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This paper reports on the development of a hybrid atmospheric pressure atomic force microscopy/mass spectrometry imaging system utilizing nanothermal analysis probes for thermal desorption surface sampling with subsequent atmospheric pressure chemical ionization and mass analysis. The basic instrumental setup and the general operation of the system were discussed, and optimized performance metrics were presented. The ability to correlate topographic images of a surface with atomic force microscopy and a mass spectral chemical image of the same surface, utilizing the same probe without moving the sample from the system, was demonstrated. Co-registered mass spectral chemical images and atomic force microscopy topographical images were obtained from inked patterns on paper as well as from a living bacterial colony on an agar gel. Spatial resolution of the topography images based on pixel size (0.2 μm × 0.8 μm) was better than the resolution of the mass spectral images (2.5 μm × 2.0 μm), which were limited by current mass spectral data acquisition rate and system detection levels. [ABSTRACT FROM AUTHOR]

This letter describes the coupling of ambient pressure transmission geometry laser ablation with a liquid phase sample collection method for surface sampling and ionization with subsequent mass spectral analysis. A commercially available autosampler was adapted to produce a liquid droplet at the end of the syringe injection needle while in close proximity to the surface to collect the sample plume produced by laser ablation. The sample collection was followed by either flow injection or a high-performance liquid chromatography (HPLC) separation of the extracted components and detection with electrospray ionization mass spectrometry (ESI-MS). To illustrate the analytical utility of this coupling, thin films of a commercial ink sample containing rhodamine 6G and of mixed isobaric rhodamine B and 6G dyes on glass microscope slides were analyzed. The flow injection and HPLC-ESI-MS analysis revealed successful laser ablation, capture, and with HPLC, the separation of the two compounds. The ablated circular area was about 70 μm in diameter for these experiments. The spatial sampling resolution afforded by the laser ablation, as well as the ability to use sample processing methods like HPLC between the sample collection and ionization steps, makes this combined surface sampling/ionization technique a highly versatile analytical tool. [ABSTRACT FROM AUTHOR]

Proximal probe thermal desorption/secondary ionization mass spectrometiy was studied and applied to molecular surface sampling and chemical imaging using printed patterns on photopaper as test substrates. With the use of a circular cross section proximal probe with a tip diameter of 50 μm and fixed temperature (350 °C), the influence of probe-to-surface distance, lane scan spacing, and surface scan speed on signal quality and spatial resolution were studied and optimized. As a compromise between signal amplitude, signal reproducibility, and data acquisition time, a surface scan speed of 100 μm/s, probe-to-paper surface distance of 5 μm, and lane spacing of 10μm were used for imaging. Under those conditions the proximal probe thermal desorption/secondary ionization mass spectrometry method was able to achieve a spatial resolution of about 50 μm as determined by the ability to distinguish surface patterns of known dimensions that were printed on the paper substrate, It is expected that spatial resolution and chemical image quality could be further improved by using probes of smaller cross section size and by incorporating a means to maintain a fixed optimal probe-to-surface distance real time, continuously adapting to the changing topography of the surface during a lane scan. [ABSTRACT FROM AUTHOR]

