Synchrotron infrared nanospectroscopy in fourth-generation storage rings.

Fourth-generation synchrotron storage rings represent a significant milestone in synchrotron technology, offering outstandingly bright and tightly focused X-ray beams for a wide range of scientific applications. However, due to their inherently tight magnetic lattices, these storage rings have posed critical challenges for accessing lower-energy radiation, such as infrared (IR) and THz. Here the first-ever IR beamline to be installed and to operate at a fourth-generation synchrotron storage ring is introduced. This work encompasses several notable advancements, including a thorough examination of the new IR source at Sirius, a detailed description of the radiation extraction scheme, and the successful validation of our optical concept through both measurements and simulations. This optimal optical setup has enabled us to achieve an exceptionally wide frequency range for our nanospectroscopy experiments. Through the utilization of synchrotron IR nanospectroscopy on biological and hard matter samples, the practicality and effectiveness of this beamline has been successfully demonstrated. The advantages of fourth-generation synchrotron IR sources, which can now operate with unparalleled stability as a result of the stringent requirements for producing low-emittance X-rays, are emphasized.

Simultaneous Inside and Outside Functionalization of Single-Walled Carbon Nanotubes.

Functionalizing single-walled carbon nanotubes (SWCNTs) in a robust way that does not affect the sp2 carbon framework is a considerable research challenge. Here we describe how triiodide salts of positively charged macrocycles can be used not only to functionalize SWCNTs from the outside, but simultaneously from the inside. We employed disulfide exchange in aqueous solvent to maximize the solvophobic effect and therefore achieve a high degree of macrocycle immobilization. Characterization by Raman spectroscopy, EDX-STEM and HR-TEM clearly showed that serendipitously this wet-chemical functionalization procedure also led to the encapsulation of polyiodide chains inside the nanotubes. The resulting three-shell composite materials are redox-active and experience an intriguing interplay of electrostatic, solvophobic and mechanical effects that could be of interest for applications in energy storage.

Synthesis and Characterization of Bola-Amphiphilic Porphyrin-Perylenebisimide Architectures.

We report on the synthesis and characterization of a family of three water-soluble bola-amphiphilic zinc-porphyrin-perylenebisimide triads containing oligo carboxylic-acid capped Newkome dendrons in the periphery. Variations of the perylenebisimide (PBI) core geometry and dendron size (G1 and G2 dendrons with 3- and 9-carboxylic acid groups respectively) allow for tuning the supramolecular aggregation behavior with respect to variation of the molecular architecture. The triads show good solubility in basic aqueous media and aggregation to supramolecular assemblies. Theoretical investigations at the DFT level of theory accompanied by electrochemical measurements unravel the geometric and electronic structure of the amphiphiles. UV/Vis and fluorescence titrations with varying amounts of THF demonstrate disaggregation.

Speeding-up Hybrid Functional-Based Ab Initio Molecular Dynamics Using Multiple Time-stepping and Resonance-Free Thermostat.

Ab initio molecular dynamics (AIMD) based on density functional theory (DFT) has become a workhorse for studying the structure, dynamics, and reactions in condensed matter systems. Currently, AIMD simulations are primarily carried out at the level of generalized gradient approximation (GGA), which is at the second rung of DFT functionals in terms of accuracy. Hybrid DFT functionals, which form the fourth rung in the accuracy ladder, are not commonly used in AIMD simulations as the computational cost involved is 100 times or higher. To facilitate AIMD simulations with hybrid functionals, we propose here an approach using multiple time stepping with adaptively compressed exchange operator and resonance-free thermostat, that could speed up the calculations by ∼30 times or more for systems with a few hundred of atoms. We demonstrate that by achieving this significant speed up and making the compute time of hybrid functional-based AIMD simulations at par with that of GGA functionals, we are able to study several complex condensed matter systems and model chemical reactions in solution with hybrid functionals that were earlier unthinkable to be performed.

Social Learning versus Individual Learning in the Division of Labour.

