the cost-effective and mechanical properties of Co-free MAs are of great importance, their oxidation resistance remains insufficiently understood. In this work, we designed multiple heterogeneous structures within a cast dual-phase CrFeNiAlTi MA by tailoring the Al and Ti ratio, which consists of body-centered-cubic (BCC) grains reinforced by multi-scale nanoprecipitates (i.e., L2, B2, and phase) and an L1-strengthened face-centered cubic (FCC) skeleton. Isothermal oxidation experiments at 800 °C, 1000 °C, and 1200 °C with varying exposure durations were conducted. The oxidation kinetics at 800 °C and 1000 °C followed a parabolic law, while both low weight increment and oxidation rate confirm remarkable oxidation resistance. At 800 °C, the oxides mainly consist of CrO and AlO, while are dominated by (TiO + CrO) and the mixed oxides of AlO, TiO and TiO above 1000 °C. Importantly, the inability to form a continuous AlO oxide scale at higher temperatures led to a deterioration in oxidation resistance. These findings offer valuable insights into underlying mechanisms contributing to oxidation resistance for Co-free MAs.","PeriodicalId":48495,"journal":{"name":"Materials Today Advances","volume":"142 1","pages":""},"PeriodicalIF":10.0,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140198869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-06DOI: 10.1016/j.mtadv.2024.100477
Tomasz Baran, Szymon Wojtyła, Marco Scavini, Francesco Carlà, Edmund Welter, Roberto Comparelli, Angela Dibenedetto, Michele Aresta
Low activity and a short lifetime are the main weaknesses of photocatalysts. The photoactivity of copper oxide, which is known as one of the most promising materials for H evolution and CO reduction, can be improved by coupling with other semiconductors. This effect is based on a mutual charge transfer. The photocathode developed in this work, based on a CuO–ZnO composite with mutual self-doping, exhibits attractive photoelectrochemical properties, in particular a high density of generated photocurrent lasting for 24 h. Under visible light irradiation, the composite produces water-splitting, while in the presence of carbon dioxide it is able to perform CO reduction to methanol with good selectivity coupled to water oxidation. The high activity of the CuO-based cathode is due to the presence of zinc oxide, which is progressively leached, causing a slow decrease of the photoactivity of the material.
活性低和寿命短是光催化剂的主要缺点。众所周知，氧化铜是最有前途的 H 演化和 CO 还原材料之一，它的光活性可以通过与其他半导体耦合而得到改善。这种效应基于电荷的相互转移。在可见光照射下，这种复合材料能产生水分裂，而在二氧化碳存在的情况下，它能以良好的选择性将一氧化碳还原成甲醇，并与水氧化作用相结合。氧化铜基阴极的高活性是由于氧化锌的存在，而氧化锌会逐渐被沥滤，导致材料的光活性缓慢下降。
{"title":"Copper–zinc oxide heterostructure photocathodes for hydrogen and methanol production","authors":"Tomasz Baran, Szymon Wojtyła, Marco Scavini, Francesco Carlà, Edmund Welter, Roberto Comparelli, Angela Dibenedetto, Michele Aresta","doi":"10.1016/j.mtadv.2024.100477","DOIUrl":"https://doi.org/10.1016/j.mtadv.2024.100477","url":null,"abstract":"Low activity and a short lifetime are the main weaknesses of photocatalysts. The photoactivity of copper oxide, which is known as one of the most promising materials for H evolution and CO reduction, can be improved by coupling with other semiconductors. This effect is based on a mutual charge transfer. The photocathode developed in this work, based on a CuO–ZnO composite with mutual self-doping, exhibits attractive photoelectrochemical properties, in particular a high density of generated photocurrent lasting for 24 h. Under visible light irradiation, the composite produces water-splitting, while in the presence of carbon dioxide it is able to perform CO reduction to methanol with good selectivity coupled to water oxidation. The high activity of the CuO-based cathode is due to the presence of zinc oxide, which is progressively leached, causing a slow decrease of the photoactivity of the material.","PeriodicalId":48495,"journal":{"name":"Materials Today Advances","volume":"27 1","pages":""},"PeriodicalIF":10.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140056933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-01DOI: 10.1016/j.mtadv.2024.100474
Seungpyo Kang, Joonchul Kim, Taehyun Park, Joonghee Won, Chul Baik, Jungim Han, Kyoungmin Min
