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Triple-negative breast cancer (TNBC) is one of the most malignant subtypes of breast cancer with an unsatisfied prognosis. Effective biomarkers could predict the risk and improve patients' survival. Whether LINC00466 possesses the potential to serve as a biomarker of the progression and prognosis of TNBC was evaluated.A total of 122 TNBC patients were included in this study and provided paired clinical tissues. The expression of LINC00466 in TNBC was evaluated by RT-qPCR and evaluated the clinical significance with a series of statistical analyses. The biological effects and the mechanism were investigated in TNBC cells with CCK8, Transwell, and luciferase reporter assays.LINC00466 was significantly upregulated in TNBC and showed close association with the clinical features of patients, which indicates the development, and severity of patients. LINC00466 functioned as a prognostic biomarker predicting the survival of patients and a tumor promoter improving the proliferation, migration, and invasion of TNBC cells through sponging miR-539-5p.LINC00466 promotes the progression of TNBC via regulating miR-539-5p. The inhibition of LINC00466 might be a novel therapeutic strategy for TNBC.

At present, the progression mechanism of knee osteoarthritis (KOA) has not been fully elucidated, and there is a clinical need for late KOA-specific diagnostic markers to provide reference for preventive treatment. This study aimed to analyze the sequencing results of early- and late-stage KOA synovial tissue based on the key genes of late-stage KOA in combination with a machine learning algorithm.The whole transcriptome sequencing results of synovial tissue from KOA patients (GSE176223 and GSE32317) were downloaded from the gene expression omnibus (GEO) database. Thirty-nine early KOA synovial tissue samples and 31 late KOA synovial tissue samples were included in this study. The diagnostic criteria and baseline data balance of early and late KOA were referred to the data source literature, and the two groups of data had good baseline data balance. R software (V3.5.1) and R packages were used for screening and enrichment analysis of differentially expressed genes (DEGs). The key genes were screened by weighted correlation network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression analysis. A receiver operating characteristic curve (ROC) curve was used to evaluate the diagnostic efficacy of key genes for advanced KOA.A total of 211 DEGs related to knee arthritis were screened out. Compared with synovial tissue of early knee arthritis, 111 genes were upregulated and 100 genes were downregulated in the synovial tissue of late knee arthritis. Sixty-six key genes were screened out through WGCNA and 34 key genes were screened out in the LASSO analysis. The genes obtained by the two algorithms combined with three overlapping genes, namely interleukin- 6 (IL-6), C-X-C chemokine ligand 12 (CXCL12), and macrophage migration inhibitor factor (MIF). The areas under the ROC curves of

SARS-CoV-2 neutralizing antibodies have excellent application prospects in the prevention and treatment of COVID-19. This study established a competitive colloidal gold immunochromatography assay (GICA) to detect neutralizing antibodies against the receptor-binding domain (RBD) of SARS-CoV-2 in postvaccination serum. The sensitivity, stability, and specificity of GICA were evaluated using neutralizing antibody solution reference material and positive serum. The consistency and correlation between GICA, pseudovirus neutralization (PN) assay, and ELISA were compared. Consistency analysis of serum neutralizing antibody and specific IgG antibody titers was conducted, and changes in neutralizing antibodies and specific IgG antibodies in serum after inoculation with the homologous booster inactivated vaccine and recombinant vaccine were noted. The sensitivity of the reagent was 20.66 IU/L, and the specificity was 100%. There was a strong consistency and correlation between GICA and PN (κ = 0.886

