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Metal organic frameworks (MOFs) are nanoporous materials with high surface-to-volume ratio that have potential applications as gas sorbents. Sample quality is, however, often compromised and it is unclear how defects and surface contaminants affect the spectral properties of single MOF crystals. Raman micro-spectroscopy is a powerful tool for characterizing MOFs, yet spatial spectral heterogeneity distributions of single MOF crystals have not been reported so far. In this work, we use Raman micro-spectroscopy to characterize spatially isolated, single crystals of the MOF species HKUST-1. In a first step, we validate HKUST-1's Raman spectrum based on DFT simulations and we identify a previously unreported vibrational feature. In a second step, we acquire diffraction-limited, mode-selective Raman images of a single HKUST-1 crystals that reveal how the spectral variations are distributed across the crystal surface. In a third step, we statistically analyze the measured spectral peak positions and line widths for quantifying the variability occurring within the same crystal as well as between different crystals taken from the same batch. Finally, we explore how multivariate data analysis can aid feature identification in Raman images of single MOF crystals. For enabling validation and reuse, we have made the spectroscopic data and simulation code publicly available.

Automated knowledge extraction from scientific literature can potentially accelerate materials discovery. We have investigated an approach for extracting synthesis protocols for reticular materials from scientific literature using large language models (LLMs). To that end, we introduce a Knowledge Extraction Pipeline (KEP) that automatizes LLM-assisted paragraph classification and information extraction. By applying prompt engineering with in-context learning (ICL) to a set of open-source LLMs, we demonstrate that LLMs can retrieve chemical information from PDF documents, without the need for fine-tuning or training and at a reduced risk of hallucination. By comparing the performance of five open-source families of LLMs in both paragraph classification and information extraction tasks, we observe excellent model performance even if only few example paragraphs are included in the ICL prompts. The results show the potential of the KEP approach for reducing human annotations and data curation efforts in automated scientific knowledge extraction., Comment: 16 pages

Predicting the geometrical evolution of the pore space in geological formations due to fluid-solid interactions has applications in reservoir engineering, oil recovery, and geological storage of carbon dioxide. However, modeling frameworks that combine fluid flow with physical and chemical processes at a rock's pore scale are scarce. Here, we report a method for modeling a rock's pore space as a network of connected capillaries and to simulate the capillary diameter modifications caused by reactive flow processes. Specifically, we model mineral erosion, deposition, dissolution, and precipitation processes by solving the transport equations iteratively, computing diameter changes within each capillary of the network simultaneously. Our automated modeling framework enables simulations on digital rock samples as large as (1.125mm)$^3$ with 125$\times 10^6$ voxels within seconds of CPU time per iteration. As an application of the computational method, we have simulated brine injection and calcium carbonate precipitation in sandstone. For quantitatively comparing simulation results obtained with models predicting either a constant or a flow-rate dependent precipitation, we track the time-dependent capillary diameter distribution as well as the permeability of the connected pore space. For validation and reuse, we have made the automated simulation workflow, the reactive flow model library, and the digital rock samples available in public repositories., Comment: Main article: 12 pages, 1 equation, 2 tables and 4 figures. Supplementary Information: 6 pages, 11 equations and 3 figures. Includes DOI for accompanying digital rock data, URL of github repository for flow simulator code, and of accompanying processing python code for automation of the scientific workflows

Direct air capture (DAC) of carbon dioxide is a promising method for mitigating climate change. Solid sorbents, such as metal-organic frameworks, are currently being tested for DAC application. However, their potential for deployment at scale has not been fully realized. The computational discovery of solid sorbents is challenging, given the vast chemical search space and the DAC requirements for molecular selectivity. Quantum computing can potentially accelerate the discovery of solid sorbents for DAC by predicting molecular binding energies. In this work, we explore simulation methods and algorithms for predicting gas adsorption in metal-organic frameworks using a quantum computer. Specifically, we simulate the potential energy surfaces of CO2, N2, and H2O molecules at the Mg+2 metal center that represents the binding sites of typical metal-organic frameworks. We apply the qubit-ADAPT-VQE technique to run simulations on both classical computing and quantum computing hardware, and achieve reasonable accuracy while maintaining hardware efficiency., Comment: 19 pages, 6 figures, 4 tables

