DeepSSM: A blueprint for image-to-shape deep learning models

Statistical shape modeling (SSM) characterizes anatomical variations in a population of shapes generated from medical images. Statistical analysis of shapes requires consistent shape representation across samples in shape cohort. Establishing this representation entails a processing pipeline that includes anatomy segmentation, image re-sampling, shape-based registration, and non-linear, iterative optimization. These shape representations are then used to extract low-dimensional shape descriptors that are anatomically relevant to facilitate subsequent statistical analyses in different applications. However, the current process of obtaining these shape descriptors from imaging data relies on human and computational resources, requiring domain expertise for segmenting anatomies of interest. Moreover, this same taxing pipeline needs to be repeated to infer shape descriptors for new image data using a pre-trained/existing shape model. Here, we propose DeepSSM, a deep learning-based framework for learning the functional mapping from images to low-dimensional shape descriptors and their associated shape representations, thereby inferring statistical representation of anatomy directly from 3D images. Once trained using an existing shape model, DeepSSM circumvents the heavy and manual pre-processing and segmentation required by classical models and significantly improves the computational time, making it a viable solution for fully end-to-end shape modeling applications. In addition, we introduce a model-based data-augmentation strategy to address data scarcity, a typical scenario in shape modeling applications. Finally, this paper presents and analyzes two different architectural variants of DeepSSM with different loss functions using three medical datasets and their downstream clinical application. Experiments showcase that DeepSSM performs comparably or better to the state-of-the-art SSM both quantitatively and on application-driven downstream tasks. Therefore, DeepSSM aims to provide a comprehensive blueprint for deep learning-based image-to-shape models.

High-entropy oxychloride increasing the stability of Li-sulfur batteries

A novel lithiated high-entropy oxychloride Li_0.5(Zn_0.25Mg_0.25Co_0.25Cu_0.25)_0.5Fe₂O_3.5Cl_0.5 (LiHEOFeCl) with spinel structure belonging to the cubic Fd3̄m space group is synthesized by a mechanochemical-thermal route. Cyclic voltammetry measurement of the pristine LiHEOFeCl sample confirms its excellent electrochemical stability and the initial charge capacity of 648 mA h g^-1. The reduction of LiHEOFeCl starts at ca. 1.5 V vs. Li⁺/Li, which is outside the electrochemical window of the Li-S batteries (1.7/2.9 V). The addition of the LiHEOFeCl material to the composite of carbon with sulfur results in improved long-term electrochemical cycling stability and increased charge capacity of this cathode material in Li-S batteries. The carbon/LiHEOFeCl/sulfur cathode provides a charge capacity of 530 mA h g^-1 after 100 galvanostatic cycles, which represents ca. 33% increase as compared to the charge capacity of the blank carbon/sulfur composite cathode after 100 cycles. This considerable effect of the LiHEOFeCl material is assigned to its excellent structural and electrochemical stability within the potential window of 1.7 V/2.9 V vs. Li⁺/Li. In this potential region, our LiHEOFeCl has no inherent electrochemical activity. Hence, it acts solely as an electrocatalyst accelerating the redox reactions of polysulfides. This can be beneficial for the performance of Li-S batteries, as evidenced by reference experiments with TiO₂ (P90).

Quantifying the Severity of Metopic Craniosynostosis Using Unsupervised Machine Learning

BACKGROUND

Quantifying the severity of head shape deformity and establishing a threshold for operative intervention remains challenging in patients with metopic craniosynostosis (MCS). This study combines three-dimensional skull shape analysis with an unsupervised machine-learning algorithm to generate a quantitative shape severity score (cranial morphology deviation) and provide an operative threshold score.

METHODS

Head computed tomography scans from subjects with MCS and normal controls (5 to 15 months of age) were used for objective three-dimensional shape analysis using ShapeWorks software and in a survey for craniofacial surgeons to rate head-shape deformity and report whether they would offer surgical correction based on head shape alone. An unsupervised machine-learning algorithm was developed to quantify the degree of shape abnormality of MCS skulls compared to controls.