Neuromorphic photonics has recently emerged as a promising hardware accelerator, with significant potential speed and energy advantages over digital electronics for machine learning algorithms, such as neural networks of various types. Integrated photonic networks are particularly powerful in performing analog computing of matrix-vector multiplication (MVM) as they afford unparalleled speed and bandwidth density for data transmission. Incorporating nonvolatile phase-change materials in integrated photonic devices enables indispensable programming and in-memory computing capabilities for on-chip optical computing. Here, we demonstrate a multimode photonic computing core consisting of an array of programable mode converters based on on-waveguide metasurfaces made of phase-change materials. The programmable converters utilize the refractive index change of the phase-change material Ge2Sb2Te5 during phase transition to control the waveguide spatial modes with a very high precision of up to 64 levels in modal contrast. This contrast is used to represent the matrix elements, with 6-bit resolution and both positive and negative values, to perform MVM computation in neural network algorithms. We demonstrate a prototypical optical convolutional neural network that can perform image processing and recognition tasks with high accuracy. With a broad operation bandwidth and a compact device footprint, the demonstrated multimode photonic core is promising toward large-scale photonic neural networks with ultrahigh computation throughputs., Integrated optical computing requires programmable photonic and nonlinear elements. The authors demonstrate a phase-change metasurface mode converter, which can be programmed to control the waveguide mode contrast, and build an optical convolutional neural network to perform image processing tasks.

Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material., Machine learning driven research holds big promise towards accelerating materials’ discovery. Here the authors demonstrate CAMEO, which integrates active learning Bayesian optimization with practical experiments execution, for the discovery of new phase- change materials using X-ray diffraction experiments.

In this study, we fabricate high-quality epitaxial ${\mathrm{Li}}_{1\text{--}x}\mathrm{Co}{\mathrm{O}}_{2}$ (LCO) thin films on $\mathrm{SrTi}{\mathrm{O}}_{3}$ substrates with different orientations and systematically investigate the change in the crystal orientation of thin-film grains by tuning the oxygen deposition pressure. Further tensile strain study reveals the depth dependence in these epitaxial LCO thin films. Further, synaptic transistors are fabricated based on these high-quality epitaxial LCO films, and long-duration nonvolatile potentiation and depression states are established. By reducing the thickness of the LCO channel, the signal-to-noise ratio of the nonvolatile switching is substantially improved. Changes in the potentiation and depression states are found to significantly depend on the lattice orientation of the LCO channel, suggesting that an anisotropic Li-ion diffusion rate in highly crystallized LCO films plays an important role in the device performance.

Integrated optoelectronics is emerging as a promising platform of neural network accelerator, which affords efficient in-memory computing and high bandwidth interconnectivity. The inherent optoelectronic noises, however, make the photonic systems error-prone in practice. It is thus imperative to devise strategies to mitigate and, if possible, harness noises in photonic computing systems. Here, we demonstrate a photonic generative network as a part of a generative adversarial network (GAN). This network is implemented with a photonic core consisting of an array of programable phase-change memory cells to perform four-element vector-vector dot multiplication. The GAN can generate a handwritten number (“7”) in experiments and full 10 digits in simulation. We realize an optical random number generator, apply noise-aware training by injecting additional noise, and demonstrate the network’s resilience to hardware nonidealities. Our results suggest the resilience and potential of more complex photonic generative networks based on large-scale, realistic photonic hardware., Description, A photonic generative adversarial network that harnesses optoelectronic noises to generate handwritten numbers is demonstrated.

Integrated programmable optoelectronics is emerging as a promising platform of neural network accelerator, which affords efficient in-memory computing and high bandwidth interconnectivity. The analog nature of optical computing and the inherent optoelectronic noises, however, make the systems error-prone in practical implementations such as classification by discriminative neural networks. It is thus imperative to devise strategies to mitigate and, if possible, harness optical and electrical noises in optoelectronic computing systems. Here, we demonstrate a prototypical hybrid photonic generative adversarial network (GAN) that generates handwritten numbers using an optoelectronic core consisting of an array of programable phase-change optical memory cells. We harness optoelectronic noises in the hybrid photonic GAN by realizing an optoelectronic random number generator derived from the amplified spontaneous emission noise, applying noise-aware training by injecting additional noise to the network, and implementing the trained network with resilience to hardware non-idealities. Surprisingly, the hybrid photonic GAN with hardware noises and inaccuracies can generate images of even higher quality than the noiseless software baseline. Our results suggest the resilience and potential of more complex hybrid generative networks based on large-scale, non-ideal optoelectronic hardware. The demonstrated GAN architecture and the proposed noise-aware training approach are generic and thus applicable to various types of optoelectronic neuromorphic computing hardware.