Automated molecular simulations are used extensively for predicting material properties. Typically, these simulations exhibit two regimes: a dynamic equilibration part, followed by a steady state. For extracting observable properties, the simulations must first reach a steady state so that thermodynamic averages can be taken. However, as equilibration depends on simulation conditions, predicting the optimal number of simulation steps a priori is impossible. Here, we demonstrate the application of the Marginal Standard Error Rule (MSER) for automatically identifying the optimal truncation point in Grand Canonical Monte Carlo (GCMC) simulations. This novel automatic procedure determines the point in which steady state is reached, ensuring that figures-of-merits are extracted in an objective, accurate, and reproducible fashion. In the case of GCMC simulations of gas adsorption in metal-organic frameworks, we find that this methodology reduces the computational cost by up to 90%. As MSER statistics are independent of the simulation method that creates the data, this library is, in principle, applicable to any time series analysis in which equilibration truncation is required. The open-source Python implementation of our method, pyMSER, is publicly available for reuse and validation at https://github.com/IBM/pymser., Comment: 22 pages, 19 figures

Carbon dioxide (CO2) trapping in capillary networks of reservoir rocks is a pathway to long-term geological storage. At pore scale, the CO2 trapping potential depends on injection pressure, temperature, and the rock's interaction with the surrounding fluids. Modeling this interaction requires adequate representations of both capillary volume and surface. For the lack of scalable representations, however, the prediction of a rock's CO2 storage potential has been challenging. Here, we report how to represent a rock's pore space by statistically sampled capillary networks (ssCN) that preserve morphological rock characteristics. We have used the ssCN method to simulate CO2 drainage within a representative sandstone sample at reservoir pressures and temperatures, exploring intermediate- and CO2-wet conditions. This wetting regime is often neglected, despite evidence of plausibility. By raising pressure and temperature we observe increasing CO2 penetration within the capillary network. For contact angles approaching 90 degrees, the CO2 saturation exhibits a pronounced maximum reaching 80 percent of the accessible pore volume. This is about twice as high as the saturation values reported previously. For enabling validation of our results and a broader application of our methodology, we have made available the rock tomography data, the digital rock computational workflows, and the ssCN models used in this study., Comment: Main article: 16 pages and 3 figures. Supplementary Information: 17 pages, 3 equations, 13 figures and 3 tables. Includes DOI for accompanying digital rock data, URL of github repository for flow simulator code, and of accompanying digital rock processing python code and automation of the scientific workflows

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions., Comment: 65 pages, 15 figures; comments welcome

Polymers are candidate materials for a wide range of sustainability applications such as carbon capture and energy storage. However, computational polymer discovery lacks automated analysis of reaction pathways and stability assessment through retro-synthesis. Here, we report the first extension of transformer-based language models to polymerization reactions for both forward and retrosynthesis tasks. To that end, we have curated a polymerization dataset for vinyl polymers covering reactions and retrosynthesis for representative homo-polymers and co-polymers. Overall, we obtain a forward model Top-4 accuracy of 80% and a backward model Top-4 accuracy of 60%. We further analyze the model performance with representative polymerization and retro-synthesis examples and evaluate its prediction quality from a materials science perspective., Comment: 23 pages, 8 figures

Artificial intelligence holds promise to improve materials discovery. GFlowNets are an emerging deep learning algorithm with many applications in AI-assisted discovery. By using GFlowNets, we generate porous reticular materials, such as metal organic frameworks and covalent organic frameworks, for applications in carbon dioxide capture. We introduce a new Python package (matgfn) to train and sample GFlowNets. We use matgfn to generate the matgfn-rm dataset of novel and diverse reticular materials with gravimetric surface area above 5000 m$^2$/g. We calculate single- and two-component gas adsorption isotherms for the top-100 candidates in matgfn-rm. These candidates are novel compared to the state-of-art ARC-MOF dataset and rank in the 90th percentile in terms of working capacity compared to the CoRE2019 dataset. We discover 15 materials outperforming all materials in CoRE2019.

We report a dataset containing full-scale, 3D images of rock plugs augmented by petrophysical lab characterization data for application in digital rock and capillary network analysis. Specifically, we have acquired microscopically resolved tomography datasets of 18 cylindrical sandstone and carbonate rock samples having lengths of 25.4 mm and diameters of 9.5 mm, respectively. Based on the micro-tomography data, we have computed porosity-values for each imaged rock sample. For validating the computed porosity values with a complementary lab method, we have measured porosity for each rock sample by using standard petrophysical characterization techniques. Overall, the tomography-based porosity values agree with the measurement results obtained from the lab, with values ranging from 8% to 30%. In addition, we provide for each rock sample the experimental permeabilities, with values ranging from 0.4 mD to above 5D. This dataset will be essential for establishing, benchmarking, and referencing the relation between porosity and permeability of reservoir rock at pore scale., Comment: 12 pages, 9 figures, 4 tables

