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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

An analytic theory for the degree of Arctic Amplification

Arctic Amplification (AA), the amplified surface warming in the Arctic relative to the global mean, is a robust and impactful feature of climate change. While the basic physical picture of AA has been depicted, a clear understanding of how the degree of AA is determined has not been established. Here, by deciphering the intricate role of atmospheric heat transport (AHT), we build a two-box energy-balance model of AA and derive that the degree of AA is a simple nonlinear function of the Arctic and global feedbacks, the meridional heterogeneity in radiative forcing, and the partial sensitivities of AHT to global mean warming and meridional warming gradient. The formula captures the varying degree of AA in individual climate models and attributes the variation to specific physical factors. It further conveys a concise picture of how essential physics mutually determine the degree of AA and limit the range within 1.5~3.5. Our results articulate AHT as both forcing and feedback to AA, highlighting its partial sensitivities instead of total change as the key parameters for understanding AA. We also find that the effect of lapse rate feedback, a widely-recognized major contributor to AA, is fully offset by the effect of water vapor feedback.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1984

Analytical Models

A system-level design and analysis model was developed. This model was conceived to have several key elements: a solar pond thermodynamic performance model, a power generation subsystem model, and an economic analysis element. The basic approach was to create these elements or modules and refine them on an individual basis yet retain the capability to easily couple them into a full system design model. This building block approach allows for maximum flexibility and substitution of refined descriptions as the technology develops. A general overview of interconnecting these subsystem models is presented. The primary program control element will perform the administrative functions of data input, data output, information storage and transfer, and sequential calling of the subsystem models. From the point of view of the requirements of a system design model, a power conversion subsystem model was developed. The goal of the effort was a preliminary subsystem model compatible with the solar pond subsystem model so that a first order system simulation analysis could be performed.

Source record↗
DOE OSTI2021

C-AAC Occupancy and Transition Out of CMR [Slides]

This in-depth evaluation was performed in order to gather information for a plan and time estimate of C-AAC space cleanout and handover to the CMR facility once programmatic operations are relocated to other facilities. C-AAC space ownership in CMR wings 1, 5, 7, and 9 have been identified down to the Team level (see subsequent slides and other supporting documentation). This plan assumes that C-AAC activities that support Pu sustainment efforts (Wings 1, 5, and 7) are relocated to PF-4 and RLUOB in mid/end FY24 and current projects in Wing 9 are completed by the end of FY24/mid FY25 (cessation of C-AAC programmatic operations in CMR). This plan assumes that no other programmatic activities are initiated in CMR (e.g. MOX fuel rods). Timing and order of cleanout of programmatic laboratory and office spaces in wings 1, 5, and 7 is dependent upon analytical chemistry operations being established and relocated to RLUOB and PF-4. Timing and order of cleanout of programmatic laboratory and office spaces in wing 9 is dependent upon completion of current projects (MR&R, Thermo Fisher drum, etc.). As C-AAC Teams move out of the CMR building and into other facilities, the spaces occupied by those teams are planned to be targeted for in depth cleanout in the order that teams and capabilities are relocated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2017

Advanced Software V&V for Civil Aviation and Autonomy

With the advances in high-computing platform (e.g., advanced graphical processing units or multi-core processors), computationally-intensive software techniques such as the ones used in artificial intelligence or formal methods have provided us with an opportunity to further increase safety in the aviation industry. Some of these techniques have facilitated building safety at design time, like in aircraft engines or software verification and validation, and others can introduce safety benefits during operations as long as we adapt our processes. In this talk, I will present how NASA is taking advantage of these new software techniques to build in safety at design time through advanced software verification and validation, which can be applied earlier and earlier in the design life cycle and thus help also reduce the cost of aviation assurance. I will then show how run-time techniques (such as runtime assurance or data analytics) offer us a chance to catch even more complex problems, even in the face of changing and unpredictable environments. These new techniques will be extremely useful as our aviation systems become more complex and more autonomous.

software assurance↗
DOE OSTI2014

Imaging Fluorescence-Correlation Spectroscopy for Measuring Fast Surface Diffusion at Liquid/Solid Interfaces