RESULTS

One hundred twenty-four computed tomography scans were used to develop the model; 50 (24% MCS, 76% controls) were rated by 36 craniofacial surgeons, with an average of 20.8 ratings per skull. The interrater reliability was high (intraclass correlation coefficient, 0.988). The algorithm performed accurately and correlates closely with the surgeons assigned severity ratings (Spearman correlation coefficient, r = 0.817). The median cranial morphology deviation for affected skulls was 155.0 (interquartile range, 136.4 to 194.6; maximum, 231.3). Skulls with ratings of 150.2 or higher were very likely to be offered surgery by the experts in this study.

CONCLUSIONS

This study describes a novel metric to quantify the head shape deformity associated with MCS and contextualizes the results using clinical assessments of head shapes by craniofacial experts. This metric may be useful in supporting clinical decision making around operative intervention and in describing outcomes and comparing patient population across centers.

Oxidative dehydrogenation of cyclohexene on atomically precise subnanometer Cu4-nPdn (0 ≤ n ≤ 4) tetramer clusters: the effect of cluster composition and support on performance

The pronounced effects of the composition of four-atom monometallic Cu and Pd and bimetallic CuPd clusters and the support on the catalytic activity and selectivity in the oxidative dehydrogenation of cyclohexene are reported. The ultra-nanocrystalline diamond supported clusters are highly active and dominantly produce benzene; some of the mixed clusters also produce cyclohexadiene, which are all clusters with a much suppressed combustion channel. The also highly active TiO₂-supported tetramers solely produce benzene, without any combustion to CO₂. The selectivity of the zirconia-supported mixed CuPd clusters and the monometallic Cu cluster is entirely different; though they are less active in comparison to clusters with other supports, these clusters produce significant fractions of cyclohexadiene, with their selectivity towards cyclohexadiene gradually increasing with the increasing number of copper atoms in the cluster, reaching about 50% for Cu₃Pd₁. The zirconia-supported copper tetramer stands out from among all the other tetramers in this reaction, with a selectivity towards cyclohexadiene of 70%, which far exceeds those of all the other cluster-support combinations. The findings from this study indicate a positive effect of copper on the stability of the mixed tetramers and potential new ways of fine-tuning catalyst performance by controlling the composition of the active site and via cluster-support interactions in complex oxidative reactions under the suppression of the undesired combustion of the feed.

Atom by atom built subnanometer copper cluster catalyst for the highly selective oxidative dehydrogenation of cyclohexene

The effect of particle size and support on the catalytic performance of supported subnanometer copper clusters was investigated in the oxidative dehydrogenation of cyclohexene. From among the investigated seven size-selected subnanometer copper particles between a single atom and clusters containing 2–7 atoms, the highest activity was observed for the titania-supported copper tetramer with 100% selectivity toward benzene production and being about an order of magnitude more active than not only all the other investigated cluster sizes on the same support but also the same tetramer on the other supports, Al2O3, SiO2, and SnO2. In addition to the profound effect of cluster size on activity and with Cu4 outstanding from the studied series, Cu4 clusters supported on SiO2 provide an example of tuning selectivity through support effects when this particular catalyst also produces cyclohexadiene with about 30% selectivity. Titania-supported Cu5 and Cu7 clusters supported on TiO2 produce a high fraction of cyclohexadiene in contrast to their neighbors, while Cu4 and Cu6 solely produce benzene without any combustion, thus representing odd–even oscillation of selectivity with the number of atoms in the cluster.

Interactive Deep Colorization and its Application for Image Compression

Recent methods based on deep learning have shown promise in converting grayscale images to colored ones. However, most of them only allow limited user inputs (no inputs, only global inputs, or only local inputs), to control the output colorful images. The possible difficulty lies in how to differentiate the influences of different inputs. To solve this problem, we propose a two-stage deep colorization method allowing users to control the results by flexibly setting global inputs and local inputs. The key steps include enabling color themes as global inputs by extracting K mean colors and generating K-color maps to define a global theme loss, and designing a loss function to differentiate the influences of different inputs without causing artifacts. We also propose a color theme recommendation method to help users choose color themes. Based on the colorization model, we further propose an image compression scheme, which supports variable compression ratios in a single network. Experiments on colorization show that our method can flexibly control the colorized results with only a few inputs and generate state-of-the-art results. Experiments on compression show that our method achieves much higher image quality at the same compression ratio when compared to the state-of-the-art methods.