Under thin film deposition, when used in conjunction with the semiconductor atomic layer deposition (ALD) method, the choice of precursor determines the properties and quality of the thin film. Organometallic precursors such as alkaline earth metals (Sr and Ba) and group 4 transition metals (Zr and Hf) with cyclopentadienyl and tetrakis (ethylmethylamino) ligands have recently gained attention for their stable deposition within high-temperature windows in the ALD. The design of organometallic precursors with an molecular dynamics (AIMD) simulations-based approach ensures high accuracy but comes with significant computational costs. In this study, we aim to develop a machine-learning interatomic potential (MLIP) through moment tensor potential (MTP) for fast and accurate potential development of Sr, Ba, Zr, and Hf precursors. To establish the reliable training database for MTP construction, we conducted AIMD simulations on each precursor across a range of temperature settings, resulting in a variety of atomic structures. Constructed MTPs enable efficient utilization of molecular dynamics (MD) simulations as well as calculations that achieve an accuracy that approximates density functional theory (DFT). MTP construction coupled with active learning ensures that the MTP for each precursor is reliable and that databases can be expanded. High prediction accuracy is demonstrated by a mean absolute error (MAE) of less than 0.04 eV/atom in all structures. In addition, generalization performance is confirmed for general structures (structures with the same chemical elements but different proportions) and is extended to cluster structures. The constructed MTP exhibits an MAE of less than 0.15 eV/atom, even for untrained cluster structures. These results demonstrate adequate representation and scalability as a basis for the development of MLIPs capable of atomic simulations of organometallic precursors under various thermodynamic conditions.
{"title":"Toward fast and accurate machine learning interatomic potentials for atomic layer deposition precursors","authors":"Seungpyo Kang, Joonchul Kim, Taehyun Park, Joonghee Won, Chul Baik, Jungim Han, Kyoungmin Min","doi":"10.1016/j.mtadv.2024.100474","DOIUrl":"https://doi.org/10.1016/j.mtadv.2024.100474","url":null,"abstract":"Under thin film deposition, when used in conjunction with the semiconductor atomic layer deposition (ALD) method, the choice of precursor determines the properties and quality of the thin film. Organometallic precursors such as alkaline earth metals (Sr and Ba) and group 4 transition metals (Zr and Hf) with cyclopentadienyl and tetrakis (ethylmethylamino) ligands have recently gained attention for their stable deposition within high-temperature windows in the ALD. The design of organometallic precursors with an molecular dynamics (AIMD) simulations-based approach ensures high accuracy but comes with significant computational costs. In this study, we aim to develop a machine-learning interatomic potential (MLIP) through moment tensor potential (MTP) for fast and accurate potential development of Sr, Ba, Zr, and Hf precursors. To establish the reliable training database for MTP construction, we conducted AIMD simulations on each precursor across a range of temperature settings, resulting in a variety of atomic structures. Constructed MTPs enable efficient utilization of molecular dynamics (MD) simulations as well as calculations that achieve an accuracy that approximates density functional theory (DFT). MTP construction coupled with active learning ensures that the MTP for each precursor is reliable and that databases can be expanded. High prediction accuracy is demonstrated by a mean absolute error (MAE) of less than 0.04 eV/atom in all structures. In addition, generalization performance is confirmed for general structures (structures with the same chemical elements but different proportions) and is extended to cluster structures. The constructed MTP exhibits an MAE of less than 0.15 eV/atom, even for untrained cluster structures. These results demonstrate adequate representation and scalability as a basis for the development of MLIPs capable of atomic simulations of organometallic precursors under various thermodynamic conditions.","PeriodicalId":48495,"journal":{"name":"Materials Today Advances","volume":"104 1","pages":""},"PeriodicalIF":10.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140045938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-28DOI: 10.1016/j.mtadv.2024.100475
Kyu Hyun Han, Seung-Geun Kim, Seung-Hwan Kim, Jong-Hyun Kim, Seong-Hyun Hwang, Min-Su Kim, Sung-Joo Song, Hyun-Yong Yu
Negative differential resistance (NDR) devices have recently attracted interest as multi-valued logic (MVL) circuits, owing to their folded electrical characteristics. However, with necessity of sophisticated computing systems, advanced NDR devices are required for stable low-power-consumption MVL circuits. Here, we developed van der Waals (vdW) NDR device with high peak-to-valley current ratio (PVCR) and low peak voltage (V), utilizing the passivation and doping effects of APTES layer as aminosilane coupling agent, at dielectric interface. The PVCR of NDR device reached 10 through reduced interface trap owing to the passivation effect of APTES silane group. Additionally, low V of NDR device was achieved at 0.2 V through doping effect of the APTES amine group. These PVCR and V values indicate the one of the best vdW NDR performance. Furthermore, stable logic state and low operation voltage of the ternary inverter were implemented using NDR device with high PVCR and low V. This NDR device represents a significant advancement for next-generation MVL technologies.
