Microscopic chemical characterization of epoxy resin with scanning transmission electron microscopy - electron energy-loss spectroscopy.

The structural characterization of epoxy resins is essential to improve the understanding on their structure-property relationship for promising high-performance applications. Among all analytical techniques, scanning transmission electron microscopy-electron energy-loss spectroscopy (STEM-EELS) is a powerful tool for probing the chemical and structural information of various materials at a high spatial resolution. However, for sensitive materials, such as epoxy resins, the structural damage induced by electron-beam irradiation limits the spatial resolution in the STEM-EELS analysis. In this study, we demonstrated the extraction of the intrinsic features and structural characteristics of epoxy resins by STEM-EELS under electron doses below 1 e-/Å2 at room temperature. The reliability of the STEM-EELS analysis was confirmed by X-ray absorption spectroscopy and spectrum simulation as low- or non-damaged reference data. The investigation of the dependence of the epoxy resin on the electron dose and exposure time revealed the structural degradation associated with electron-beam irradiation, exploring the prospect of EELS for examining epoxy resin at low doses. Furthermore, the degradation mechanisms in the epoxy resin owing to electron-beam irradiation were revealed. These findings can promote the structural characterization of epoxy-resin-based composites and other soft materials.

Effect of inorganic material surface chemistry on structures and fracture behaviours of epoxy resin.

The mechanisms underlying the influence of the surface chemistry of inorganic materials on polymer structures and fracture behaviours near adhesive interfaces are not fully understood. This study demonstrates the first clear and direct evidence that molecular surface segregation and cross-linking of epoxy resin are driven by intermolecular forces at the inorganic surfaces alone, which can be linked directly to adhesive failure mechanisms. We prepare adhesive interfaces between epoxy resin and silicon substrates with varying surface chemistries (OH and H terminations) with a smoothness below 1 nm, which have different adhesive strengths by ~13 %. The epoxy resins within sub-nanometre distance from the surfaces with different chemistries exhibit distinct amine-to-epoxy ratios, cross-linked network structures, and adhesion energies. The OH- and H-terminated interfaces exhibit cohesive failure and interfacial delamination, respectively. The substrate surface chemistry impacts the cross-linked structures of the epoxy resins within several nanometres of the interfaces and the adsorption structures of molecules at the interfaces, which result in different fracture behaviours and adhesive strengths.

The automatic diagnosis artificial intelligence system for preoperative magnetic resonance imaging of uterine sarcoma.

OBJECTIVE: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources. METHODS: The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas. We used 15 types of MRI patient sequences to train deep neural network (DNN) models used by tumor filter and sarcoma evaluator with 8 cross-validation sets. We implemented tumor filter and sarcoma evaluator using ensemble prediction technique with 9 DNN models. Ten tumor filters and sarcoma evaluator sets were developed to evaluate fluctuation accuracy. Finally, AutoDiag-AI was used to evaluate the new validation dataset, including 8 cases of sarcomas and 24 leiomyomas. RESULTS: Tumor image filter and sarcoma evaluator accuracies were 92.68% and 90.50%, respectively. AutoDiag-AI with the original dataset accuracy was 89.32%, with 90.47% sensitivity and 88.95% specificity, whereas AutoDiag-AI with the new validation dataset accuracy was 92.44%, with 92.25% sensitivity and 92.50% specificity. CONCLUSION: Our newly established AI system automatically extracts tumor sites from MRI images and diagnoses them as uterine sarcomas without human intervention. Its accuracy is comparable to that of a radiologist. With further validation, the system could be applied for diagnosis of other diseases. Further improvement of the system's accuracy may enable its clinical application in the future.

Applications of electron microscopic observations to electrochemistry in liquid electrolytes for batteries.