Division of labour, or the differentiation of the individuals in a collective across tasks, is a fundamental aspect of social organisations, such as social insect colonies. It allows for efficient resource use and improves the chances of survival for the entire collective. The emergence of large inactive groups of individuals in insect colonies sometimes referred to as laziness, has been a puzzling and hotly debated division-of-labour phenomenon in recent years that is counter to the intuitive notion of effectiveness. It has previously been shown that inactivity can be explained as a by-product of social learning without the need to invoke an adaptive function. While highlighting an interesting and important possibility, this explanation is limited because it is not yet clear whether the relevant aspects of colony life are governed by social learning. In this paper, we explore the two fundamental types of behavioural adaptation that can lead to a division of labour, individual learning and social learning. We find that inactivity can just as well emerge from individual learning alone. We compare the behavioural dynamics in various environmental settings under the social and individual learning assumptions, respectively. We present individual-based simulations backed up by analytic theory, focusing on adaptive dynamics for the social paradigm and cross-learning for the individual paradigm. We find that individual learning can induce the same behavioural patterns previously observed for social learning. This is important for the study of the collective behaviour of social insects because individual learning is a firmly established paradigm of behaviour learning in their colonies. Beyond the study of inactivity, in particular, the insight that both modes of learning can lead to the same patterns of behaviour opens new pathways to approach the study of emergent patterns of collective behaviour from a more generalised perspective.

Explaining workers' inactivity in social colonies from first principles.

Social insects are among the ecologically most successful collectively living organisms, with efficient division of labour a key feature of this success. Surprisingly, these efficient colonies often have a large proportion of inactive workers in their workforce, sometimes referred to as lazy workers. The dominant hypotheses explaining this are based on specific life-history traits, specific behavioural features or uncertain environments where inactive workers can provide a 'reserve' workforce that can spring into action quickly. While there is a number of experimental studies that show and investigate the presence of inactive workers, mathematical and computational models exploring specific hypotheses are not common. Here, using a simple mathematical model, we show that a parsimonious hypothesis can explain this puzzling social phenomenon. Our model incorporates social interactions and environmental influences into a game-theoretical framework and captures how individuals react to environment by allocating their activity according to environmental conditions. This model shows that inactivity can emerge under specific environmental conditions as a by-product of the task allocation process. Our model confirms the empirical observation that in the case of worker loss, prior homeostatic balance is re-established by replacing some of the lost force with previously inactive workers. Most importantly, our model shows that inactivity in social colonies can be explained without the need to assume an adaptive function for this phenomenon.

Factors associated with successful median arcuate ligament release in an international, multi-institutional cohort.

OBJECTIVE: Prior research on median arcuate ligament syndrome has been limited to institutional case series, making the optimal approach to median arcuate ligament release (MALR) and resulting outcomes unclear. In the present study, we compared the outcomes of different approaches to MALR and determined the predictors of long-term treatment failure. METHODS: The Vascular Low Frequency Disease Consortium is an international, multi-institutional research consortium. Data on open, laparoscopic, and robotic MALR performed from 2000 to 2020 were gathered. The primary outcome was treatment failure, defined as no improvement in median arcuate ligament syndrome symptoms after MALR or symptom recurrence between MALR and the last clinical follow-up. RESULTS: For 516 patients treated at 24 institutions, open, laparoscopic, and robotic MALR had been performed in 227 (44.0%), 235 (45.5%), and 54 (10.5%) patients, respectively. Perioperative complications (ileus, cardiac, and wound complications; readmissions; unplanned procedures) occurred in 19.2% (open, 30.0%; laparoscopic, 8.9%; robotic, 18.5%; P < .001). The median follow-up was 1.59 years (interquartile range, 0.38-4.35 years). For the 488 patients with follow-up data available, 287 (58.8%) had had full relief, 119 (24.4%) had had partial relief, and 82 (16.8%) had derived no benefit from MALR. The 1- and 3-year freedom from treatment failure for the overall cohort was 63.8% (95% confidence interval [CI], 59.0%-68.3%) and 51.9% (95% CI, 46.1%-57.3%), respectively. The factors associated with an increased hazard of treatment failure on multivariable analysis included robotic MALR (hazard ratio [HR], 1.73; 95% CI, 1.16-2.59; P = .007), a history of gastroparesis (HR, 1.83; 95% CI, 1.09-3.09; P = .023), abdominal cancer (HR, 10.3; 95% CI, 3.06-34.6; P < .001), dysphagia and/or odynophagia (HR, 2.44; 95% CI, 1.27-4.69; P = .008), no relief from a celiac plexus block (HR, 2.18; 95% CI, 1.00-4.72; P = .049), and an increasing number of preoperative pain locations (HR, 1.12 per location; 95% CI, 1.00-1.25; P = .042). The factors associated with a lower hazard included increasing age (HR, 0.99 per increasing year; 95% CI, 0.98-1.0; P = .012) and an increasing number of preoperative diagnostic gastrointestinal studies (HR, 0.84 per study; 95% CI, 0.74-0.96; P = .012) Open and laparoscopic MALR resulted in similar long-term freedom from treatment failure. No radiographic parameters were associated with differences in treatment failure. CONCLUSIONS: No difference was found in long-term failure after open vs laparoscopic MALR; however, open release was associated with higher perioperative morbidity. These results support the use of a preoperative celiac plexus block to aid in patient selection. Operative candidates for MALR should be counseled regarding the factors associated with treatment failure and the relatively high overall rate of treatment failure.