Soft Articulated Characters in Projective Dynamics

We propose a fast and robust solver to simulate continuum-based deformable models with constraints, in particular, rigid-body and joint constraints useful for soft articulated characters. Our method embeds the degrees of freedom of both articulated rigid bodies and deformable bodies in one unified constrained optimization problem, thus coupling the deformable and rigid bodies. Inspired by Projective Dynamics which is a fast numerical solver to simulate deformable objects, we also propose a novel local/global solver that takes full advantage of the pre-factorized system matrices to accelerate the solve of our constrained optimization problem. Therefore, our method can efficiently simulate character models, with rigid-body parts (bones) being correctly coupled with deformable parts (flesh). Our method is stable because backward Euler time integration is applied to both rigid and deformable degrees of freedom. Our unified optimization problem is rigorously derived from constrained Newtonian mechanics. When simulating only articulated rigid bodies as a special case, our method converges to the state-of-the-art rigid body simulators.

Leveraging unsupervised image registration for discovery of landmark shape descriptor

In current biological and medical research, statistical shape modeling (SSM) provides an essential framework for the characterization of anatomy/morphology. Such analysis is often driven by the identification of a relatively small number of geometrically consistent features found across the samples of a population. These features can subsequently provide information about the population shape variation. Dense correspondence models can provide ease of computation and yield an interpretable low-dimensional shape descriptor when followed by dimensionality reduction. However, automatic methods for obtaining such correspondences usually require image segmentation followed by significant preprocessing, which is taxing in terms of both computation as well as human resources. In many cases, the segmentation and subsequent processing require manual guidance and anatomy specific domain expertise. This paper proposes a self-supervised deep learning approach for discovering landmarks from images that can directly be used as a shape descriptor for subsequent analysis. We use landmark-driven image registration as the primary task to force the neural network to discover landmarks that register the images well. We also propose a regularization term that allows for robust optimization of the neural network and ensures that the landmarks uniformly span the image domain. The proposed method circumvents segmentation and preprocessing and directly produces a usable shape descriptor using just 2D or 3D images. In addition, we also propose two variants on the training loss function that allows for prior shape information to be integrated into the model. We apply this framework on several 2D and 3D datasets to obtain their shape descriptors. We analyze these shape descriptors in their efficacy of capturing shape information by performing different shape-driven applications depending on the data ranging from shape clustering to severity prediction to outcome diagnosis.

Atomic layer deposited films of Al2O3 on fluorine-doped tin oxide electrodes: stability and barrier properties

Al₂O₃ layers were deposited onto electrodes by atomic layer deposition. Solubility and electron-transport blocking were tested. Films deposited onto fluorine-doped tin oxide (FTO, F:SnO₂/glass) substrates blocked electron transfer to redox couples (ferricyanide/ferrocyanide) in aqueous media. However, these films were rapidly dissolved in 1 M NaOH (≈100 nm/h). The dissolution was slower in 1 M H₂SO₄ (1 nm/h) but after 24 h the blocking behaviour was entirely lost. The optimal stability was reached at pH 7.2 where no changes were found up to 24 h and even after 168 h of exposure the changes in the blocking behaviour were still minimal. This behaviour was also observed for protection against direct reduction of FTO.

Self-Supervised Discovery of Anatomical Shape Landmarks

Statistical shape analysis is a very useful tool in a wide range of medical and biological applications. However, it typically relies on the ability to produce a relatively small number of features that can capture the relevant variability in a population. State-of-the-art methods for obtaining such anatomical features rely on either extensive preprocessing or segmentation and/or significant tuning and post-processing. These shortcomings limit the widespread use of shape statistics. We propose that effective shape representations should provide sufficient information to align/register images. Using this assumption we propose a self-supervised, neural network approach for automatically positioning and detecting landmarks in images that can be used for subsequent analysis. The network discovers the landmarks corresponding to anatomical shape features that promote good image registration in the context of a particular class of transformations. In addition, we also propose a regularization for the proposed network which allows for a uniform distribution of these discovered landmarks. In this paper, we present a complete framework, which only takes a set of input images and produces landmarks that are immediately usable for statistical shape analysis. We evaluate the performance on a phantom dataset as well as 2D and 3D images.