Grand Canonical Monte Carlo is an important method for performing molecular-level simulations and assisting the study and development of nanoporous materials for gas capture application. These simulations are based on the use of force fields and partial charges to model the interaction between the adsorbent molecules and the solid framework. The choice of the force field parameters and partial charges can significantly impact the results obtained, however, there are very few databases available to support a comprehensive impact evaluation. Here, we present a database of simulations of CO2 and N2 adsorption isotherms on 726 metal-organic frameworks taken from the CoRE MOF 2014 database. We performed simulations with two force fields (UFF and DREIDING), four partial charge schemes (no charge, Qeq, EQeq, and DDEC), and three temperatures (273, 298, 323 K). The resulting isotherms compose the Charge-dependent, Reproducible, Accessible, Forcefield-dependent, and Temperature-dependent Exploratory Database (CRAFTED) of adsorption isotherms., Comment: 18 pages, 6 figures, dataset available at 10.5281/zenodo.7106173

We investigate the graph-based convolutional neural network approach for predicting and ranking gas adsorption properties of crystalline Metal-Organic Framework (MOF) adsorbents for application in post-combustion capture of $\textrm{CO}_2$. Our model is based solely on standard structural input files containing atomistic descriptions of the adsorbent material candidates. We construct novel methodological extensions to match the prediction accuracy of classical machine learning models that were built with hundreds of features at much higher computational cost. Our approach can be more broadly applied to optimize gas capture processes at industrial scale., Comment: AAAI Conference on Artificial Intelligence (2022)

For optimizing production yield while limiting negative environmental impact, sustainable agriculture benefits greatly from real-time, on-the-spot analysis of soil at low cost. Colorimetric paper sensors are ideal candidates for cheap and rapid chemical spot testing. However, their field application requires previously unattained paper sensor reliability and automated readout and analysis by means of integrated mobile communication, artificial intelligence, and cloud computing technologies. Here, we report such a mobile chemical analysis system based on colorimetric paper sensors that operates under tropical field conditions. By mapping topsoil pH in a field with an area of 9 hectares, we have benchmarked the mobile system against precision agriculture standards following a protocol with reference analysis of compound soil samples. As compared with routine lab analysis, our mobile soil analysis system has correctly classified soil pH in 97% of cases while reducing the analysis turnaround time from days to minutes. Moreover, by performing on-the-spot analyses of individual compound sub-samples in the field, we have achieved a 9-fold increase of spatial resolution that reveals pH-variations not detectable in compound mapping mode. Our mobile system can be extended to perform multi-parameter chemical tests of soil nutrients for applications in environmental monitoring at marginal manufacturing cost., Comment: Main article: 23 pages and 4 figures. Supplementary Information: 12 pages and 12 figures. Includes DOI for accompanying data and images datasets and URL for github repository of accompanying python code

The generation of molecules with Artificial Intelligence (AI) is poised to revolutionize materials discovery. Potential applications range from development of potent drugs to efficient carbon capture and separation technologies. However, existing computational frameworks lack automated training data creation and physical performance validation at meso-scale where complex properties of amorphous materials emerge. The methodological gaps have so far limited AI design to small-molecule applications. Here, we report the first automated discovery of complex materials through inverse molecular design which is informed by meso-scale target features and process figures-of-merit. We have entered the new discovery regime by computationally generating and validating hundreds of polymer candidates designed for application in post-combustion carbon dioxide filtration. Specifically, we have validated each discovery step, from training dataset creation, via graph-based generative design of optimized monomer units, to molecular dynamics simulation of gas permeation through the polymer membranes. For the latter, we have devised a Representative Elementary Volume (REV) enabling permeability simulations at about 1,000x the volume of an individual, AI-generated monomer, obtaining quantitative agreement. The discovery-to-validation time per polymer candidate is on the order of 100 hours in a standard computing environment, offering a computational screening alternative prior to lab validation., Comment: 29 pages, 12 figures and 1 table