Hybrid Functional and Plane Waves based Ab Initio Molecular Dynamics Study of the Aqueous Fe2+ /Fe3+ Redox Reaction.

Kohn-Sham density functional theory and plane wave basis set based ab initio molecular dynamics (AIMD) simulation is a powerful tool for studying complex reactions in solutions, such as electron transfer (ET) reactions involving Fe2+ /Fe3+ ions in water. In most cases, such simulations are performed using density functionals at the level of Generalized Gradient Approximation (GGA). The challenge in modelling ET reactions is the poor quality of GGA functionals in predicting properties of such open-shell systems due to the inevitable self-interaction error (SIE). While hybrid functionals can minimize SIE, standard plane-wave based AIMD at that level of theory is typically 150 times slower than GGA for systems containing ∼100 atoms. Among several approaches reported to speed-up AIMD simulations with hybrid functionals, the noise-stabilized MD (NSMD) procedure, together with the use of localized orbitals to compute the required exchange integrals, is an attractive option. In this work, we demonstrate the application of the NSMD approach for studying the Fe2+ /Fe3+ redox reaction in water. It is shown here that long AIMD trajectories at the level of hybrid density functionals can be obtained using this approach. Redox properties of the aqueous Fe2+ /Fe3+ system computed from these simulations are compared with the available experimental data for validation.

Water Structures Reveal Local Hydrophobicity on the In2O3(111) Surface.

Clean oxide surfaces are generally hydrophilic. Water molecules anchor at undercoordinated surface metal atoms that act as Lewis acid sites, and they are stabilized by H bonds to undercoordinated surface oxygens. The large unit cell of In2O3(111) provides surface atoms in various configurations, which leads to chemical heterogeneity and a local deviation from this general rule. Experiments (TPD, XPS, nc-AFM) agree quantitatively with DFT calculations and show a series of distinct phases. The first three water molecules dissociate at one specific area of the unit cell and desorb above room temperature. The next three adsorb as molecules in the adjacent region. Three more water molecules rearrange this structure and an additional nine pile up above the OH groups. Despite offering undercoordinated In and O sites, the rest of the unit cell is unfavorable for adsorption and remains water-free. The first water layer thus shows ordering into nanoscopic 3D water clusters separated by hydrophobic pockets.

Spatial speech detection for binaural hearing aids using deep phoneme classifiers.

Current hearing aids are limited with respect to speech-specific optimization for spatial sound sources to perform speech enhancement. In this study, we therefore propose an approach for spatial detection of speech based on sound source localization and blind optimization of speech enhancement for binaural hearing aids. We have combined an estimator for the direction of arrival (DOA), featuring high spatial resolution but no specialization to speech, with a measure of speech quality with low spatial resolution obtained after directional filtering. The DOA estimator provides spatial sound source probability in the frontal horizontal plane. The measure of speech quality is based on phoneme representations obtained from a deep neural network, which is part of a hybrid automatic speech recognition (ASR) system. Three ASR-based speech quality measures (ASQM) are explored: entropy, mean temporal distance (M-Measure), matched phoneme (MaP) filtering. We tested the approach in four acoustic scenes with one speaker and either a localized or a diffuse noise source at various signal-to-noise ratios (SNR) in anechoic or reverberant conditions. The effects of incorrect spatial filtering and noise were analyzed. We show that two of the three ASQMs (M-Measure, MaP filtering) are suited to reliably identify the speech target in different conditions. The system is not adapted to the environment and does not require a-priori information about the acoustic scene or a reference signal to estimate the quality of the enhanced speech signal. Nevertheless, our approach performs well in all acoustic scenes tested and varying SNRs and reliably detects incorrect spatial filtering angles.