Herein, we review notable points from observations of electrochemical reactions in a liquid electrolyte by liquid-phase electron microscopy. In situ microscopic observations of electrochemical reactions are urgently required, particularly to solve various battery issues. Battery performance is evaluated by various electrochemical measurements of bulk samples. However, it is necessary to understand the physical/chemical phenomena occurring in batteries to elucidate the reaction mechanisms. Thus, in situ microscopic observation is effective for understanding the reactions that occur in batteries. Herein, we focus on two methods, of the liquid phase (scanning) transmission electron microscopy and liquid phase scanning electron microscopy, and summarize the advantages and disadvantages of both methods.

Development of bioinspired damage-tolerant calcium phosphate bulk materials.

Improving the damage tolerance and reliability of ceramic artificial bone materials, such as sintered bodies of hydroxyapatite (HAp), that remain in vivo for long periods of time is of utmost importance. However, the intrinsic brittleness and low damage tolerance of ceramics make this challenging. This paper reports the synthesis of highly damage tolerant calcium phosphate-based materials with a bioinspired design for novel artificial bones. The heat treatment of isophthalate ion-containing octacalcium phosphate compacts in a nitrogen atmosphere at 1000°C for 24 h produced an HAp/ß-tricalcium phosphate/pyrolytic carbon composite with a brick-and-mortar structure (similar to that of the nacreous layer). This composite exhibited excellent damage tolerance, with no brittle fracture upon nailing, likely attributable to the specific mechanical properties derived from its unique microstructure. Its maximum bending stress, maximum bending strain, Young's modulus, and Vickers hardness were 11.7 MPa, 2.8 × 10‒2, 5.3 GPa, and 11.7 kgf/mm2, respectively. The material exhibited a lower Young's modulus and higher fracture strain than that of HAp-sintered bodies and sintered-body samples prepared from pure octacalcium phosphate compacts. Additionally, the apatite-forming ability of the obtained material was confirmed in vitro, using a simulated body fluid. The proposed bioinspired material design could enable the fabrication of highly damage tolerant artificial bones that remain in vivo for long durations of time.

Preoperative prediction of sinonasal papilloma by artificial intelligence using nasal video endoscopy: a retrospective study.

Sinonasal inverted papilloma (IP) is at risk of recurrence and malignancy, and early diagnosis using nasal endoscopy is essential. We thus developed a diagnostic system using artificial intelligence (AI) to identify nasal sinus papilloma. Endoscopic surgery videos of 53 patients undergoing endoscopic sinus surgery were edited to train and evaluate deep neural network models and then a diagnostic system was developed. The correct diagnosis rate based on visual examination by otolaryngologists was also evaluated using the same videos and compared with that of the AI diagnostic system patients. Main outcomes evaluated included the percentage of correct diagnoses compared to AI diagnosis and the correct diagnosis rate for otolaryngologists based on years of practice experience. The diagnostic system had an area under the curve of 0.874, accuracy of 0.843, false positive rate of 0.124, and false negative rate of 0.191. The average correct diagnosis rate among otolaryngologists was 69.4%, indicating that the AI was highly accurate. Evidently, although the number of cases was small, a highly accurate diagnostic system was created. Future studies with larger samples to improve the accuracy of the system and expand the range of diseases that can be detected for more clinical applications are warranted.

Direct imaging of local atomic structures in zeolite using optimum bright-field scanning transmission electron microscopy.

Zeolites are used in industries as catalysts, ion exchangers, and molecular sieves because of their unique porous atomic structures. However, direct observation of zeolitic local atomic structures via electron microscopy is difficult owing to low electron irradiation resistance. Subsequently, their fundamental structure-property relationships remain unclear. A low-electron-dose imaging technique, optimum bright-field scanning transmission electron microscopy (OBF STEM), has recently been developed. It reconstructs images with a high signal-to-noise ratio and a dose efficiency approximately two orders of magnitude higher than that of conventional methods. Here, we performed low-dose atomic-resolution OBF STEM observations of two types of zeolite, effectively visualizing all atomic sites in their frameworks. In addition, we visualized the complex local atomic structure of the twin boundaries in a faujasite (FAU)-type zeolite and Na+ ions with low occupancy in eight-membered rings in a Na-Linde Type A (LTA) zeolite. The results of this study facilitate the characterization of local atomic structures in many electron beam-sensitive materials.