{"title":"Dielectric interface engineering using aminosilane coupling agent for enhancement of negative differential resistance phenomenon","authors":"Kyu Hyun Han, Seung-Geun Kim, Seung-Hwan Kim, Jong-Hyun Kim, Seong-Hyun Hwang, Min-Su Kim, Sung-Joo Song, Hyun-Yong Yu","doi":"10.1016/j.mtadv.2024.100475","DOIUrl":"https://doi.org/10.1016/j.mtadv.2024.100475","url":null,"abstract":"Negative differential resistance (NDR) devices have recently attracted interest as multi-valued logic (MVL) circuits, owing to their folded electrical characteristics. However, with necessity of sophisticated computing systems, advanced NDR devices are required for stable low-power-consumption MVL circuits. Here, we developed van der Waals (vdW) NDR device with high peak-to-valley current ratio (PVCR) and low peak voltage (V), utilizing the passivation and doping effects of APTES layer as aminosilane coupling agent, at dielectric interface. The PVCR of NDR device reached 10 through reduced interface trap owing to the passivation effect of APTES silane group. Additionally, low V of NDR device was achieved at 0.2 V through doping effect of the APTES amine group. These PVCR and V values indicate the one of the best vdW NDR performance. Furthermore, stable logic state and low operation voltage of the ternary inverter were implemented using NDR device with high PVCR and low V. This NDR device represents a significant advancement for next-generation MVL technologies.","PeriodicalId":48495,"journal":{"name":"Materials Today Advances","volume":"66 1","pages":""},"PeriodicalIF":10.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140046186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-27DOI: 10.1016/j.mtadv.2024.100478
Muhammad Latif, Yangxiaozhe Jiang, Jaehwan Kim
Nanocellulose (NC)-based piezoelectric films prepared via solution casting show low mechanical, dielectric, and piezoelectric performance due to the randomly oriented cellulose nanofibers and dispersion of piezoelectric domains. Moreover, a high electric field for piezoelectric domain alignment may also increase the brittleness of the piezoelectric films. For the first time, an additive manufacturing (AM) technology is demonstrated to fabricate high mechanical strength and flexible NC-based piezoelectric films efficiently. Different concentrations (10, 20, and 30 wt%) of lead zirconate titanate (PZT) particles are mixed in the NC suspension and additively manufactured, followed by drying at cleanroom conditions. Next, the magnetically induced electric field is introduced into the PZT-NC films coated with silver electrodes. The obtained flexible piezoelectric PZT-NC films show outstanding mechanical strength of 203.5 ± 4.8 MPa, good flexibility, high dielectric constant (87.7 at 1 kHz), low dielectric loss (0.09 at 1 kHz), and high piezoelectric constant (d = 53 pC/N). Furthermore, the 30PZT-NC piezoelectric nanogenerator showed a peak-to-peak voltage of 2.24 V and an output power density of 1.56 μW/cm. The measured mechanical, dielectric, and piezoelectric properties are superior to the previously reported NC-based piezoelectric and commercially available PVDF films. Based on the outstanding multifunctional properties of NC-based piezoelectric films, AM technology can replace traditional solution casting methods and open a wide range of applications in flexible piezoelectric materials.