Despite over a decade of research on metal halide perovskites (MHPs) in the context of photovoltaic applications, understanding the nature of electronic and ionic processes associated with current-voltage (I-V) hysteretic behavior has been limited. Here, we explore the hysteretic behavior in (FAPbI 3) 0.85 (MAPbBr 3) 0.15 perovskite devices with lateral Cr electrodes by applying first order reversal curve (FORC) bias waveform in I-V , Kelvin probe force microscopy (KPFM) measurements, and in-situ chemical imaging by time-resolved time-of-flight secondary ion mass spectrometry (tr-ToF-SIMS). In dark, we reveal pronounced hysteretic behaviors of charge dynamics in the off-field by probing time-dependent current and contact potential difference (CPD). Under illumination, transient and hysteretic behaviors are significantly reduced. The tr-ToF-SIMS results reveal that the hysteretic behaviors are strongly associated with accumulation of Br- ions at the interfaces. In addition, the low mobility MA+ ions result in transient behavior and contribute to the hysteretic phenomena. It was shown that Pb2+ ions can be reduced at the interfaces due to electrochemical reactions with the electrode in the presence of charge injection and photogenerated charges. These hysteretic behaviors associated with charge dynamics, ion migration, and interfacial electrochemical reaction are critical to further improve the performance and stability of MHPs photovoltaics and optoelectronics. [Display omitted] • The interfacial and bulk electrochemical dynamics in lateral electrodes devices with double cations and double halides (FAPbI 3) 0.85 (MAPbBr 3) 0.15 perovskite films are revelead using the combination of time-resolved- I-V , KPFM, and ToF-SIMS measurements. • The Br- ions accumulate at the interface between (FAPbI 3) 0.85 (MAPbBr 3) 0.15 perovskites and drain electrode during the measurements. • The low mobility MA+ ions result in transient behavior and contribute to the hysteretic phenomena in the I-V and KPFM results. • The Pb2+ ions can be reduced at the interfaces due to electrochemical reactions with the electrode. • The ion migration can be fully or partially reversible while the electrochemical reaction at the interface can result in non-reversible and permanent damage to the device performance and stability. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has become an indispensable tool in hyperspectral data analysis, automatically extracting valuable features from complex, high-dimensional datasets. Super-resolution reconstruction, an essential aspect of hyperspectral data, involves enhancing spatial resolution, particularly relevant to low-resolution hyperspectral data. Yet, the pursuit of super-resolution in hyperspectral analysis is fraught with challenges, including acquiring ground truth high-resolution data for training, generalization, and scalability. The pressing issue of extended spectral acquisition times, notably for high-resolution scans, is a significant roadblock in hyperspectral imaging. Super-resolution methods offer a promising solution by providing higher spatial resolution data to expedite data collection and yield more efficient outcomes. This paper delves into a practical application of these concepts using Raman imaging, where spectral acquisition times can be prohibitively long. In this context, DL-based super-resolution models demonstrate their efficacy by predicting and reconstructing high-resolution Raman data from low-resolution input, eliminating the need for resource-intensive high-resolution scans. While previous work often relied on substantial high-resolution datasets, this study showcases the ability to achieve similar outcomes even with limited data, presenting a more practical and cost-effective approach. The results offer a glimpse into the transformative potential of this technology to streamline hyperspectral imaging applications by saving valuable time and resources through the successful generation of high-resolution data from low-resolution inputs. [ABSTRACT FROM AUTHOR]

Ferroelastic, ferroelectric, ion migration, metal halide perovskites, strain Ferroic Halide Perovskite Optoelectronics (Adv. Funct. Keywords: ferroelastic; ferroelectric; ion migration; metal halide perovskites; strain EN ferroelastic ferroelectric ion migration metal halide perovskites strain 1 1 1 09/06/21 20210902 NES 210902 B Metal Halide Perovskites b In article number 2102793, Mahshid Ahmadi, Olga S. Ovchinnikova, and co-workers highlight the coexistence of multiple phenomena such as ferroicity, ionicity, and strain that contributes to the performance of metal halide perovskite (MHP) devices and opens up broad opportunities for the applications of MHPs. [Extracted from the article]

Two-dimensional transition-metal dichalcogenides (TMDs) have a strain-sensitive nature and can only exhibit in-plane piezoelectricity, owing to their in-plane inversion symmetry breaking, which limits their practical applications for vertical stimulations. In this study, we demonstrated the capability of focused ion beams to create out-of-plane piezoelectricity on multi-layered MoTe 2. We utilized a focused helium ion beam to selectively pattern the out-of-plane piezoelectricity via defect engineering in a layered MoTe 2 flake. The generated out-of-plane piezoelectricity in the desired area was quantitatively examined using atomic force microscopy, and ion beam irradiation-induced defect formation that gave rise to inversion symmetry breaking was confirmed. These results indicated that the out-of-plane piezoelectricity can be selectively patterned through a focused helium ion beam, and it is expected that this approach can also be applied to other classes of TMDs and can expand the application fields of TMD-based devices. ga1 • Selective patterning of out-of-plane piezoelectricity in 2D TMDs. • Generation/improvement of piezoelectricity using ion beam unlike usual reports on the ion beam irradiation. • Atomic-scale inversion symmetry breaking via local defect formation. [ABSTRACT FROM AUTHOR]