Scribble-Based 3D Shape Segmentation via Weakly-Supervised Learning

Shape segmentation is a fundamental problem in shape analysis. Previous research shows that prior knowledge helps to improve the segmentation accuracy and quality. However, completely labeling each 3D shape in a large training data set requires a heavy manual workload. In this paper, we propose a novel weakly-supervised algorithm for segmenting 3D shapes using deep learning. Our method jointly propagates information from scribbles to unlabeled faces and learns deep neural network parameters. Therefore, it does not rely on completely labeled training shapes and only needs a really simple and convenient scribble-based partially labeling process, instead of the extremely time-consuming and tedious fully labeling processes. Various experimental results demonstrate the proposed method's superior segmentation performance over the previous unsupervised approaches and comparable segmentation performance to the state-of-the-art fully supervised methods.

Detecting 3D Points of Interest Using Multiple Features and Stacked Auto-encoder

Considering the fact that points of interest on 3D shapes can be discriminated from a geometric perspective, it is reasonable to map the geometric signature of a point $p$p to a probability value encoding to what degree $p$p is a point of interest, especially for a specific class of 3D shapes. Based on the observation, we propose a three-phase algorithm for learning and predicting points of interest on 3D shapes by using multiple feature descriptors. Our algorithm requires two separate deep neural networks (stacked auto-encoders) to accomplish the task. During the first phase, we predict the membership of the given 3D shape according to a set of geometric descriptors using a deep neural network. After that, we train the other deep neural network to predict a probability distribution defined on the surface representing the possibility of a point being a point of interest. Finally, we use a manifold clustering technique to extract a set of points of interest as the output. Experimental results show superior detection performance of the proposed method over the previous state-of-the-art approaches.

Average Vector Field Integration for St. Venant-Kirchhoff Deformable Models

We propose Average Vector Field (AVF) integration for simulation of deformable solids in physics-based animation. Our method achieves exact energy conservation for the St. Venant-Kirchhoff material without any correction steps or extra parameters. Exact energy conservation implies that our resulting animations 1) cannot explode and 2) do not suffer from numerical damping, which are two common problems with previous numerical integration techniques. Our method produces lively motion even with large time steps as typically used in physics-based animation. Our implicit update rules can be formulated as a minimization problem and solved in a similar way as optimization-based backward Euler, with only a mild computing overhead. Our approach also supports damping and collision response models, making it easy to deploy in practical computer animation pipelines.

Transparent rutile TiO2 films prepared by thermal oxidation of sputtered Ti on FTO glass

TiO2 films were prepared via a two-step fabrication process, i.e. deposition of Ti films by magnetron sputtering on an FTO glass substrate followed by thermal oxidation at 600-725 °C. The investigated parameters were Ti layer thickness, temperature of oxidation and deposition conditions (pre-treatment and substrate heating). Such TiO2 films have a rutile structure and contain metallic Sn which is the result of a thermal reaction at the interface between SnO2 and Ti at temperatures above 500 °C. A calcination temperature of 600 °C is optimal for fabricating TiO2 films with significant photoelectrochemical response. Heating of the FTO substrate during magnetron sputtering deposition of Ti films results in a significant improvement of the compactness of the TiO2 films. A similar but not so pronounced improvement was observed for the TiO2 films deposited on the FTO substrate pre-treated with radio-frequency plasma under Ar-O2 and N2-H2 atmosphere. The observed correlation between the increased content of Sn in the TiO2 films and compactness of the TiO2 films supports the explanation of both positive effects by better adhesion of the Ti films to the FTO substrate.

Nanocrystalline Boron-Doped Diamond as a Corrosion-Resistant Anode for Water Oxidation via Si Photoelectrodes

Due to its high sensitivity to corrosion, the use of Si in direct photoelectrochemical (PEC) water-splitting systems that convert solar energy into chemical fuels has been greatly limited. Therefore, the development of low-cost materials resistant to corrosion under oxidizing conditions is an important goal toward a suitable protection of otherwise unstable semiconductors used in PEC cells. Here, we report on the development of a protective coating based on thin and electrically conductive nanocrystalline boron-doped diamond (BDD) layers. We found that  BDD layers protect the underlying Si photoelectrodes over a wide pH range (1-14) in aqueous electrolyte solutions. A BDD layer maintains an efficient charge carrier transfer from the underlying silicon to the electrolyte solution. Si|BDD photoelectrodes show no sign of performance degradation after a continuous PEC treatment in neutral, acidic, and basic electrolytes. The deposition of a cobalt phosphate (CoPi) oxygen evolution catalyst onto the BDD layer significantly reduces the overpotential for water oxidation, demonstrating the ability of  BDD layers to substitute the transparent conductive oxide coatings, such as indium tin oxide (ITO) and fluorine-doped tin oxide (FTO), frequently used as protective layers in Si photoelectrodes.