The time course of adaptation to distorted speech.

When confronted with unfamiliar or novel forms of speech, listeners' word recognition performance is known to improve with exposure, but data are lacking on the fine-grained time course of adaptation. The current study aims to fill this gap by investigating the time course of adaptation to several different types of distorted speech. Keyword scores as a function of sentence position in a block of 30 sentences were measured in response to eight forms of distorted speech. Listeners recognised twice as many words in the final sentence compared to the initial sentence with around half of the gain appearing in the first three sentences, followed by gradual gains over the rest of the block. Rapid adaptation was apparent for most of the eight distortion types tested with differences mainly in the gradual phase. Adaptation to sine-wave speech improved if listeners had heard other types of distortion prior to exposure, but no similar facilitation occurred for the other types of distortion. Rapid adaptation is unlikely to be due to procedural learning since listeners had been familiarised with the task and sentence format through exposure to undistorted speech. The mechanisms that underlie rapid adaptation are currently unclear.

A model of speech recognition for hearing-impaired listeners based on deep learning.

Automatic speech recognition (ASR) has made major progress based on deep machine learning, which motivated the use of deep neural networks (DNNs) as perception models and specifically to predict human speech recognition (HSR). This study investigates if a modeling approach based on a DNN that serves as phoneme classifier [Spille, Ewert, Kollmeier, and Meyer (2018). Comput. Speech Lang. 48, 51-66] can predict HSR for subjects with different degrees of hearing loss when listening to speech embedded in different complex noises. The eight noise signals range from simple stationary noise to a single competing talker and are added to matrix sentences, which are presented to 20 hearing-impaired (HI) listeners (categorized into three groups with different types of age-related hearing loss) to measure their speech recognition threshold (SRT), i.e., the signal-to-noise ratio with 50% word recognition rate. These are compared to responses obtained from the ASR-based model using degraded feature representations that take into account the individual hearing loss of the participants captured by a pure-tone audiogram. Additionally, SRTs obtained from eight normal-hearing (NH) listeners are analyzed. For NH subjects and three groups of HI listeners, the average SRT prediction error is below 2 dB, which is lower than the errors of the baseline models.

Life cycle inventory data generation by process simulation for conventional, feedstock recycling and power-to-X technologies for base chemical production.

The article presents the methodology and applicable data for the generation of life cycle inventory for conventional and alternative processes for base chemical production by process simulation. Addressed base chemicals include lower olefins, BTX aromatics, methanol, ammonia and hydrogen. Assessed processes include conventional chemical production processes from naphtha, LPG, natural gas and heavy fuel oil; feedstock recycling technologies via gasification and pyrolysis of refuse derived fuel; and power-to-X technologies from hydrogen and CO2. Further, process variations with additional hydrogen input are covered. Flowsheet simulation in Aspen Plus is applied to generate datasets with conclusive mass and energy balance under uniform modelling and assessment conditions with available validation data. Process inventory data is generated with no regard to the development stage of the respective technology, but applicable process data with high technology maturity is prioritized for model validation. The generated inventory data can be applied for life cycle assessments. Further, the presented modelling and balancing framework can be applied for inventory data generation of similar processes to ensure comparability in life cycle inventory data.

Improving the scaling and performance of multiple time stepping-based molecular dynamics with hybrid density functionals.