Imaging mass spectrometry (IMS) is a powerful analytical technique widely used in biology, chemistry, and materials science fields that continue to expand. IMS provides a qualitative compositional analysis and spatial mapping with high chemical specificity. The spatial mapping information can be 2D or 3D depending on the analysis technique employed. Due to the combination of complex mass spectra coupled with spatial information, large high-dimensional datasets (hyperspectral) are often produced. Therefore, the use of automated computational methods for an exploratory analysis is highly beneficial. The fast-paced development of artificial intelligence (AI) and machine learning (ML) tools has received significant attention in recent years. These tools, in principle, can enable the unification of data collection and analysis into a single pipeline to make sampling and analysis decisions on the go. There are various ML approaches that have been applied to IMS data over the last decade. In this review, we discuss recent examples of the common unsupervised (principal component analysis, non-negative matrix factorization, k-means clustering, uniform manifold approximation and projection), supervised (random forest, logistic regression, XGboost, support vector machine), and other methods applied to various IMS datasets in the past five years. The information from this review will be useful for specialists from both IMS and ML fields since it summarizes current and representative studies of computational ML-based exploratory methods for IMS. [ABSTRACT FROM AUTHOR]

The unique properties of ferroelectric materials enable a plethora of applications, which are hindered by the phenomenon known as ferroelectric fatigue that leads to the degradation of ferroelectric properties with polarization cycling. Multiple microscopic models explaining fatigue have been suggested; however, the chemical origins remain poorly understood. Here, we utilize multimodal chemical imaging that combines atomic force microscopy with time-of-flight secondary mass spectrometry to explore the chemical phenomena associated with fatigue in PbZr0.2Ti0.8O3 (PZT) thin films. Investigations reveal that the degradation of ferroelectric properties is correlated with a local chemical change and migration of electrode ions into the PZT structure. Density functional theory simulations support the experimental results and demonstrate stable doping of the thin surface PZT layer with copper ions, leading to a decrease in the spontaneous polarization. Overall, the performed research allows for the observation and understanding of the chemical phenomena associated with polarization cycling and their effects on ferroelectric functionality. Ferroelectric fatigue degrades ferroelectric properties upon polarization cycling, but its underlying chemistry is poorly understood. Here, the authors show by multimodal chemical imaging that fatigue in PbZr0.2Ti0.8O3 thin films is associated with Cu + ions migration from the electrode into the film structure. [ABSTRACT FROM AUTHOR]

Genome engineering for materials synthesis is a promising avenue for manufacturing materials with unique properties under ambient conditions. Biomineralization in diatoms, unicellular algae that use silica to construct micron-scale cell walls with nanoscale features, is an attractive candidate for functional synthesis of materials for applications including photonics, sensing, filtration, and drug delivery. Therefore, controllably modifying diatom structure through targeted genetic modifications for these applications is a very promising field. In this work, we used gene knockdown in Thalassiosira pseudonana diatoms to create modified strains with changes to structural morphology and linked genotype to phenotype using supervised machine learning. An artificial neural network (NN) was developed to distinguish wild and modified diatoms based on the SEM images of frustules exhibiting phenotypic changes caused by a specific protein (Thaps3_21880), resulting in 94% detection accuracy. Class activation maps visualized physical changes that allowed the NNs to separate diatom strains, subsequently establishing a specific gene that controls pores. A further NN was created to batch process image data, automatically recognize pores, and extract pore-related parameters. Class interrelationship of the extracted paraments was visualized using a multivariate data visualization tool, called CrossVis, and allowed to directly link changes in morphological diatom phenotype of pore size and distribution with changes in the genotype. [ABSTRACT FROM AUTHOR]