Modern machine learning and autonomous experimentation schemes in materials science rely on accurate analysis of the data ingested by these models. Unfortunately, accurate analysis of the underlying data can be difficult, even for domain experts, complicating the training of the models intended to drive experiments. This is especially true when the goal is to identify the presence of weak signatures in diffraction or spectroscopic datasets. In this work, we examine a set of as-obtained diffraction data that track the phase transition from monoclinic to tetragonal in a Nb-doped VO2 film as a function of temperature and dopant concentration. We then task a set of domain experts and a set of machine learning experts with identifying which phase is present in each diffraction pattern manually and algorithmically, respectively; in both cases, the labels can vary dramatically, especially at the phase boundaries. We use the mode of the labels and the Shannon entropy as a method to capture, preserve and propagate consensus labels and their variance. Further we use the expert labels as a benchmark and demonstrate the use of Shannon entropy weighted scoring to test the performance of machine learning generated labels. Finally, we propose a material data challenge centered around generating improved labeling algorithms. This real-world dataset curated with expert labels can act as test bed for new algorithms. The raw data, annotations and code used in this study are all available online at data.gov and the interested reader is encouraged to replicate and improve the existing models

We report an enhanced magnetoelastic contribution to the Gilbert damping in highly magnetostrictive ${\mathrm{Fe}}_{0.7}{\mathrm{Ga}}_{0.3}$ thin films. This effect is mitigated for perpendicular-to-plane fields, leading to a large anisotropy of the Gilbert damping in all of the films (up to a factor of 10 at room temperature). These claims are supported by broadband measurements of the ferromagnetic resonance linewidths over a range of temperatures (5 to 400 K), which serve to elucidate the effect of both the magnetostriction and phonon relaxation on the magnetoelastic Gilbert damping.

Efficient integrated photonic switches play a critical role in both interchip optical interconnects and data center networks that need to be dynamically reconfigured. Here, we demonstrate a 1 × 2 s...

Marked evolution of properties with minute changes in the doping level is a hallmark of the complex chemistry that governs copper oxide superconductivity as manifested in the celebrated superconducting domes and quantum criticality taking place at precise compositions1-4. The strange-metal state, in which the resistivity varies linearly with temperature, has emerged as a central feature in the normal state of copper oxide superconductors5-9. The ubiquity of this behaviour signals an intimate link between the scattering mechanism and superconductivity10-12. However, a clear quantitative picture of the correlation has been lacking. Here we report the observation of precise quantitative scaling laws among the superconducting transition temperature (Tc), the linear-in-T scattering coefficient (A1) and the doping level (x) in electron-doped copper oxide La2-xCexCuO4 (LCCO). High-resolution characterization of epitaxial composition-spread films, which encompass the entire overdoped range of LCCO, has enabled us to systematically map its structural and transport properties with unprecedented accuracy and with increments of Δx = 0.0015. We have uncovered the relations Tc ~ (xc - x)0.5 ~ (A1□)0.5, where xc is the critical doping in which superconductivity disappears and A1□ is the coefficient of the linear resistivity per CuO2 plane. The striking similarity of the Tc versus A1□ relation among copper oxides, iron-based and organic superconductors may be an indication of a common mechanism of the strange-metal behaviour and unconventional superconductivity in these systems.