The development of techniques to probe interfacial molecular transport is important for understanding and optimizing surface-based analytical methods including surface-enhanced spectroscopies, biological assays, and chemical separations. Single-molecule-fluorescence imaging and tracking has been used to measure lateral diffusion rates of fluorescent molecules at surfaces, but the technique is limited to the study of slower diffusion, where molecules must remain relatively stationary during acquisition of an image in order to build up sufficient intensity in a spot to detect and localize the molecule. Although faster time resolution can be achieved by fluorescence-correlation spectroscopy (FCS), where intensity fluctuations in a small spot are related to the motions of molecules on the surface, long-lived adsorption events arising from surface inhomogeneity can overwhelm the correlation measurement and mask the surface diffusion of the moving population. Here, we exploit a combination of these two techniques, imaging-FCS, for measurement of fast interfacial transport at a model chromatographic surface. This is accomplished by rapid imaging of the surface using an electron-multiplied-charged-coupled-device (CCD) camera, while limiting the acquisition to a small area on the camera to allow fast framing rates. The total intensity from the sampled region is autocorrelated to determine surface diffusion rates of molecules with millisecond time resolution. Here, the technique allows electronic control over the acquisition region, which can be used to avoid strong adsorption sites and thus minimize their contribution to the measured autocorrelation decay and to vary the acquisition area to resolve surface diffusion from adsorption and desorption kinetics. As proof of concept, imaging-FCS was used to measure surface diffusion rates, interfacial populations, and adsorption–desorption rates of 1,1'-dioctadecyl-3,3,3'3'-tetramethylindocarbocyanine (DiI) on planar C18- and C1-modified surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Selective laser sintering of MA956 oxide dispersion strengthened steel

Oxide Dispersion Strengthened (ODS) steels’ qualities of radiation damage resistance and high strength at high temperature make them promising nuclear structural materials. However, the dispersed yttria that gives ODS steel its beneficial qualities are generally compromised during joining processes, making fabrication difficult and expensive. The selective laser sintering process offers a potential path through this barrier by which net-shape parts can feasibly be built via additive manufacturing without fully melting the structure. By rastering a 400 W laser over a MA956 ODS steel powder bed, here we additively built parts with varying build conditions. Although density was achieved to within 97% of the wrought MA956, ultimate tensile strengths achieved only 65% of the wrought strength. Spectroscopy analysis points to the agglomeration of the yttria nano-particles as a possible explanation for the loss in strength. Further study might benefit from exploration of other parameters such as thinner powder build layers which would require less energy input to achieve sintering while minimizing time above the melting temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Function of Tetrahedral ZnS 3 O Building Blocks in the Formation of SrZn 2 S 2 O: A Phase Matchable Polar Oxysulfide with a Large Second Harmonic Generation Response

Single crystals of a new zinc oxysulfide SrZn 2 S2O were grown in a eutectic KF-KCl flux and the structure was determined by single-crystal X-ray diffraction. SrZn 2 S 2 O crystallizes in the non-centrosymmetric polar space group Pmn2 1 with lattice parameters of a = 3.87440(10) Å, b = 9.9847(3) Å, and c = 6.0916(2) Å. In the crystal structure, close-packed corrugated double layers of ZnS3O tetrahedra, which are derived from the wurtzite structure, are vertically separated by Sr 2+ ions. In addition, the O/S anion ordered arrangement in each close-packed layer yields two distinct orientations of the Zn-centered tetrahedra. The crystals of SrZn 2 S 2 O are colorless and transparent and the oxysulfide has a band gap of 3.86 eV, based on UV-vis-NIR diffuse reflectance measurements. Thermogravimetric measurements showed that SrZn 2 S 2 O is stable up to 650 °C in O 2 gas atmosphere. First-principle calculations indicate that the valence band maximum is mainly composed of O-2p and S-3p states, whereas the conduction band minimum is derived from Zn-4s, Zn-4p, and Sr-4d states. The calculated band dispersion reveals a direct band gap corresponding to a transition between S-3p and Zn-4s energy levels. Second harmonic generation (SHG) measurements determined that SrZn 2 S 2 O is phase matchable with twice the SHG intensity of potassium dihydrogen phosphate (KDP) in contrast to CaZnSO with similar ZnS 3 O building units, which exhibits non-phase matching behavior.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Exploiting Saturation Regimes and Surface Effects to Tune Composite Design: Single Platelet Nanocomposites of Peptoid Nanosheets and CaCO 3