The coupling of atomic force microscopy with infrared spectroscopy (AFM-IR) offers the unique capability to characterize the local chemical and physical makeup of a broad variety of materials with nanoscale resolution. However, in order to fully utilize the measurement capability of AFM-IR, a three-dimensional dataset (2D map with a spectroscopic dimension) needs to be acquired, which is prohibitively time-consuming at the same spatial resolution of a regular AFM scan. In this paper, we provide a new approach to process spectral AFM-IR data based on a multicomponent pan-sharpening algorithm. This approach requires only a low spatial resolution spectral and a limited number of high spatial resolution single wavenumber chemical maps to generate a high spatial resolution hyperspectral image, greatly reducing data acquisition time. As a result, we are able to generate high-resolution maps of component distribution, produce chemical maps at any wavenumber available in the spectral range, and perform correlative analysis of the physical and chemical properties of the samples. We highlight our approach via imaging of plant cell walls as a model system and showcase the interplay between mechanical stiffness of the sample and its chemical composition. We believe our pan-sharpening approach can be more generally applied to different material classes to enable deeper understanding of that structure-property relationship at the nanoscale. [ABSTRACT FROM AUTHOR]

The rapid development of spectral-imaging methods in scanning probe, electron, and optical microscopy in the last decade have given rise for large multidimensional datasets. In many cases, the reduction of hyperspectral data to the lower-dimension materials-specific parameters is based on functional fitting, where an approximate form of the fitting function is known, but the parameters of the function need to be determined. However, functional fits of noisy data realized via iterative methods, such as least-square gradient descent, often yield spurious results and are very sensitive to initial guesses. Here, we demonstrate an approach for the reduction of the hyperspectral data using a deep neural network approach. A combined deep neural network/least-square approach is shown to improve the effective signal-to-noise ratio of band-excitation piezoresponse force microscopy by more than an order of magnitude, allowing characterization when very small driving signals are used or when a material's response is weak. [ABSTRACT FROM AUTHOR]

Resistive switching in thin films has been widely studied in a broad range of materials. Yet, the mechanisms behind electroresistive switching have been persistently difficult to decipher and control, in part due to their non-equilibrium nature. Here, we demonstrate new experimental approaches that can probe resistive switching phenomena, utilizing amorphous TaOx as a model material system. Specifically, we applied scanning microwave impedance microscopy and cathodoluminescence (CL) microscopy as direct probes of conductance and electronic structure, respectively. These methods provide direct evidence of the electronic state of TaOx despite its amorphous nature. For example, CL identifies characteristic impurity levels in TaOx, in agreement with first principles calculations. We applied these methods to investigate He-ion-beam irradiation as a path to activate conductivity of materials and enable electroforming-free control over resistive switching. However, we find that even though He-ions begin to modify the nature of bonds even at the lowest doses, the films' conductive properties exhibit remarkable stability with large displacement damage and they are driven to metallic states only at the limit of structural decomposition. Finally, we show that electroforming in a nanoscale junction can be carried out with a dissipated power of <20 nW, a much smaller value compared to earlier studies and one that minimizes irreversible structural modifications of the films. The multimodal approach described here provides a new framework toward the theory/experiment guided design and optimization of electroresistive materials. [ABSTRACT FROM AUTHOR]

Nanoscale hyperspectral techniques—such as electron energy loss spectroscopy (EELS)—are critical to understand the optical response in plasmonic nanostructures, but as systems become increasingly complex, the required sampling density and acquisition times become prohibitive for instrumental and specimen stability. As a result, there has been a recent push for new experimental methodologies that can provide comprehensive information about a complex system, while significantly reducing the duration of the experiment. Here, we present a pan-sharpening approach to hyperspectral EELS analysis, where we acquire two datasets from the same region (one with high spatial resolution and one with high spectral fidelity) and combine them to achieve a single dataset with the beneficial properties of both. This work outlines a straightforward, reproducible pathway to reduced experiment times and higher signal-to-noise ratios, while retaining the relevant physical parameters of the plasmonic response, and is generally applicable to a wide range of spectroscopy modalities. [ABSTRACT FROM AUTHOR]