Permeability is the key parameter for quantifying fluid flow in porous rocks. Knowledge of the spatial distribution of the connected pore space allows, in principle, to predict the permeability of a rock sample. However, limitations in feature resolution and approximations at microscopic scales have so far precluded systematic upscaling of permeability predictions. Here, we report fluid flow simulations in capillary network representations designed to overcome such limitations. Performed with an unprecedented level of accuracy in geometric approximation at microscale, the pore scale flow simulations predict experimental permeabilities measured at lab scale in the same rock sample without the need for calibration or correction. By applying the method to a broader class of representative geological samples, with permeability values covering two orders of magnitude, we obtain scaling relationships that reveal how mesoscale permeability emerges from microscopic capillary diameter and fluid velocity distributions., Comment: Main article: 11 pages and 4 figures. Supplementary Information: 6 pages and 4 figures. Version 2 includes DOI for microCT dataset

We performed mesoscale simulations in order to investigate the effects that additives, such as surfactants and polymers, have in the oil displacement process by water and brine injection. A Many-Body Dissipative Particle Dynamics (MDPD) model was parameterized in order to reproduce physical properties obtained either by experiments or Classical Molecular Dynamics (MD). The MDPD model was then employed to simulate the displacement of n-dodecane by water and brine, with surfactant concentrations ranging from 1 wt. % to 10 wt. % SDS and polymer concentration of 0.5 wt. % HPAM. We observed that while the additives may enter the capillary without clogging, specific combinations of surfactant concentrations and injection rates may lead to poor oil displacement. This result can be understood in terms of the balance between water-wall and (surface-mediated) water-oil interactions. As a consequence, our results show an effective reversal of the wettability character, from water-wet to oil-wet, as a function of the interface speed. We conclude by summarizing the implications such results have on the fluid-fluid displacements involving additives.

Controlled placement of nanomaterials at predefined locations with nanoscale precision remains among the most challenging problems that inhibit their large-scale integration in the field of semiconductor process technology. Methods based on surface functionalization have a drawback where undesired chemical modifications can occur and deteriorate the deposited material. The application of electric-field assisted placement techniques eliminates the element of chemical treatment; however, it requires an incorporation of conductive placement electrodes that limit the performance, scaling, and density of integrated electronic devices. Here, we report a method for electric-field assisted placement of solution-processed nanomaterials by using large-scale graphene layers featuring nanoscale deposition sites. The structured graphene layers are prepared via either transfer or synthesis on standard substrates, then are removed without residue once nanomaterial deposition is completed, yielding material assemblies with nanoscale resolution that cover surface areas larger than 1mm2. In order to demonstrate the broad applicability, we have assembled representative zero-, one-, and two-dimensional semiconductors at predefined substrate locations and integrated them into nanoelectronic devices. This graphene-based placement technique affords nanoscale resolution at wafer scale, and could enable mass manufacturing of nanoelectronics and optoelectronics involving a wide range of nanomaterials prepared via solution-based approaches., Comment: 17 pages, 5 figures

Electric field-controlled, two-dimensional (2D) exciton dynamics in transition metal dichalcogenide monolayers is a current research focus in condensed matter physics. We have experimentally investigated the spectral and temporal properties of the A-exciton in a molybdenum diselenide (MoSe2) monolayer under controlled variation of a vertical, electric dc field at room temperature. By using steady-state and time-resolved photoluminescence spectroscopies, we have observed dc field-induced spectral shifts and linewidth broadenings that are consistent with the shortening of the exciton's non-radiative lifetime due to field-induced dissociation. We discuss the implications of the results for future developments in nanoscale metrology and exploratory, optoelectronics technologies based on layered, 2D semiconductors., Comment: 23 pages, 5 figures

Wettability is the affinity of a liquid for a solid surface. For energetic reasons, macroscopic drops of liquid are nearly spherical away from interfaces with solids, and any local deformations due to molecular-scale surface interactions are negligible. Studies of wetting phenomena, therefore, typically assume that a liquid on a surface adopts the shape of a spherical cap. The degree of wettability is then captured by the contact angle where the liquid-vapor interface meets the solid-liquid interface. As droplet volumes shrink to the scale of attoliters, however, surface interactions become significant, and droplets gradually assume distorted shapes that no longer comply with our conventional, macroscopic conception of a drop. In this regime, the contact angle becomes ambiguous, and it is unclear how to parametrize a liquid's affinity for a surface. A scalable metric for quantifying wettability is needed, especially given the emergence of technologies exploiting liquid-solid interactions at the nanoscale. Here we combine nanoscale experiments with molecular-level simulation to study the breakdown of spherical droplet shapes at small length scales. We demonstrate how measured droplet topographies increasingly reveal non-spherical features as volumes shrink, in agreement with theoretical predictions. Ultimately, the nanoscale liquid flattens out to form layer-like molecular assemblies, instead of droplets, at the solid surface. For the lack of a consistent contact angle at small scales, we introduce a droplet's adsorption energy density as a new metric for a liquid's affinity for a surface. We discover that extrapolating the macroscopic idealization of a drop to the nanoscale, though it does not geometrically resemble a realistic droplet, can nonetheless recover its adsorption energy if line tension is properly included.