Mineral-polymer composites found in nature exhibit exceptional structural properties essential to their function, and transferring these attributes to the synthetic design of functional materials holds promise across various sectors. Biomimetic fabrication of nanocomposites introduces new pathways for advanced material design and explores biomineralization strategies. This study presents a novel approach for producing single platelet nanocomposites composed of CaCO 3 and biomimetic peptoid (N-substituted glycines) polymers, akin to the bricks found in the brick-and-mortar structure of nacre, the inner layer of certain mollusc shells. The significant aspect of the proposed strategy is the use of organic peptoid nanosheets as the scaffolds for brick formation, along with their controlled mineralization in solution. Here, we employ the B28 peptoid nanosheet as a scaffold, which readily forms free-floating zwitterionic bilayers in aqueous solution. The peptoid nanosheets were mineralized under consistent initial conditions (σ calcite = 1.2, pH 9.00), with variations in mixing conditions and supersaturation profiles over time aimed at controlling the final product. Nanosheets were mineralized in both feedback control experiments, where supersaturation was continuously replenished by titrant addition and in batch experiments without a feedback loop. Complete coverage of the nanosheet surface by amorphous calcium carbonate was achieved under specific conditions with feedback control mineralization, whereas vaterite was the primary CaCO 3 phase observed after batch experiments. Thermodynamic calculations suggest that time-dependent supersaturation profiles as well as the spatial distribution of supersaturation are effective controls for tuning the mineralization extent and product. We anticipate that the control strategies outlined in this work can serve as a foundation for the advanced and scalable fabrication of nanocomposites as building blocks for nacre-mimetic and functional materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Brilliant Execution of Smart Labs: Employing Best Practices to Improve Safety and Reduce Energy for Sustainable Labs: Preprint

Laboratory buildings often dominate the energy use on research campuses, consuming three to four times the energy of a typical office building. In most cases, these facilities are defined as “mission critical,” with excessive energy use as simply an unfortunate truth that cannot be avoided without compromising safety. However, case studies indicate cost savings opportunities of up to 40% in laboratories (compared to standard practices) by employing energy efficient design strategies that also improve the safety of lab spaces (Better Buildings 2018). To help stakeholders implement improvement measures, the Smart Labs Toolkit describes a proven approach to optimizing performance of laboratories and critical control environments in new or existing facilities. The Toolkit outlines distinct phases for building a successful Smart Labs program that include: Plan, Assess, Optimize, and Manage. Each phase has specific tasks, training videos, and resources to educate users, support the efforts of key stakeholders, and maximize benefits for the organization in implementing high-performance labs. The Toolkit also includes case studies from the 17 Better Buildings Smart Labs Accelerator partners, sharing lessons learned and supporting the Smart Labs approach to safe, efficient world class science. This paper reviews the technology choices for implementing Smart Labs ventilation strategies, controls, and other efficiency measures. Better Building Smart Lab Accelerator partners provide case study examples of safety and energy-savings results of using Smart Labs approaches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Scale-free, programmable design of morphable chain loops of kilobots and colloidal motors

Micron-scale robots require systems that can morph into arbitrary target configurations controlled by external agents such as heat, light, electricity, and chemical environment. Achieving this behavior using conventional approaches is challenging because the available materials at these scales are not programmable like their macroscopic counterparts. To overcome this challenge, we propose a design strategy to make a robotic machine that is both programmable and compatible with colloidal-scale physics. Our strategy uses motors in the form of active colloidal particles that constantly propel forward. We sequence these motors end-to-end in a closed chain forming a two-dimensional loop that folds under its mechanical constraints. We encode the target loop shape and its motion by regulating six design parameters, each scale-invariant and achievable at the colloidal scale. We demonstrate the plausibility of our design strategy using centimeter-scale robots called kilobots . We use Brownian dynamics simulation to explore the large design space beyond that possible with kilobots, and present an analytical theory to aid the design process. Multiple loops can also be fused together to achieve several complex shapes and robotic behaviors, demonstrated by folding a letter shape “M,” a dynamic gripper , and a dynamic pacman . The material-agnostic, scale-free, and programmable nature of our design enables building a variety of reconfigurable and autonomous robots at both colloidal scales and macroscales.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Drought and Extreme Heat Impacts to Data Centers in Northern California