{"title":"Additively manufactured flexible piezoelectric lead zirconate titanate-nanocellulose films with outstanding mechanical strength, dielectric and piezoelectric properties","authors":"Muhammad Latif, Yangxiaozhe Jiang, Jaehwan Kim","doi":"10.1016/j.mtadv.2024.100478","DOIUrl":"https://doi.org/10.1016/j.mtadv.2024.100478","url":null,"abstract":"Nanocellulose (NC)-based piezoelectric films prepared via solution casting show low mechanical, dielectric, and piezoelectric performance due to the randomly oriented cellulose nanofibers and dispersion of piezoelectric domains. Moreover, a high electric field for piezoelectric domain alignment may also increase the brittleness of the piezoelectric films. For the first time, an additive manufacturing (AM) technology is demonstrated to fabricate high mechanical strength and flexible NC-based piezoelectric films efficiently. Different concentrations (10, 20, and 30 wt%) of lead zirconate titanate (PZT) particles are mixed in the NC suspension and additively manufactured, followed by drying at cleanroom conditions. Next, the magnetically induced electric field is introduced into the PZT-NC films coated with silver electrodes. The obtained flexible piezoelectric PZT-NC films show outstanding mechanical strength of 203.5 ± 4.8 MPa, good flexibility, high dielectric constant (87.7 at 1 kHz), low dielectric loss (0.09 at 1 kHz), and high piezoelectric constant (d = 53 pC/N). Furthermore, the 30PZT-NC piezoelectric nanogenerator showed a peak-to-peak voltage of 2.24 V and an output power density of 1.56 μW/cm. The measured mechanical, dielectric, and piezoelectric properties are superior to the previously reported NC-based piezoelectric and commercially available PVDF films. Based on the outstanding multifunctional properties of NC-based piezoelectric films, AM technology can replace traditional solution casting methods and open a wide range of applications in flexible piezoelectric materials.","PeriodicalId":48495,"journal":{"name":"Materials Today Advances","volume":"19 1","pages":""},"PeriodicalIF":10.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140006950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-23DOI: 10.1016/j.mtadv.2024.100476
Shixia Wang, Yalin Wang, Tao Liu, Lu Wang, Yuxuan Huang, Yang Lu
{"title":"Irreversible pressure effect on phase transitions and bandgap narrowing of layered MoO3","authors":"Shixia Wang, Yalin Wang, Tao Liu, Lu Wang, Yuxuan Huang, Yang Lu","doi":"10.1016/j.mtadv.2024.100476","DOIUrl":"https://doi.org/10.1016/j.mtadv.2024.100476","url":null,"abstract":"","PeriodicalId":48495,"journal":{"name":"Materials Today Advances","volume":"52 1","pages":""},"PeriodicalIF":10.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-17DOI: 10.1016/j.mtadv.2024.100470
Busra Ozlu, Mohammad Boshir Ahmed, Ruth M. Muthoka, Zuwang Wen, Yechan Bea, Ji Ho Youk, Yongjin Lee, Myung Han Yoon, Bong Sup Shim
Amid the escalating demand for electronic devices, electronic waste poses a critical environmental dilemma. While current recovery techniques offer some respite, their efficacy is still debated. A burgeoning discourse emphasizes the potential of naturally derived conducting materials (i.e., melanin, indigo, and carotenoids), advocating their utility in fabricating biocompatible and biodegradable electronics. This review critically examines this emerging paradigm of green electronics. Beyond a mere overview, we interrogate such materials′ physical, chemical, and electrical performances, paying particular attention to the charge transport dynamics in substances like melanin, indigo, and carotenoids. In doing so, we shed light on potential pitfalls and broach unresolved challenges to developing biodegradable electronics. This review finding indicates that naturally derived conducting materials have great potential to develop eco-friendly electronics. We also suggest pivotal future directions for truly sustainable electronics development.