We report operating temperatures and heating coefficients measured in a multi-layer black phosphorus device as a function of injected electrical power. By combining micro-Raman spectroscopy and electrical transport measurements, we have observed a linear temperature increase up to 600K at a power dissipation rate of 0.896K\mu m^3/mW. By further increasing the bias voltage, we determined the threshold power and temperature for electrical breakdown and analyzed the fracture in the black phosphorus layer that caused the device failure by means of scanning electron microscopy and atomic force microscopy. The results will benefit the research and development of electronics and optoelectronics based on novel two-dimensional materials., Comment: 12 pages, 4 figures

We study the origin of photocurrent generated in doped multilayer BP photo-transistors, and find that it is dominated by thermally driven thermoelectric and bolometric processes. The experimentally observed photocurrent polarities are consistent with photo-thermal processes. The photo-thermoelectric current can be generated up to a $\mu$m away from the contacts, indicating a long thermal decay length. With an applied source-drain bias, a photo-bolometric current is generated across the whole device, overwhelming the photo-thermoelectric contribution at a moderate bias. The photo-responsivity in the multilayer BP device is two orders of magnitude larger than that observed in graphene.

Black phosphorus is a layered semiconductor that is intensely researched in view of applications in optoelectronics. In this Letter, we investigate a multi-layer black phosphorus photo-detector that is capable of acquiring high-contrast (V>0.9) images both in the visible ({\lambda}_{VIS}=532nm) as well as in the infrared ({\lambda}_{IR}=1550nm) spectral regime. In a first step, by using photocurrent microscopy, we map the active area of the device and we characterize responsivity and gain. In a second step, by deploying the black phosphorus device as a point-like detector in a confocal microsope setup, we acquire diffraction-limited optical images with sub-micron resolution. The results demonstrate the usefulness of black phosphorus as an optoelectronic material for hyperspectral imaging applications., Comment: 15 pages, 6 figures

We report the radio-frequency performance of carbon nanotube array transistors that have been realized through the aligned assembly of highly separated, semiconducting carbon nanotubes on a fully scalable device platform. At a gate length of 100 nm, we observe output current saturation and obtain as-measured, extrinsic current gain and power gain cut-off frequencies, respectively, of 7 GHz and 15 GHz. While the extrinsic current gain is comparable to the state-of-the-art the extrinsic power gain is improved. The de-embedded, intrinsic current gain and power gain cut-off frequencies of 153 GHz and 30 GHz are the highest values experimentally achieved to date. We analyze the consistency of DC and AC performance parameters and discuss the requirements for future applications of carbon nanotube array transistors in high-frequency electronics., Comment: 15 pages, 4 figures + Supplementary Information

Graphene has extraordinary electronic and optical properties and holds great promise for applications in photonics and optoelectronics. Demonstrations including high-speed photodetectors, optical modulators, plasmonic devices, and ultrafast lasers have now been reported. More advanced device concepts would involve photonic elements such as cavities to control light-matter interaction in graphene. Here we report the first monolithic integration of a graphene transistor and a planar, optical microcavity. We find that the microcavity-induced optical confinement controls the efficiency and spectral selection of photocurrent generation in the integrated graphene device. A twenty-fold enhancement of photocurrent is demonstrated. The optical cavity also determines the spectral properties of the electrically excited thermal radiation of graphene. Most interestingly, we find that the cavity confinement modifies the electrical transport characteristics of the integrated graphene transistor. Our experimental approach opens up a route towards cavity-quantum electrodynamics on the nanometre scale with graphene as a current-carrying intra-cavity medium of atomic thickness., Comment: 21 pages, 4 figures + Supplementary Information

Carbon electronics based on carbon nanotube array field-effect transistors (AFETs) and 2-dimensional graphene field-effect transistors (GFETs) have recently attracted significant attention for potential RF applications. Here, we explore the ultimate RF performance potential for these two unique devices using semi-classical ballistic transport simulations. It is shown that the intrinsic current-gain and power-gain cutoff frequencies (fT and fMAX) above 1 THz should be possible in both AFETs and GFETs. Thus, both devices could deliver higher cut-off frequencies than traditional semiconductors such as Si and III-V's. In the case of AFETs, we show that their RF operation is not sensitive to the diameter variation of semiconducting tubes and the presence of metallic tubes in the channel. The ultimate fT and fMAX values in AFETs are observed to be higher than that in GFETs. The optimum device biasing conditions for AFETs require smaller biasing currents, and thus, lower power dissipation compared to GFETs. The degradation in high-frequency performance in the presence of external parasitics is also seen to be lower in AFETs compared to GFETs., Comment: 12 pages, 11 figures, 2 tables