DeepSSM: A Deep Learning Framework for Statistical Shape Modeling from Raw Images

Statistical shape modeling is an important tool to characterize variation in anatomical morphology. Typical shapes of interest are measured using 3D imaging and a subsequent pipeline of registration, segmentation, and some extraction of shape features or projections onto some lower-dimensional shape space, which facilitates subsequent statistical analysis. Many methods for constructing compact shape representations have been proposed, but are often impractical due to the sequence of image preprocessing operations, which involve significant parameter tuning, manual delineation, and/or quality control by the users. We propose DeepSSM: a deep learning approach to extract a low-dimensional shape representation directly from 3D images, requiring virtually no parameter tuning or user assistance. DeepSSM uses a convolutional neural network (CNN) that simultaneously localizes the biological structure of interest, establishes correspondences, and projects these points onto a low-dimensional shape representation in the form of PCA loadings within a point distribution model. To overcome the challenge of the limited availability of training images with dense correspondences, we present a novel data augmentation procedure that uses existing correspondences on a relatively small set of processed images with shape statistics to create plausible training samples with known shape parameters. In this way, we leverage the limited CT/MRI scans (40-50) into thousands of images needed to train a deep neural net. After the training, the CNN automatically produces accurate low-dimensional shape representations for unseen images. We validate DeepSSM for three different applications pertaining to modeling pediatric cranial CT for characterization of metopic craniosynostosis, femur CT scans identifying morphologic deformities of the hip due to femoroacetabular impingement, and left atrium MRI scans for atrial fibrillation recurrence prediction.

Semi-automatic spray pyrolysis deposition of thin, transparent, titania films as blocking layers for dye-sensitized and perovskite solar cells

For proper function of the negative electrode of dye-sensitized and perovskite solar cells, the deposition of a nonporous blocking film is required on the surface of F-doped SnO₂ (FTO) glass substrates. Such a blocking film can minimise undesirable parasitic processes, for example, the back reaction of photoinjected electrons with the oxidized form of the redox mediator or with the hole-transporting medium can be avoided. In the present work, thin, transparent, blocking TiO₂ films are prepared by semi-automatic spray pyrolysis of precursors consisting of titanium diisopropoxide bis(acetylacetonate) as the main component. The variation in the layer thickness of the sprayed films is achieved by varying the number of spray cycles. The parameters investigated in this work were deposition temperature (150, 300 and 450 °C), number of spray cycles (20-200), precursor composition (with/without deliberately added acetylacetone), concentration (0.05 and 0.2 M) and subsequent post-calcination at 500 °C. The photo-electrochemical properties were evaluated in aqueous electrolyte solution under UV irradiation. The blocking properties were tested by cyclic voltammetry with a model redox probe with a simple one-electron-transfer reaction. Semi-automatic spraying resulted in the formation of transparent, homogeneous, TiO₂ films, and the technique allows for easy upscaling to large electrode areas. The deposition temperature of 450 °C was necessary for the fabrication of highly photoactive TiO₂ films. The blocking properties of the as-deposited TiO₂ films (at 450 °C) were impaired by post-calcination at 500 °C, but this problem could be addressed by increasing the number of spray cycles. The modification of the precursor by adding acetylacetone resulted in the fabrication of TiO₂ films exhibiting perfect blocking properties that were not influenced by post-calcination. These results will surely find use in the fabrication of large-scale dye-sensitized and perovskite solar cells.