{"title":"Naturally derived electrically active materials for eco-friendly electronics","authors":"Busra Ozlu, Mohammad Boshir Ahmed, Ruth M. Muthoka, Zuwang Wen, Yechan Bea, Ji Ho Youk, Yongjin Lee, Myung Han Yoon, Bong Sup Shim","doi":"10.1016/j.mtadv.2024.100470","DOIUrl":"https://doi.org/10.1016/j.mtadv.2024.100470","url":null,"abstract":"Amid the escalating demand for electronic devices, electronic waste poses a critical environmental dilemma. While current recovery techniques offer some respite, their efficacy is still debated. A burgeoning discourse emphasizes the potential of naturally derived conducting materials (i.e., melanin, indigo, and carotenoids), advocating their utility in fabricating biocompatible and biodegradable electronics. This review critically examines this emerging paradigm of green electronics. Beyond a mere overview, we interrogate such materials′ physical, chemical, and electrical performances, paying particular attention to the charge transport dynamics in substances like melanin, indigo, and carotenoids. In doing so, we shed light on potential pitfalls and broach unresolved challenges to developing biodegradable electronics. This review finding indicates that naturally derived conducting materials have great potential to develop eco-friendly electronics. We also suggest pivotal future directions for truly sustainable electronics development.","PeriodicalId":48495,"journal":{"name":"Materials Today Advances","volume":"5 1","pages":""},"PeriodicalIF":10.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139924683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.1016/j.mtadv.2024.100468
Stephen Taller, Luke Scime, Ty Austin
Over the past half century, the transmission electron microscope enabled insight into the fundamental arrangements and structures of materials. State-of-the-art electron microscopes can acquire large image datasets across multiple imaging modalities. However, the manual annotation process for feature or defect quantification may not be feasible with the modern microscope. Convolutional neural networks emerged to characterize individual microstructural features from an image in a cost-effective, consistent manner. However, many of these neural network approaches rely on thousands to hundreds of thousands of manual annotations of each feature type across hundreds of images to train the network for adequate performance. This work focused on the development and application of a pixel-wise defect detection machine-learning dynamic segmentation convolutional neural network with associated automated acquisition and postprocessing to identify microstructural features rapidly and quantitatively from a small initial dataset incorporating multiple imaging modes. The approach was demonstrated for characterization of superalloy 718 from both single image acquisition on multiple detectors to in-situ evolution captured with a single detector on a standard desktop computer to demonstrate the low barrier to entry required for widespread adoption. Pixel-by-pixel class identification was excellent with strong identification of chemically distinct phases, structurally distinct phases, and defect structures, thus demonstrating the new paradigm of machine learning-assisted characterization.
{"title":"A new paradigm in electron microscopy: Automated microstructure analysis utilizing a dynamic segmentation convolutional neutral network","authors":"Stephen Taller, Luke Scime, Ty Austin","doi":"10.1016/j.mtadv.2024.100468","DOIUrl":"https://doi.org/10.1016/j.mtadv.2024.100468","url":null,"abstract":"Over the past half century, the transmission electron microscope enabled insight into the fundamental arrangements and structures of materials. State-of-the-art electron microscopes can acquire large image datasets across multiple imaging modalities. However, the manual annotation process for feature or defect quantification may not be feasible with the modern microscope. Convolutional neural networks emerged to characterize individual microstructural features from an image in a cost-effective, consistent manner. However, many of these neural network approaches rely on thousands to hundreds of thousands of manual annotations of each feature type across hundreds of images to train the network for adequate performance. This work focused on the development and application of a pixel-wise defect detection machine-learning dynamic segmentation convolutional neural network with associated automated acquisition and postprocessing to identify microstructural features rapidly and quantitatively from a small initial dataset incorporating multiple imaging modes. The approach was demonstrated for characterization of superalloy 718 from both single image acquisition on multiple detectors to in-situ evolution captured with a single detector on a standard desktop computer to demonstrate the low barrier to entry required for widespread adoption. Pixel-by-pixel class identification was excellent with strong identification of chemically distinct phases, structurally distinct phases, and defect structures, thus demonstrating the new paradigm of machine learning-assisted characterization.","PeriodicalId":48495,"journal":{"name":"Materials Today Advances","volume":"4 1","pages":""},"PeriodicalIF":10.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139924670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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