Antiferromagnetic (AF) spin fluctuations are commonly believed to play a key role in electron pairing of cuprate superconductors. In electron-doped cuprates, a paradox still exists about the interplay among different electronic states in quantum perturbations, especially between superconducting and magnetic states. Here, we report a systematic transport study of cation-optimized ${\mathrm{La}}_{2\ensuremath{-}x}{\mathrm{Ce}}_{x}{\mathrm{CuO}}_{4\ifmmode\pm\else\textpm\fi{}\ensuremath{\delta}}$ $(x=0.10)$ thin films in high magnetic fields. We find an AF quantum phase transition near 60 T, where the Hall number jumps from ${n}_{\mathrm{H}}=\ensuremath{-}x$ to ${n}_{\mathrm{H}}=1\ensuremath{-}x$, resembling the change in ${n}_{\mathrm{H}}$ at the AF boundary $({x}_{\mathrm{AF}}=0.14)$ tuned by Ce doping. In the AF region a spin-dependent state manifesting anomalous positive magnetoresistance is observed, which is closely related to superconductivity. Once the AF state is suppressed by magnetic field, a polarized ferromagnetic state is predicted, reminiscent of the recently reported ferromagnetic state at the quantum end point of the superconducting dome by Ce doping. The magnetic field that drives phase transitions in a manner similar to but distinct from doping thereby provides a unique perspective to understand the quantum criticality of electron-doped cuprates.

We develop deep-learning methods for rapid analysis of spectroscopic ellipsometry data. Our approach speeds analysis by thousand-fold compared to traditional methods. We demonstrate the usefulness of our approach for a high-throughput study of phase-change alloys.

We demonstrate photonic generative adversarial networks (GANs) based on a phase-change metasurface mode converter (PMMC) array and perform the handwritten-like number generation task.

Background Mastoscopic surgery is proven to have lower incidence of postoperative complications and better postoperative recovery than traditional breast cancer surgery. This study aimed to examine the feasibility of mastoscopic modified radical mastectomy (MRM) with skin nipple-areola preservation under air cavity-free suspension hook and stage I silicone prosthesis implantation (SMALND) compared with routine MRM. Methods This was a retrospective study of patients who underwent MRM for breast cancer at the Shengjing Hospital Affiliated to China Medical University between January 1, 2019, and June 30, 2019. Surgical outcomes, complications, satisfaction, and quality of life (Functional Assessment of Cancer Therapy-Breast [FACT-B] [Chinese version]) were compared between the two groups. Results A total of 87 patients were enrolled, with 30 underwent SMALND and 57 underwent routine MRM. The intraoperative blood loss in the SMALND group was lower than in the control group (165.3±44.1 vs. 201.4±52.7 ml, P=0.001), the operation time was longer (220.5±23.9 vs. 155.6±9.2 min, PPPPPP=0.041). Compared with the controls, the occurrence rates of nipple and flap necrosis, upper limb edema, and paraesthesia in the SMALND group were lower within 6 months (all P Conclusions Compared with traditional MRM, SMALND had better surgical outcomes, higher satisfaction, higher quality of life, and lower complication rates.

We demonstrate a programmable TE0-to-TE1 mode converter utilizing phase change material Ge 2 Sb 2 Te 5 based phase gradient metasurface integrated on a waveguide. This compact mode converter features high energy efficiency, high precision and broadband performance and is very promising toward a large-scale photonic processor for neural networks.

We demonstrate a programmable TE0-to-TE1 mode converter utilizing phase change material Ge2Sb2Te5 (GST) based phase gradient metasurface integrated on a silicon nitride waveguide. This compact mode converter features high energy efficiency and broadband performance.