Electrically-driven light emission from carbon nanotubes could be exploited in nano-scale lasers and single-photon sources, and has therefore been the focus of much research. However, to date, high electric fields and currents have been either required for electroluminescence, or have been an undesired side effect, leading to high power requirements and low efficiencies. In addition, electroluminescent linewidths have been broad enough to obscure the contributions of individual optical transitions. Here, we report electrically-induced light emission from individual carbon nanotube p-n diodes. A new level of control over electrical carrier injection is achieved, reducing power dissipation by a factor of up to 1000, and resulting in zero threshold current, negligible self-heating, and high carrier-to- photon conversion efficiencies. Moreover, the electroluminescent spectra are significantly narrower (ca. 35 meV) than in previous studies, allowing the identification of emission from free and localized excitons.

Graphene is a 2-dimensional material with high carrier mobility and thermal conductivity, suitable for high-speed electronics. Conduction and valence bands touch at the Dirac point. The absorptivity of single-layer graphene is 2.3%, nearly independent of wavelength. Here we investigate the thermal radiation from biased graphene transistors. We find that the emission spectrum of single-layer graphene follows that of a grey body with constant emissivity (1.6 \pm 0.8)%. Most importantly, we can extract the temperature distribution in the ambipolar graphene channel, as confirmed by Stokes/anti-Stokes measurements. The biased graphene exhibits a temperature maximum whose location can be controlled by the gate voltage. We show that this peak in temperature reveals the spatial location of the minimum in carrier density, i.e. the Dirac point., Comment: Accepted in principle at Nature Nanotechnology

We measure the temperature distribution in a biased single-layer graphene transistor using Raman scattering microscopy of the 2D-phonon band. Peak operating temperatures of 1050 K are reached in the middle of the graphene sheet at 210 KW cm^(-2) of dissipated electric power. The metallic contacts act as heat sinks, but not in a dominant fashion. To explain the observed temperature profile and heating rate, we have to include heat-flow from the graphene to the gate oxide underneath, especially at elevated temperatures, where the graphene thermal conductivity is lowered due to umklapp scattering. Velocity saturation due to phonons with about 50 meV energy is inferred from the measured charge density via shifts in the Raman G-phonon band, suggesting that remote scattering (through field coupling) by substrate polar surface phonons increases the energy transfer to the substrate and at the same time limits the high-bias electronic conduction of graphene., Comment: The pdf-file contains the main manuscript (19 pages, 3 figures) and the supplement (5 pages, 4 figures)

We study the optical properties of a single, semiconducting single-walled carbon nanotube (CNT) that is partially suspended across a trench and partially supported by a SiO2-substrate. By tuning the laser excitation energy across the E33 excitonic resonance of the suspended CNT segment, the scattering intensities of the principal Raman transitions, the radial breathing mode (RBM), the G-mode and the D-mode show strong resonance enhancement of up to three orders of magnitude. In the supported part of the CNT, despite a loss of Raman scattering intensity of up to two orders of magnitude, we recover the E33 excitonic resonance suffering a substrate-induced red shift of 50 meV. The peak intensity ratio between G-band and D-band is highly sensitive to the presence of the substrate and varies by one order of magnitude, demonstrating the much higher defect density in the supported CNT segments. By comparing the E33 resonance spectra measured by Raman excitation spectroscopy and photoluminescence (PL) excitation spectroscopy in the suspended CNT segment, we observe that the peak energy in the PL excitation spectrum is red-shifted by 40 meV. This shift is associated with the energy difference between the localized exciton dominating the PL excitation spectrum and the free exciton giving rise to the Raman excitation spectrum. High-resolution Raman spectra reveal substrate-induced symmetry breaking, as evidenced by the appearance of additional peaks in the strongly broadened Raman G band. Laser-induced line shifts of RBM and G band measured on the suspended CNT segment are both linear as a function of the laser excitation power. Stokes/anti-Stokes measurements, however, reveal an increase of the G phonon population while the RBM phonon population is rather independent of the laser excitation power., Comment: Revised manuscript, 20 pages, 8 figures