Molecular Design of Efficient Organic D-A-π -A Dye Featuring Triphenylamine as Donor Fragment for Application in Dye-Sensitized Solar Cells

A metal-free organic sensitizer, suitable for the application in dye-sensitized solar cells (DSSCs), has been designed, synthesized and characterized both experimentally and theoretically. The structure of the novel donor-acceptor-π-bridge-acceptor (D-A-π-A) dye incorporates a triphenylamine (TPA) segment and 4-(benzo[c][1,2,5]thiadiazol-4-ylethynyl)benzoic acid (BTEBA). The triphenylamine unit is widely used as an electron donor for photosensitizers, owing to its nonplanar molecular configuration and excellent electron-donating capability, whereas 4-(benzo[c][1,2,5]thiadiazol-4-ylethynyl)benzoic acid is used as an electron acceptor unit. The influences of I₃^- /I^- , [Co(bpy)₃ ]^3+/2+ and [Cu(tmby)₂ ]^2+/+ (tmby=4,4',6,6'-tetramethyl-2,2'-bipyridine) as redox electrolytes on the DSSC device performance were also investigated. The maximal monochromatic incident photon-to-current conversion efficiency (IPCE) reached 81 % and the solar light to electrical energy conversion efficiency of devices with [Cu(tmby)₂ ]^2+/+ reached 7.15 %. The devices with [Co(bpy)₃ ]^3+/2+ and I₃^- /I^- electrolytes gave efficiencies of 5.22 % and 6.14 %, respectively. The lowest device performance with a [Co(bpy)₃ ]^3+/2+ -based electrolyte is attributed to increased charge recombination.

Functionalization of boron-doped diamond with a push–pull chromophore via Sonogashira and CuAAC chemistry

Functionalization of boron-doped diamond with a push–pull chromophore via Sonogashira cross-coupling affords better photovoltaic performances as compared to functionalization via CuAAC.

Copper Bipyridyl Redox Mediators for Dye-Sensitized Solar Cells with High Photovoltage

Redox mediators play a major role determining the photocurrent and the photovoltage in dye-sensitized solar cells (DSCs). To maintain the photocurrent, the reduction of oxidized dye by the redox mediator should be significantly faster than the electron back transfer between TiO₂ and the oxidized dye. The driving force for dye regeneration with the redox mediator should be sufficiently low to provide high photovoltages. With the introduction of our new copper complexes as promising redox mediators in DSCs both criteria are satisfied to enhance power conversion efficiencies. In this study, two copper bipyridyl complexes, Cu^(II/I)(dmby)₂TFSI_2/1 (0.97 V vs SHE, dmby = 6,6'-dimethyl-2,2'-bipyridine) and Cu^(II/I)(tmby)₂TFSI_2/1 (0.87 V vs SHE, tmby = 4,4',6,6'-tetramethyl-2,2'-bipyridine), are presented as new redox couples for DSCs. They are compared to previously reported Cu^(II/I)(dmp)₂TFSI_2/1 (0.93 V vs SHE, dmp = bis(2,9-dimethyl-1,10-phenanthroline). Due to the small reorganization energy between Cu(I) and Cu(II) species, these copper complexes can sufficiently regenerate the oxidized dye molecules with close to unity yield at driving force potentials as low as 0.1 V. The high photovoltages of over 1.0 V were achieved by the series of copper complex based redox mediators without compromising photocurrent densities. Despite the small driving forces for dye regeneration, fast and efficient dye regeneration (2-3 μs) was observed for both complexes. As another advantage, the electron back transfer (recombination) rates were slower with Cu^(II/I)(tmby)₂TFSI_2/1 as evidenced by longer lifetimes. The solar-to-electrical power conversion efficiencies for [Cu(tmby)₂]^2+/1+, [Cu(dmby)₂]^2+/1+, and [Cu(dmp)₂]^2+/1+ based electrolytes were 10.3%, 10.0%, and 10.3%, respectively, using the organic Y123 dye under 1000 W m^-2 AM1.5G illumination. The high photovoltaic performance of Cu-based redox mediators underlines the significant potential of the new redox mediators and points to a new research and development direction for DSCs.

Efficiency and stability of spectral sensitization of boron-doped-diamond electrodes through covalent anchoring of a donor–acceptor organic chromophore (P1)

A novel procedure is developed for chemical modification of H-terminated B-doped diamond surfaces with a donor–π-bridge–acceptor molecule (P1).

n-Type phosphorus-doped nanocrystalline diamond: electrochemical and in situ Raman spectroelectrochemical study

Electrochemical and in situ Raman spectroelectrochemical characterization of n-type phosphorus-doped nanocrystalline diamond (P-NCD) is carried out.