Density functionals at the level of the generalized gradient approximation (GGA) and a plane-wave basis set are widely used today to perform ab initio molecular dynamics (AIMD) simulations. Going up in the ladder of accuracy of density functionals from GGA (second rung) to hybrid density functionals (fourth rung) is much desired pertaining to the accuracy of the latter in describing structure, dynamics, and energetics of molecular and condensed matter systems. On the other hand, hybrid density functional based AIMD simulations are about two orders of magnitude slower than GGA based AIMD for systems containing ~100 atoms using ~100 compute cores. Two methods, namely MTACE and s-MTACE, based on a multiple time step integrator and adaptively compressed exchange operator formalism are able to provide a speed-up of about 7-9 in performing hybrid density functional based AIMD. In this work, we report an implementation of these methods using a task-group based parallelization within the CPMD program package, with the intention to take advantage of the large number of compute cores available on modern high-performance computing platforms. We present here the boost in performance achieved through this algorithm. This work also identifies the computational bottleneck in the s-MTACE method and proposes a way to overcome it.

Tunable Photoswitching in Norbornadiene (NBD)/Quadricyclane (QC) - Fullerene Hybrids.

With respect to molecular switches, initializing the quadricyclane (QC) to norbornadiene (NBD) back-reaction by light is highly desirable. Our previous publication provided a unique solution for this purpose by utilizing covalently bound C60 . In this work, the fundamental processes within these hybrids has been investigated. Variation of the linker unit connecting the NBD/QC moiety with the fullerene core is used as a tool to tune the properties of the resulting hybrids. Utilizing the Prato reaction, two unprecedented NBD/QC - fullerene hybrids having a long-rigid and a short-rigid linker were synthesized. Molecular dynamics simulations revealed that this results in an average QC-C60 distance of up to 14.2 Å. By comparing the NBD-QC switching of these derivatives with the already established one having a flexible linker, valuable mechanistic insights were gained. Most importantly, spatial convergence of the QC moiety and the fullerene core is inevitable for an efficient back-reaction.

Direct assessment of the acidity of individual surface hydroxyls.

The state of deprotonation/protonation of surfaces has far-ranging implications in chemistry, from acid-base catalysis1 and the electrocatalytic and photocatalytic splitting of water2, to the behaviour of minerals3 and biochemistry4. An entity's acidity is described by its proton affinity and its acid dissociation constant pKa (the negative logarithm of the equilibrium constant of the proton transfer reaction in solution). The acidity of individual sites is difficult to assess for solids, compared with molecules. For mineral surfaces, the acidity is estimated by semi-empirical concepts, such as bond-order valence sums5, and increasingly modelled with first-principles molecular dynamics simulations6,7. At present, such predictions cannot be tested-experimental measures, such as the point of zero charge8, integrate over the whole surface or, in some cases, individual crystal facets9. Here we assess the acidity of individual hydroxyl groups on In2O3(111)-a model oxide with four different types of surface oxygen atom. We probe the strength of their hydrogen bonds with the tip of a non-contact atomic force microscope and find quantitative agreement with density functional theory calculations. By relating the results to known proton affinities of gas-phase molecules, we determine the proton affinity of the different surface sites of In2O3 with atomic precision. Measurements on hydroxylated titanium dioxide and zirconium oxide extend our method to other oxides.

Adsorption of sulfur mustard on clean and water-saturated ZnO(101¯0): Structural diversity from first-principles calculations.

We investigate the adsorption of a chemical warfare agent, namely sulfur mustard (SM), on clean and water-saturated ZnO(101¯0) surfaces using density functional theory calculations to understand the first step of its efficient neutralization to less toxic chemical compounds. We determine the relative stability of various SM conformers adsorbed at different sites on both ZnO surfaces. The unique hydrogen bonding patterns obtained for the idealized clean and the more realistic water-saturated ZnO surface are analyzed and their influence on the stability of the SM@ZnO structures is demonstrated. We find that absolute values of the calculated binding and interaction energies are significantly higher for the clean than for the water-saturated ZnO surface due to the formation of Cl⋯Zn and S⋯Zn contacts. The high adsorptive reactivity of the clean ZnO surface is also evident from the strong structural changes of the initial local energy minimum gas-phase conformations of the SM molecules upon adsorption. This phenomenon is not observed for the water-saturated ZnO surface, which has almost no impact on the SM conformation after adsorption, leaving it as it exists in the gas phase. The insights from the results obtained provide a missing piece toward the understanding of the complex mechanism of SM neutralization on ZnO surfaces.