Present work demonstrates the formation of spin-orbital polarons in electron doped copper oxides, that arise due to doping-induced polarisation of the oxygen orbitals in the CuO$_2$ planes. The concept of such polarons is fundamentally different from previous interpretations. The novel aspect of spin-orbit polarons is best described by electrons becoming self-trapped in one-dimensional channels created by polarisation of the oxygen orbitals. The one-dimensional channels form elongated filaments with two possible orientations, along the diagonals of the elementary CuO$_2$ square plaquette. As the density of doped electrons increases multiple filaments are formed. These may condense into a single percollating filamentary phase. Alternatively, the filaments may cross perpendicularly to create an interconnected conducting quasi-one-dimensional web. At low electron doping the antiferromagnetic (AFM) state and the polaron web coexist. As the doping is increased the web of filaments modifies and transforms the AFM correlations leading to a series of quantum phase transitions - which affect the normal and superconducting state properties., Comment: Please cite this article as: A. Kusmartseva, H. Yu, K. Jin, F.V. Kusmartsev, Spin-orbital polarons in electron doped copper oxides, Journal of Magnetism and Magnetic Materials (2017), doi: https://doi.org/10.1016/j.jmmm. 2017.11.021

The evolution from superconducting LiTi2O4-δ to insulating Li4Ti5O12 thin films has been studied by precisely tuning the oxygen pressure in the sample fabrication process. In superconducting LiTi2O4-δ films, with the increase of oxygen pressure, the oxygen vacancies are filled gradually and the c-axis lattice constant decreases. When the oxygen pressure increases to a certain critical value, the c-axis lattice constant becomes stable, which implies that the sample has been completely converted to Li4Ti5O12 phase. The two processes can be manifested by the angular bright-field images of the scanning transmission electron microscopy techniques. The transition temperature (T ch ) of magnetoresistance from the positive to the negative shows a nonmonotonic behavior, i.e. first decrease and then increase, with the increase of oxygen pressure. We suggest that the decrease Tch can be attributed to the suppressing of orbital-related state, and the inhomogeneous phase separated regions contribute positive MR and thereby lead to the reverse relation between Tch and oxygen pressure.

High‐throughput experimental approaches to rapidly develop new materials require high‐throughput data analysis methods to match. Spectroscopic ellipsometry is a powerful method of optical properties characterization, but for unknown materials and/or layer structures the data analysis using traditional methods of nonlinear regression is too slow for autonomous, closed‐loop, high‐throughput experimentation. Herein, three methods (termed spectral, piecewise, and pointwise) of spectroscopic ellipsometry data analysis based on deep learning are introduced and studied. After initial training, the incremental time for inferring optical properties can be a thousand times faster than traditional methods. Results for multilayer sample structures with optically isotropic materials are presented, appropriate for high‐throughput studies of thin films of phase‐change materials such as GeSbTe (GST) alloys. Results for studies on highly birefringent layered materials are also presented, exemplified by the transition metal dichalcogenide MoS2. How the materials under test and the experimental objectives may guide the choice of analysis methods are discussed. The utility of our approach is demonstrated by analyzing data measured on a composition spread of GeSbTe phase‐change alloys containing 177 distinct compositions, and identifying the composition with optimal phase‐change figure of merit in only 1.4 s of analysis time.

The highly c-axis oriented La 1.9 Ce 0.1 CuO 4 (LCCO) film was prepared on SrTiO 3 (STO) substrate by pulsed-laser deposition (PLD). The identification and characterization of the LCCO film was determined by X-ray diffraction (XRD), atomic force microscopy (AFM), scanning electron microscope (SEM) and X-ray photoelectron spectroscopy (XPS). Spectroscopic ellipsometry (SE) measurement was performed on the LCCO film in 0.5–4.13 eV range. The complex pseudodielectric function of the LCCO film was reported by Drude and Lorentz oscillators in this work. Critical point analysis on third-derivative spectra of the Lorentz contributions was accomplished to obtain the interband transition energies. The results indicate that the interband transitions are mainly related to the copper-oxygen planes. The metallic behavior of the pseudodielectric function, which is a common feature in all known electron-doped superconductors, can be obviously observed in the LCCO film.