Single Layer Molybdenum Disulfide under Direct Out-of-Plane Compression: Low-Stress Band-Gap Engineering

Tuning the electronic structure of 2D materials is a very powerful asset toward tailoring their properties to suit the demands of future applications in optoelectronics. Strain engineering is one of the most promising methods in this regard. We demonstrate that even very small out-of-plane axial compression readily modifies the electronic structure of monolayer MoS2. As we show through in situ resonant and nonresonant Raman spectroscopy and photoluminescence measurements combined with theoretical calculations, the transition from direct to indirect band gap semiconductor takes place at ∼0.5 GPa, and the transition to a semimetal occurs at stress smaller than 3 GPa.

Room temperature spontaneous conversion of OCS to CO2 on the anatase TiO2 surface

High-resolution FT-IR spectroscopy combined with quantum chemical calculations was used to study the chemistry of OCS-disproportionation over the reduced surface of isotopically labelled, nanocrystalline TiO2. Analysis of the isotopic composition of the product gases has revealed that the reaction involves solely OCS molecules from the gas-phase. Using quantum chemical calculations we propose a plausible mechanistic scenario, in which two reduced Ti(3+) centres mediate the reaction of the adsorbed OCS molecules.

Visible-light sensitization of boron-doped nanocrystalline diamond through non-covalent surface modification

The boron-doped diamond electrode is efficiently sensitized with a light-harvesting molecule using a facile and versatile synthetic method.

Dye-sensitization of boron-doped diamond foam: champion photoelectrochemical performance of diamond electrodes under solar light illumination

Diamond foams composed of hollow spheres of polycrystalline boron-doped diamond are chemically modified with two donor–acceptor type molecular dyes, BT-Rho and CPDT-Fur, and tested as electrode materials for p-type dye-sensitized solar cells.

Optically transparent FTO-free cathode for dye-sensitized solar cells

The woven fabric containing electrochemically platinized tungsten wire is an affordable flexible cathode for liquid-junction dye-sensitized solar cells with the I3(-)/I(-) redox mediator and electrolyte solution consisting of ionic liquids and propionitrile. The fabric-based electrode outperforms the thermally platinized FTO in serial ohmic resistance and charge-transfer resistance for triiodide reduction, and it offers comparable or better optical transparency in the visible and particularly in the near-IR spectral region. The electrode exhibits good stability during electrochemical loading and storage at open circuit. The dye-sensitized solar cells with a C101-sensitized titania photoanode and either Pt-W/PEN or Pt-FTO cathodes show a comparable performance.

Doping of C70 fullerene peapods with lithium vapor: Raman spectroscopic and Raman spectroelectrochemical studies

Raman spectroscopy and in situ Raman spectroelectrochemistry were applied to study the lithium vapor doping of C70@SWCNTs (peapods). A strong degree of doping was proved by the vanishing of the single walled carbon nanotubes (SWCNT's) radial breathing mode (RBM) and by the attenuation of the tangential (TG) band intensity. In contrast to potassium vapor doping, the strong downshift of the frequency of the TG band has not been observed for Li-doping. The Li vapor treated peapods remained partly doped even if they were exposed to humid air. This has been reflected by a reduced intensity of the nanotube and the fullerene modes and by the change of the shape of the RBM band as compared to that of the undoped sample. The modes of the intratubular fullerene were almost unresolved after the contact of the Li-doped sample with water. A lithium insertion into the interior of a peapod and its strong interaction with the intratubular fullerene is suggested to be responsible for the air-insensitive residual doping. This residual doping was studied by spectroelectrochemical measurements. The TG band of the Li doped peapods is partly upshifted during the anodic doping, which points to the different state of C70@SWCNTs and C60@SWCNTs studied previously.

Carbon isotope labelling in graphene research

The large scale production of graphene for any potential application relies on catalytic chemical vapour deposition (CVD). Despite much effort put into the graphene CVD research, there are still many challenges to be solved, not only concerning the growth itself, but also the subsequent treatment, i.e. transfer from the catalyst to a desired substrate. The need for fast progress naturally necessitates easy-to-use, versatile and efficient characterization methods. This perspective reviews the recent advances and potential of probably the most prospective one--Raman spectroscopy in connection with carbon isotope labelling of the CVD grown graphene layers. We discuss its use for the explanation and optimization of the growth process, followed by examples of employing isotope engineering in the studies of fundamental properties of graphene, not only by Raman spectroscopy.