Electron irradiation damage of amorphous epoxy resin at low electron doses.

The mechanisms of electron irradiation damage to epoxy resin samples were evaluated using their electron diffraction patterns and electron energy-loss spectra. Their electron diffraction patterns consisted of three indistinct halo rings. The halo ring corresponding to an intermolecular distance of ∼6.4 Šdegraded rapidly. Such molecular-scale collapse could have been caused by cross-linking between molecular chains. The degree of electron irradiation damage to the samples changed with the accelerating voltage. The tolerance dose limit of the epoxy resin estimated from the intensity of the halo ring was found to be improved at a higher accelerating voltage. Changes in low-loss electron energy-loss spectra indicated that the mass loss of the epoxy resin was remarkable in the early stage of electron irradiation.

Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases.

Uterine sarcomas have very poor prognoses and are sometimes difficult to distinguish from uterine leiomyomas on preoperative examinations. Herein, we investigated whether deep neural network (DNN) models can improve the accuracy of preoperative MRI-based diagnosis in patients with uterine sarcomas. Fifteen sequences of MRI for patients (uterine sarcoma group: n = 63; uterine leiomyoma: n = 200) were used to train the models. Six radiologists (three specialists, three practitioners) interpreted the same images for validation. The most important individual sequences for diagnosis were axial T2-weighted imaging (T2WI), sagittal T2WI, and diffusion-weighted imaging. These sequences also represented the most accurate combination (accuracy: 91.3%), achieving diagnostic ability comparable to that of specialists (accuracy: 88.3%) and superior to that of practitioners (accuracy: 80.1%). Moreover, radiologists' diagnostic accuracy improved when provided with DNN results (specialists: 89.6%; practitioners: 92.3%). Our DNN models are valuable to improve diagnostic accuracy, especially in filling the gap of clinical skills between interpreters. This method can be a universal model for the use of deep learning in the diagnostic imaging of rare tumors.

Preoperative prediction by artificial intelligence for mastoid extension in pars flaccida cholesteatoma using temporal bone high-resolution computed tomography: A retrospective study.

Cholesteatoma is a progressive middle ear disease that can only be treated surgically but with a high recurrence rate. Depending on the extent of the disease, a surgical approach, such as microsurgery with a retroarticular incision or transcanal endoscopic surgery, is performed. However, the current examination cannot sufficiently predict the progression before surgery, and changes in approach may be made during the surgery. Large amounts of data are typically required to train deep neural network models; however, the prevalence of cholesteatomas is low (1-in-25, 000). Developing analysis methods that improve the accuracy with such a small number of samples is an important issue for medical artificial intelligence (AI) research. This paper presents an AI-based system to automatically detect mastoid extensions using CT. This retrospective study included 164 patients (80 with mastoid extension and 84 without mastoid extension) who underwent surgery. This study adopted a relatively lightweight neural network model called MobileNetV2 to learn and predict the CT images of 164 patients. The training was performed with eight divided groups for cross-validation and was performed 24 times with each of the eight groups to verify accuracy fluctuations caused by randomly augmented learning. An evaluation was performed by each of the 24 single-trained models, and 24 sets of ensemble predictions with 23 models for 100% original size images and 400% zoomed images. Fifteen otolaryngologists diagnosed the images and compared the results. The average accuracy of predicting 400% zoomed images using ensemble prediction model was 81.14% (sensitivity = 84.95%, specificity = 77.33%). The average accuracy of the otolaryngologists was 73.41% (sensitivity, 83.17%; specificity, 64.13%), which was not affected by their clinical experiences. Noteworthily, despite the small number of cases, we were able to create a highly accurate AI. These findings represent an important first step in the automatic diagnosis of the cholesteatoma extension.

Reliable electrochemical setup for in situ observations with an atmospheric SEM.