Superconductivity research is like running a marathon. Three decades after the discovery of high-Tc cuprates, there have been mass data generated from transport measurements, which bring fruitful information. In this review, we give a brief summary of the intriguing phenomena reported in electron-doped cuprates from the aspect of electrical transport as well as the complementary thermal transport. We attempt to sort out common features of the electron-doped family, e.g. the strange metal, negative magnetoresistance, multiple sign reversals of Hall in mixed state, abnormal Nernst signal, complex quantum criticality. Most of them have been challenging the existing theories, nevertheless, a unified diagram certainly helps to approach the nature of electron-doped cuprates., 35 figures; a brief summary of transport properties; the common features seen in the last figure

We report $ab$-plane Hall-effect and magnetoresistivity measurements on $\mathrm{L}{\mathrm{a}}_{2\ensuremath{-}x}\mathrm{C}{\mathrm{e}}_{x}\mathrm{Cu}{\mathrm{O}}_{4}$ thin films as a function of doping for magnetic fields up to 14 T and temperatures down to 1.8 K. A dramatic change in the low-temperature (1.8 K) normal-state Hall coefficient is found near a doping $\mathrm{Ce}=0.14$. This, along with a nonlinear Hall resistance as a function of magnetic field, suggests that the Fermi surface reconstructs at a critical doping of $\mathrm{Ce}=0.14$. A competing antiferromagnetic phase is the likely cause of this Fermi surface reconstruction. Low-temperature linear-in-$T$ resistivity is found at $\mathrm{Ce}=0.14$, but anomalously, also at higher doping. We compare our data with similar behavior found in hole-doped cuprates at a doping where the pseudogap ends.

The evolution from superconducting LiTi2O4-δ to insulating Li4Ti5O12 thin films has been studied by precisely tuning the oxygen pressure in the sample fabrication process. In superconducting LiTi2O4-δ films, with the increase of oxygen pressure, the oxygen vacancies are filled gradually and the c-axis lattice constant decreases. When the oxygen pressure increases to a certain critical value, the c-axis lattice constant becomes stable, which implies that the sample has been completely converted to Li4Ti5O12 phase. The two processes can be manifested by the angular bright-field images of the scanning transmission electron microscopy techniques. The transition temperature (Tch) of magnetoresistance from the positive to the negative shows a nonmonotonic behavior, i.e. first decrease and then increase, with the increase of oxygen pressure. We suggest that the decrease Tch can be attributed to the suppressing of orbital-related state, and the inhomogeneous phase separated regions contribute positive MR and thereby lead to the reverse relation between Tch and oxygen pressure.

The techniques of growing films with different parameters in single process make it possible to build up a sample library promptly. In this work, with a precisely controlled moving mask, we synthetized superconducting La2-xCexCuO4+/-{\delta} combinatorial films on one SrTiO3 substrate with the doping levels from x = 0.1 to 0.19. The monotonicity in doping along the designed direction is verified by micro-region x-ray diffraction and electric transport measurements. More importantly, by means of numerical simulation, the real change of doping levels is in accordance with a linear gradient variation of doping levels in the La2-xCexCuO4+/-{\delta} combinatorial films. Our results indicate that it is promising to accurately investigate materials with critical composition by combinatorial film technique., Comment: 4 pages, 4 figures, cover story

We report systematic in-plane magnetoresistance measurements on the electron-doped cuprate La2−x Ce x CuO4±δ thin films as a function of Ce doping and oxygen content in the magnetic field up to 14 T. A crossover from negative to positive magnetoresistance occurs between the doping level x = 0.07 and 0.08. Above x = 0.08, the positive magnetoresistance effect appears, and is almost indiscernible at x = 0.15. By tuning the oxygen content, the as-grown samples show negative magnetoresistance effect, whereas the optimally annealed ones display positive magnetoresistance effect at the doping level x = 0.15. Intriguingly, a linear-field dependence of in-plane magnetoresistance is observed at the underdoping level x = 0.06, the optimal doping level x = 0.1 and slightly overdoping level x = 0.11. These anomalies of in-plane magnetoresistance may be related to the intrinsic inhomogeneity in the cuprates, which is discussed in the framework of network model.