A third of the energy from fuel combustion in passenger car gasoline engines is lost due to friction. Carefully designed engine lubricants can recover some of these losses by reducing friction and wear by forming a nanometer-scale chemico-physico tribofilm between surfaces. Accordingly, attention has focused on developing oil formulations that form low-friction tribofilms. However, analyses of resultant tribofilms are typically conducted after tribo-tests with conventional characterization tools and do not offer insights into tribofilm formation and evolution, precluding information critical to tuning tribofilm properties. In this work, we developed a unique multimodal methodology based on Atomic Force Microscopy (AFM) with local probe heating for in situ tribological studies that activates friction modifiers and simultaneously captures the evolution of friction and surface roughness, with nanometer resolution. As a platform to demonstrate the ability of this methodology to visualize dynamics of tribofilm formation in situ, we apply it to molybdenum-based friction modifiers to distinguish key factors in their functionality and correlate nanoscale AFM and Friction Force Microscopy data to bench tribo-tests used in the industry. To decode the formation mechanisms observed in situ and underlying chemistry of tribofilms, we performed ab initio Molecular Dynamics (AIMD) simulations at comparable conditions. AIMD simulations confirmed both nanoscale and bench tribo-test results and showed deviations in molecular organization in tribofilms that are formed from different molybdenum-based friction modifiers that corroborates with surface functionality. With this innovative methodology, we demonstrate proof-of-principle in situ formation of molybdenum-based tribofilms directly on steel surfaces that could be applied generally to studying tribofilm formation. [ABSTRACT FROM AUTHOR]

Prostate cancer is one of the most common cancers globally and is the second most common cancer in the male population in the US. Here we develop a study based on correlating the hematoxylin and eosin (H&E)-stained biopsy data with MALDI mass-spectrometric imaging data of the corresponding tissue to determine the cancerous regions and their unique chemical signatures and variations of the predicted regions with original pathological annotations. We obtain features from high-resolution optical micrographs of whole slide H&E stained data through deep learning and spatially register them with mass spectrometry imaging (MSI) data to correlate the chemical signature with the tissue anatomy of the data. We then use the learned correlation to predict prostate cancer from observed H&E images using trained coregistered MSI data. This multimodal approach can predict cancerous regions with ∼80% accuracy, which indicates a correlation between optical H&E features and chemical information found in MSI. We show that such paired multimodal data can be used for training feature extraction networks on H&E data which bypasses the need to acquire expensive MSI data and eliminates the need for manual annotation saving valuable time. Two chemical biomarkers were also found to be predicting the ground truth cancerous regions. This study shows promise in generating improved patient treatment trajectories by predicting prostate cancer directly from readily available H&E-stained biopsy images aided by coregistered MSI data. [ABSTRACT FROM AUTHOR]

4D-STEM datasets obtained from EMPAD detectors broke the TEM resolution limits of 0.4Å in 2018 [[37]] and 0.25Å in 2021 [[38]] by using electron ptychography. Optical microscopes inspired the first TEM, and since then several new imaging modalities have been implemented, such as electron holography [[6]], Lorentz electron microscopy [[8]], and scanning TEM (STEM) [[9]]. Multidimensional Electron Microscopy Enabled by Detector Advances Electronic detectors have been used for TEM image acquisition since the early nineties. Keywords: electron microscope; edge computer; high-performance computing; 4D STEM; electron detectors EN electron microscope edge computer high-performance computing 4D STEM electron detectors 10 19 10 11/29/22 20221101 NES 221101 Introduction Ernst Ruska built the first transmission electron microscope (TEM) during his doctoral studies, and it celebrates its eightieth anniversary this year [[1]-[3]]. [Extracted from the article]