A novel setup for the in situ observation of electrochemical reactions in liquids through atmospheric scanning electron microscopy (SEM) is presented. The proposed liquid-phase electrochemical SEM system consists of a working electrode (WE) on an electrochemical chip and other two electrodes inserted into a liquid electrolyte; electrochemical reactions occurring at the WE are controlled precisely with an external potentiostat/galvanostat connected to the three electrodes. Copper deposition from a CuSO4 aqueous solution was conducted onto the WE, and simultaneous acquisition of nanoscale images and reliable electrochemical data was achieved with the proposed setup.

Design and fabrication of an electrochemical chip for liquid-phase transmission electron microscopy.

Liquid-phase transmission electron microscopy (LP-TEM) can be used with an electrochemical chip (e-chip) to observe electrochemical reactions in a liquid in situ. The design of electrodes on an e-chip fabricated using microelectromechanical system technology cannot be easily changed. Here, we report a newly designed e-chip and its fabrication process. Electrodes with a desired shape were fabricated with various metals via an additional step of vacuum deposition onto our e-chip with a shadow mask. For precise control of the electrochemical reactions in LP-TEM, optimization of the electrode shape and material is critical.

Ultrapermeable 2D-channeled graphene-wrapped zeolite molecular sieving membranes for hydrogen separation.

The efficient separation of hydrogen from methane and light hydrocarbons for clean energy applications remains a technical challenge in membrane science. To address this issue, we prepared a graphene-wrapped MFI (G-MFI) molecular-sieving membrane for the ultrafast separation of hydrogen from methane at a permeability reaching 5.8 × 106 barrers at a single gas selectivity of 245 and a mixed gas selectivity of 50. Our results set an upper bound for hydrogen separation. Efficient molecular sieving comes from the subnanoscale interfacial space between graphene and zeolite crystal faces according to molecular dynamic simulations. The hierarchical pore structure of the G-MFI membrane enabled rapid permeability, indicating a promising route for the ultrafast separation of hydrogen/methane and carbon dioxide/methane in view of energy-efficient industrial gas separation.

Automated system for diagnosing endometrial cancer by adopting deep-learning technology in hysteroscopy.

Endometrial cancer is a ubiquitous gynecological disease with increasing global incidence. Therefore, despite the lack of an established screening technique to date, early diagnosis of endometrial cancer assumes critical importance. This paper presents an artificial-intelligence-based system to detect the regions affected by endometrial cancer automatically from hysteroscopic images. In this study, 177 patients (60 with normal endometrium, 21 with uterine myoma, 60 with endometrial polyp, 15 with atypical endometrial hyperplasia, and 21 with endometrial cancer) with a history of hysteroscopy were recruited. Machine-learning techniques based on three popular deep neural network models were employed, and a continuity-analysis method was developed to enhance the accuracy of cancer diagnosis. Finally, we investigated if the accuracy could be improved by combining all the trained models. The results reveal that the diagnosis accuracy was approximately 80% (78.91-80.93%) when using the standard method, and it increased to 89% (83.94-89.13%) and exceeded 90% (i.e., 90.29%) when employing the proposed continuity analysis and combining the three neural networks, respectively. The corresponding sensitivity and specificity equaled 91.66% and 89.36%, respectively. These findings demonstrate the proposed method to be sufficient to facilitate timely diagnosis of endometrial cancer in the near future.

The application of machine learning for predicting recurrence in patients with early-stage endometrial cancer: a pilot study.

OBJECTIVE: Most women with early stage endometrial cancer have a favorable prognosis. However, there is a subset of patients who develop recurrence. In addition to the pathological stage, clinical and therapeutic factors affect the probability of recurrence. Machine learning is a subtype of artificial intelligence that is considered effective for predictive tasks. We tried to predict recurrence in early stage endometrial cancer using machine learning methods based on clinical data. METHODS: We enrolled 75 patients with early stage endometrial cancer (International Federation of Gynecology and Obstetrics stage I or II) who had received surgical treatment at our institute. A total of 5 machine learning classifiers were used, including support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression (LR), and boosted tree, to predict the recurrence based on 16 parameters (age, body mass index, gravity/parity, hypertension/diabetic, stage, histological type, grade, surgical content and adjuvant chemotherapy). We analyzed the classification accuracy and the area under the curve (AUC). RESULTS: The highest accuracy was 0.82 for SVM, followed by 0.77 for RF, 0.74 for LR, 0.66 for DT, and 0.66 for boosted trees. The highest AUC was 0.53 for LR, followed by 0.52 for boosted trees, 0.48 for DT, and 0.47 for RF. Therefore, the best predictive model for this analysis was LR. CONCLUSION: The performance of the machine learning classifiers was not optimal owing to the small size of the dataset. The use of a machine learning model made it possible to predict recurrence in early stage endometrial cancer.