Commonly, data centers are designed and operated on the assumption of stationarity of the climate. Historical weather data informs the basis of their design and operations. However, as a result of climate change, this assumption is not valid and poses a great risk for data centers. In the coming decades, Northern California’s drought and extreme heat risk is projected to increase significantly. This report provides a drought and extreme heat impacts assessment of data centers in this region. Expert solicitation of data center experts enabled a tailored approach to this report. We identify the climate variables and analytics relevant for assessing data center-specific impacts from extreme heat and drought and demonstrate how to access and understand future climate projections from climate models. We emphasize the importance of including future climate projections into data center design and planning. This report is an important first step towards building an effective extreme heat and drought adaptation strategy for data centers in Northern California. While our geographical focus in this report is Northern California, many findings in this report are broadly applicable.

58 GEOSCIENCES↗
NASA NTRS2004

Application of Colorimetric Solid Phase Extraction (C-SPE) to Monitoring Nickel(II) and Lead(II) in Spacecraft Water Supplies

Archived water samples collected on the International Space Station (ISS) and returned to Earth for analysis have, in a few instances, contained trace levels of heavy metals. Building on our previous advances using Colorimetric Solid Phase Extraction (C-SPE) as a biocide monitoring technique, we are devising methods for the low level monitoring of nickel(II), lead(II) and other heavy metals. C-SPE is a sorption-spectrophotometric platform based on the extraction of analytes onto a membrane impregnated with a colorimetric reagent that are then quantified on the surface of the membrane using a diffuse reflectance spectrophotometer. Along these lines, we have determined nickel(II) via complexation with dimethylglyoxime (DMG) and begun to examine the analysis of lead(II) by its reaction with 2,5- dimercapto-1,3,4-thiadiazole (DMTD) and 4-(2- pyridylazo)-resorcinol (PAR). These developments are also extending a new variant of C-SPE in which immobilized reagents are being incorporated into this methodology in order to optimize sample reaction conditions and to introduce the colorimetric reagent. This paper describes the status of our development of these two new methods.

Diaz, Neil C.↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data (Final Report)

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

Rewiring yeast metabolism to synthesize products beyond ethanol

Saccharomyces cerevisiae, Baker's yeast, is the industrial workhorse for producing ethanol and the subject of substantial metabolic engineering research in both industry and academia. S. cerevisiae has been used to demonstrate production of a wide range of chemical products from glucose. However, in many cases, the demonstrations report titers and yields that fall below thresholds for industrial feasibility. Ethanol synthesis is a central part of S. cerevisiae metabolism, and redirecting flux to other products remains a barrier to industrialize strains for producing other molecules. Furthermore, removing ethanol producing pathways leads to poor fitness, such as impaired growth on glucose. Here, we review metabolic engineering efforts aimed at restoring growth in non-ethanol producing strains with emphasis on relieving glucose repression associated with the Crabtree effect and rewiring metabolism to provide access to critical cellular building blocks. Finally, substantial progress has been made in the past decade, but many opportunities for improvement remain.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

KinBot: Automated stationary point search on potential energy surfaces

KinBot is a Python code that automatically characterizes kinetically important stationary points on reactive potential energy surfaces and arranges the results into a form that lends itself easily to master equation calculations. This version of KinBot tackles C, H, O and S atom containing species and unimolecular (isomerization or dissociation) reactions. KinBot iteratively changes the geometry of the reactant to obtain initial guesses for reactive saddle points defined by KinBot’s reaction types, which are then optimized by a third-party quantum chemistry package. KinBot verifies the connectivity of the saddle points with the reactant and identifies the products through intrinsic reaction coordinate calculations. Here, new calculations can be automatically spawned from the products to obtain complete potential energy surfaces. The utilities of KinBot include conformer searches, projected frequency and hindered rotor calculations, and the automatic determination of the rotational symmetry numbers. Input files for popular RRKM master equation codes are automatically built, enabling an automated workflow all the way to the calculation of pressure and temperature dependent rate coefficients. Four examples are as follows: (i) [1,3]-sigmatropic H-migration reactions of unsaturated hydrocarbons and oxygenates are calculated to assess the relative importance of suprafacial and antrafacial reactions. (ii) Saddle points on three products of gamma-valerolactone thermal decomposition are studied and compared to literature potential energy surfaces. (iii) The previously published propene+OH reaction is reproduced to show the capability of building an entire potential energy surface. (iv) All species up to C4 in the Aramco Mech 2.0 are subjected to a KinBot search.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Fatigue crack growth mechanisms at the microstructure scale in as-fabricated and heat treated Ti-6Al-4V ELI manufactured by electron beam melting (EBM)