[Display omitted] Indirect absorption extended below the direct transition edge and increase in carrier lifetime derived from Rashba spin–orbit coupling may advance the optoelectronic applications of metal halide perovskites. Spin-orbit coupling in halide perovskites is due to the presence of heavy elements in their structure. However, when these materials lack an inversion symmetry, for example by the application of strain, spin–orbit coupling becomes odd in the electron's momentum giving rise to a splitting in the electronic energy bands. Here we report on the observation of a large Rashba splitting of 117 meV at room temperature, as predicted by relativistic first-principles calculations, in halide perovskite single crystals through a facile compositional engineering approach. Partial substitution of organic cations by rubidium in single crystals induces significant indirect absorption and dual peak photoluminescence as a result of a large Rashba splitting. We measured circularly polarized photoluminescence and magneto-photoluminescence in perovskite films printed by single crystals as well as magneto-electroluminescence and magneto-photocurrent in spin-LEDs based on perovskite single crystals. They indicated significant spin-momentum locking due to the large Rashba effect. A hybrid perovskite single crystal photodetector achieved record figures of merit, including detectivity of more than 1.3 × 1018 Jones which represents a three orders of magnitude improvement compared to the to date record. These findings show that facile compositional engineering of perovskite single crystals holds great promise for further advancing the optoelectronic properties of existing materials. [ABSTRACT FROM AUTHOR]

We explore the potential of the Helium Ion Microscope (HIM) as a tool for direct-write patterning of graphene and describe the underlying processes of graphene milling with image data processing. Controlled helium ion irradiation of suspended graphene conducted while monitoring the mill in-situ revealed the localized formation of nanopores, their growth, and coalescence. We also explore the effects of defects on the milling dynamics, and show that pre-exposed membranes rupture by cracking and rapid crack propagation at the edges of the growing defects. The mechanism for the rupturing process is described by local defect formation by excessive irradiation of helium ions, dictated by the scanning direction of the beam. These findings enrich fundamental understanding of the graphene milling process with a helium ion beam that, is necessary for high-resolution and high throughput patterning of graphene with nanoscale precision. [ABSTRACT FROM AUTHOR]

Graphene, with unique mechanical and electrical properties, offers diverse application opportunities from beyond Moore’s nano-electronics to water filtration. However, accomplishing these relies on cleaning and processing large areas of supported and suspended graphene sheets. Here, we demonstrate the use of an Ar cluster ion beam as a versatile tool for graphene cleaning, defect engineering, and nanopore fabrication. In-situ and ex-situ characterization techniques were utilized in combination with first principles molecular dynamics, to highlight the differences in processing of the supported and suspended graphene samples. In this work, we monitor interaction of the Ar cluster ion beam with graphene as a function of ion dose with in-situ secondary ion mass spectrometry (SIMS), as well as ex-situ Raman spectroscopy measurements. Scanning transmission electron microscopy (STEM) results confirm that the Ar cluster ion beam can form nanopores in the suspended graphene; while the basal plane of graphene remains intact maintaining the lattice structure. Finally, Scanning Electron Microscopy (SEM) image analysis of the irradiated samples allows quantitative tracking of pore areas and their distribution as a function of ion dose. This study highlights the flexibility of the Ar cluster ion beam in 2D material processing, and offers insights into Ar beam interaction with graphene. [ABSTRACT FROM AUTHOR]

Atomic force microscopy is widely used for nanoscale characterization of materials by scientists worldwide. The long-held belief of ambient AFM is that the tip is generally chemically inert but can be functionalized with respect to the studied sample. This implies that basic imaging and scanning procedures do not affect surface and bulk chemistry of the studied sample. However, an in-depth study of the confined chemical processes taking place at the tip-surface junction and the associated chemical changes to the material surface have been missing as of now. Here, we used a hybrid system that combines time-of-flight secondary ion mass spectrometry with an atomic force microscopy to investigate the chemical interactions that take place at the tip-surface junction. Investigations showed that even basic contact mode AFM scanning is able to modify the surface of the studied sample. In particular, we found that the silicone oils deposited from the AFM tip into the scanned regions and spread to distances exceeding 15 μm from the tip. These oils were determined to come from standard gel boxes used for the storage of the tips. The explored phenomena are important for interpreting and understanding results of AFM mechanical and electrical studies relying on the state of the tip-surface junction. [ABSTRACT FROM AUTHOR]