Ultrafast synthesis of high-silica erionite zeolites with improved hydrothermal stability.

High-silica erionite (ERI) zeolites are conventionally synthesised via a so-called charge density mismatch (CDM) approach, and a typical synthesis takes several days to complete. We herein demonstrate an ultrafast route to synthesise high-silica erionite zeolites in as short as 2 h at 210 °C. The fast-synthesised ERI has been proved to show higher hydrothermal stability compared with the conventionally synthesised product.

Catalytic behavior of noble metal nanoparticles for the hydrogenation and oxidation of multiwalled carbon nanotubes.

The catalytic behavior of various noble metal nanoparticles (NPs) supported directly on multiwalled carbon nanotubes (MWCNTs) was observed using environmental transmission electron microscopy (E-TEM). Gasification of the MWCNTs via catalytic hydrogenation or oxidation progressed at ∼450°C in conjunction with certain noble metal NP catalysts at the interface between MWCNTs and the NPs. During such catalytic reactions, the NPs were observed to move rapidly over the MWCNT surfaces. The mobility and wettability of the NPs varied depending on the particular metal NPs employed and the ambient atmosphere. While rhodium NPs exhibited high wettability under both hydrogen and oxygen atmospheres, the wettability of platinum, palladium and iridium NPs on MWCNTs varied with the atmosphere. The metal NPs seemed to have high degrees of crystallinity while their morphologies fluctuated throughout the catalytic reactions.

Critical conditions for atomic resolution imaging of molecular crystals by aberration-corrected HRTEM.

Atomically resolved imaging of organic molecules consisting of thin crystals by aberration-corrected (AC) HRTEM was studied by experimental observations and image simulations. An atomically resolved image of the hexadecachlorophthalocyanatocopper (CuPcCl16) molecule was obtained under low-dose conditions. The conditions for imaging organic frameworks were found to be more restricted than those for heavier elements such as copper and chlorine. For the characterization of the benzene rings within CuPcCl16 molecules, the specimen thickness had to be less than ~5 nm. The effects of the defocus conditions were examined by changing the image according to the location of the inclined specimen. The optimal defocus range for atomic resolution imaging of organic molecules was limited to a narrow region around the Scherzer defocus. Compared with scanning transmission microscopy, AC-HRTEM is more suitable for low-dose imaging, but the optimum conditions were severely restricted.

Catalytic oxidation of carbon nanotubes with noble metal nanoparticles.

Catalytic oxidation of multi-walled carbon nanotubes (MWNCTs) with some noble metal nanoparticles was observed by environmental transmission electron microscopy (E-TEM). Amoeba-like movement of the nanoparticles was observed even at a temperature of ∼400°C, which is much lower than the melting points of any of the metals. In particular, rhodium particles reacted intensely with MWCNTs, and assumed a droplet-like shape. On the other hand, gold particles caused very little erosion of the MWCNTs under the conditions of this study.

Accumulation of supramolecular nanoparticles self-assembled from a bola-shaped cytidylic acid-appended fluorescein dye in cell nuclei.

We examined the cellular uptake of the nanoparticles self-assembled from a bola-shaped cytidylic acid-appended fluorescein derivative (C-FLU-C). The accumulation of fluorescence in the Caco-2 cell nucleus was observed mainly after the plateau phase of cell growth, indicating that C-FLU-C permeated the nuclear envelope without nuclear-localizing tags.