Electron beam melting (EBM) is a metal powder bed fusion additive manufacturing (AM) technology that fabricates parts by selectively scanning consecutive powder layers with an electron beam. Additive manufacturing technologies are increasing in importance for aerospace and medical applications, where the demand for a fundamental understanding and predictability of static and dynamic material properties are high. Ti-6Al-4V is the most widely used and studied alloy for this technology, and is the focus of this work in its Extra Low Interstitial (ELI) variation. The layered manufacturing of metallic components by EBM creates a unique directional microstructure, and consequently, anisotropic properties. Microstructure evolution, and its influence on mechanical properties of the alloy in the as-fabricated condition, has been documented by various researchers. However, fatigue crack propagation and the effects of the directional structure have not been sufficiently studied, imposing a barrier for this technology’s potential extension to high-integrity applications. Here in this study, fatigue crack growth (FCG) both parallel and perpendicular to the build directions was studied for different stress ratios and crack growth stages. The interaction between the directional as-fabricated EBM microstructure and FCG was investigated and compared to that of the equiaxed β annealed microstructure obtained by annealing above the β transus temperature. Finally, the FCG threshold, ΔK th , was analytically modeled for the two relative crack propagation directions at different stress ratios, and FCG microstructural mechanisms were established for all three regions of crack propagation.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Synthesis, structure and properties of zinc(II) coordination polymers with 9H-carbazole-2,7-dicarboxylic acid

From a new dicarboxylate ligand, 9H-carbazole-2,7-dicarboxylic acid (2,7-H{sub 2}CDC), three Zn(II) metal-organic frameworks were synthesized in the absence or presence of ditopic N-donor ligands. They are formulated as [Zn{sub 5}(μ{sub 3}-OH){sub 2}(2,7-CDC){sub 4}(DEF){sub 2}] (1) (DEF=N,N-diethylformamide), [Zn{sub 2}(2,7-CDC){sub 2}(DABCO)(H{sub 2}O)]·5DMF·H{sub 2}O (2) (DABCO=1-diaza-bicyclo[2.2.2]octane, DMF=N,N-dimethylformamide), and [Zn{sub 2}(2,7-CDC){sub 2}(bpea)]·3DMA·2 H{sub 2}O (3) (bpea=1,2-bis(4-pyridyl)ethylane, DMA=N,N-dimethylacetamide). Compounds 1 and 3 display the 3D pcu frameworks. In 1 the unusual pentanuclear [Zn{sub 5}(μ{sub 3}-OH){sub 2}(COO){sub 8}] secondary building units (SBUs) are linked by dicarboxylate ligands. Differently, in 3 the well-known paddle–wheel [Zn{sub 2}(COO){sub 4}] SBUs are linked by dicarboxylate and dipyridyl ligands. Compound 2 shows the rare self-catenated 3D alb-3,6-C2/c net topology based on the dinuclear paddle–wheel SBU and a mononuclear unit. The stability and fluorescent properties of the compounds have been studied. - Graphical abstract: A new dicarboxylate ligand, 9H-carbazole-2,7-dicarboxylic acid, was used to construct Zn(II) metal-organic frameworks, including a novel self-catenated network with the rare 3D alb-3,6-C2/c net and two pcu-type networks based on an unprecedented pentanuclear clusters and the common paddle–wheel units. The compounds show blue fluorescent properties. Display Omitted - Highlights: • MOFs with a new carbazole-based dicarboxylate ligand. • New pentanuclear [Zn{sub 5}(μ{sub 3}-OH){sub 2}(COO){sub 8}] secondary building unit. • The rare self-catenated 3D alb-3,6-C2/c net.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