International audience; The electromigration process has the potential capability to move atoms one by onewhen properly controlled. It is therefore an appealing tool to tune the cross sectionof monoatomic compounds with ultimate resolution or, in the case of polyatomiccompounds, to change the stoichiometry with the same atomic precision. Asdemonstrated here, a combination of electromigration and anti-electromigration canbe used to reversibly displace atoms with a high degree of control. This enables afine adjustment of the superconducting properties of Al weak links, whereas in Nbthe diffusion of atoms leads to a more irreversible process. In a superconductor witha complex unit cell (La2−xCexCuO4), the electromigration process acts selectively onthe oxygen atoms with no apparent modification of the structure. This allows to adjustthe doping of this compound and switch from a superconducting to an insulatingstate in a nearly reversible fashion. In addition, the conditions needed to replacefeedback controlled electromigration by a simpler technique of electropulsing arediscussed. These findings have a direct practical application as a method to explorethe dependence of the characteristic parameters on the exact oxygen content and pavethe way for a reversible control of local properties of nanowires.

The electromigration process has the potential capability to move atoms one by one when properly controlled. It is therefore an appealing tool to tune the cross section of monoatomic compounds with ultimate resolution or, in the case of polyatomic compounds, to change the stoichiometry with the same atomic precision. As demonstrated here, a combination of electromigration and anti-electromigration can be used to reversibly displace atoms with a high degree of control. This enables a fine adjustment of the superconducting properties of Al weak links, whereas in Nb the diffusion of atoms leads to a more irreversible process. In a superconductor with a complex unit cell (La

The influence of annealing time on microstructure, morphological, electrical and mechanical properties of La1.9Ce0.1CuO4 (LCCO) superconducting films has been studied. Single-phase LCCO thin films were prepared by pulsed-laser deposition on SrTiO3 substrate with varying annealing time. Microstructure and morphology of the LCCO films were investigated using x-ray diffraction and atomic force microscopy. Temperature dependence of electrical resistance of the LCCO films was determined by four-point method. Hardness and elastic modulus of the LCCO films were characterized by depth sensing nanoindentation technique. Results show that annealing time mainly affects the hardness and elastic modulus of the LCCO films through Cu-rich particles and the crystallite size. However, the influence of Cu-rich particles is much less remarkable than that of the crystallite size.

Superconductivity originates from the coupling between charge carriers and bosonic excitations of either phononic or electronic origin. Identifying the most relevant pairing glue is a key step towards a clear understanding of the unconventional superconductivity. Here, by applying the ultrafast optical spectroscopy on the electron-doped cuprates La$_{1.9}$Ce$_{0.1}$CuO$_{4\pm\delta}$, we discern a bosonic mode of electronic origin that has the strongest coupling with the charge carriers near $T_c$. We argue that this mode is associated with the two-dimensional antiferromagnetic spin fluctuations, and can fully account for the superconducting pairing. Our work may help to establish a quantitative relation between bosonic excitations and superconducting pairing in electron-doped cuprates, and pave the way for systematic exploration of superconductivity and other collective phenomena in all correlated materials.

Two thin La2-xCexCuO4 (x = 0.1) films were deposited on [001]-oriented SrTiO3 substrates by pulsed-laser deposition. The as-prepared LCCO films were well studied by X-ray diffraction, atomic force microscopy, transmission electron microscopy and spectroscopic ellipsometry. Spectroscopic ellipsometry provides a nondestructive, fast, and accurate method to explore the optical properties of the superconductive materials. The thickness and the optical dispersion model of the LCCO films in the visible range are presented for the first time. The results show that minor differences in the annealing progress will cause a relatively large change in the optical properties of the LCCO films.