Speech Audiometry at Home: Automated Listening Tests via Smart Speakers With Normal-Hearing and Hearing-Impaired Listeners.

Speech audiometry in noise based on sentence tests is an important diagnostic tool to assess listeners' speech recognition threshold (SRT), i.e., the signal-to-noise ratio corresponding to 50% intelligibility. The clinical standard measurement procedure requires a professional experimenter to record and evaluate the response (expert-conducted speech audiometry). The use of automatic speech recognition enables self-conducted measurements with an easy-to-use speech-based interface. This article compares self-conducted SRT measurements using smart speakers with expert-conducted laboratory measurements. With smart speakers, there is no control over the absolute presentation level, potential errors from the automated response logging, and room acoustics. We investigate the differences between highly controlled measurements in the laboratory and smart speaker-based tests for young normal-hearing (NH) listeners as well as for elderly NH, mildly and moderately hearing-impaired listeners in low, medium, and highly reverberant room acoustics. For the smart speaker setup, we observe an overall bias in the SRT result that depends on the hearing loss. The bias ranges from +0.7 dB for elderly moderately hearing-impaired listeners to +2.2 dB for young NH listeners. The intrasubject standard deviation is close to the clinical standard deviation (0.57/0.69 dB for the young/elderly NH compared with 0.5 dB observed for clinical tests and 0.93/1.09 dB for the mild/moderate hearing-impaired listeners compared with 0.9 dB). For detecting a clinically elevated SRT, the speech-based test achieves an area under the curve value of 0.95 and therefore seems promising for complementing clinical measurements.

Formation of Highly Ordered Molecular Porous 2D Networks from Cyano-Functionalized Porphyrins on Cu(111).

We investigated the adsorption of three related cyano-functionalized tetraphenyl porphyrin derivatives on Cu(111) by scanning tunneling microscopy (STM) in ultra-high vacuum (UHV) with the goal to identify the role of the cyano group and the central Cu atom for the intermolecular and supramolecular arrangement. The porphyrin derivatives studied were Cu-TCNPP, Cu-cisDCNPP, and 2H-cisDCNPP, that is, Cu-5,10,15,20-tetrakis-(p-cyano)-phenylporphyrin, Cu-meso-cis-di(p-cyano)-phenylporphyrin and 2H-meso-cis-di(p-cyano)-phenylporphyrin, respectively. Starting from different structures obtained after deposition at room temperature, all three molecules form the same long-range ordered hexagonal honeycomb-type structure with triangular pores and three molecules per unit cell. For the metal-free 2H-cisDCNPP, this occurs only after self-metalation upon heating. The structure-forming elements are pores with a distance of 3.1 nm, formed by triangles of porphyrins fused together by cyano-Cu-cyano interactions with Cu adatoms. This finding leads us to suggest that two cyano-phenyl groups in the "cis" position is the minimum prerequisite to form a highly ordered 2D porous molecular pattern. The experimental findings are supported by detailed density functional theory calculations to analyze the driving forces that lead to the formation of the porous hexagonal honeycomb-type structure.

Dynamic Covalent Formation of Concave Disulfide Macrocycles Mechanically Interlocked with Single-Walled Carbon Nanotubes.

The formation of discrete macrocycles wrapped around single-walled carbon nanotubes (SWCNTs) has recently emerged as an appealing strategy to functionalize these carbon nanomaterials and modify their properties. Here, we demonstrate that the reversible disulfide exchange reaction, which proceeds under mild conditions, can install relatively large amounts of mechanically interlocked disulfide macrocycles on the one-dimensional nanotubes. Size-selective functionalization of a mixture of SWCNTs of different diameters were observed, presumably arising from error correction and the presence of relatively rigid, curved π-systems in the key building blocks. A combination of UV/Vis/NIR, Raman, photoluminescence excitation, and transient absorption spectroscopy indicated that the small (6,4)-SWCNTs were predominantly functionalized by the small macrocycles 12 , whereas the larger (6,5)-SWCNTs were an ideal match for the larger macrocycles 22 . This size selectivity, which was rationalized computationally, could prove useful for the purification of nanotube mixtures, since the disulfide macrocycles can be removed quantitatively under mild reductive conditions.