The presence of the heavy elements leads to strong spin-orbit coupling (SOC) in hybrid organic-inorganic perovskites (HOIPs), which plays an important role in the photovoltaic performance of HOIPs by changing the populations between bright and dark states. The organic cation is a critical composition and affects SOC via the interaction with the inorganic cage. In this work, we use magneto-photocurrent measurement to explore the SOC effect upon using mixed organic cations (methylammonium (MA) and formamidinium (FA)) with different dipole moments in Pb based perovskites. Magneto-photocurrent measurements showed that the internal magnetic parameter B 0 is decreased from 402.41 mT to 180.18 mT and 104.58 mT with decreasing the MA/FA ratio. This provides evidence that changing the internal dipole moment by using mixed organic cations can essentially change the SOC in HOIPs. Simultaneously, the photocurrent is increased from 21.46 mA/cm2 to 22.60 mA/cm2 and 23.08 mA/cm2 when the MA/FA ratio is changed from 1:0 to 0.9:0.1 and 0.7:0.3. Above all, our results indicate that the change in SOC by different organic cations can also be a factor affecting the performance of perovskite solar cells. Image 1 • Organic cations mixing can essentially change the spin-orbit coupling. • Photocurrent of perovskite solar cells can be effectively tuned by changing spin-orbit coupling via organic cation mixing. • Using magneto-photocurrent measurements to monitor spin-orbit coupling change of perovskite solar cells. [ABSTRACT FROM AUTHOR]

Moving toward a future of efficient, accessible, and less carbon-reliant energy devices has been at the forefront of energy research innovations for the past 30 years. Metal-halide perovskite (MHP) thin films have gained significant attention due to their flexibility of device applications and tunable capabilities for improving power conversion efficiency. Serving as a gateway to optimize device performance, consideration must be given to chemical synthesis processing techniques. Therefore, how does common substrate processing techniques influence the behavior of MHP phenomena such as ion migration and strain? Here, we demonstrate how a hybrid approach of chemical bath deposition (CBD) and nanoparticle SnO 2 substrate processing significantly improves the performance of (FAPbI 3 ) 0.97 (MAPbBr 3 ) 0.03 by reducing micro-strain in the SnO 2 lattice, allowing distribution of K + from K-Cl treatment of substrates to passivate defects formed at the interface and produce higher current in light and dark environments. X-ray diffraction reveals differences in lattice strain behavior with respect to SnO 2 substrate processing methods. Through use of conductive atomic force microscopy (c-AFM), conductivity is measured spatially with MHP morphology, showing higher generation of current in both light and dark conditions for films with hybrid processing. Additionally, time-of-flight secondary ionization mass spectrometry (ToF-SIMS) observed the distribution of K + at the perovskite/SnO 2 interface, indicating K + passivation of defects to improve the power conversion efficiency (PCE) and device stability. We show how understanding the role of ion distribution at the SnO 2 and perovskite interface can help reduce the creating of defects and promote a more efficient MHP device.

Purpose: Deep learning (DL) models have received much attention lately for their ability to achieve expert-level performance on the accurate automated analysis of chest X-rays (CXRs). Recently available public CXR datasets include high resolution images, but state-of-the-art models are trained on reduced size images due to limitations on graphics processing unit memory and training time. As computing hardware continues to advance, it has become feasible to train deep convolutional neural networks on high-resolution images without sacrificing detail by downscaling. This study examines the effect of increased resolution on CXR classification performance., Approach: We used the publicly available MIMIC-CXR-JPG dataset, comprising 377,110 high resolution CXR images for this study. We applied image downscaling from native resolution to 2048 × 2048    pixels , 1024 × 1024    pixels , 512 × 512    pixels , and 256 × 256    pixels and then we used the DenseNet121 and EfficientNet-B4 DL models to evaluate clinical task performance using these four downscaled image resolutions., Results: We find that while some clinical findings are more reliably labeled using high resolutions, many other findings are actually labeled better using downscaled inputs. We qualitatively verify that tasks requiring a large receptive field are better suited to downscaled low resolution input images, by inspecting effective receptive fields and class activation maps of trained models. Finally, we show that stacking an ensemble across resolutions outperforms each individual learner at all input resolutions while providing interpretable scale weights, indicating that diverse information is extracted across resolutions., Conclusions: This study suggests that instead of focusing solely on the finest image resolutions, multi-scale features should be emphasized for information extraction from high-resolution CXRs., (© 2023 Society of Photo-Optical Instrumentation Engineers (SPIE).)