Bond Order Research Articles

Efficient Capture and Low Energy Release of NH3 by Azophenol Decorated Photoresponsive Covalent Organic Frameworks.

In NH3 capture technologies, the desorption process is usually driven by high temperature and low pressure (such as 150-200 °C under vacuum), which accounts for intensive energy consumption and CO2 emission. Developing light responsive adsorbent is promising in this regard but remains a great challenge. Here, we for the first time designed and synthesized a light responsive azophenol-containing covalent organic framework (COF), COF-HNU38, to address this challenge. We found that at 25 °C and 1.0 bar, the cis -COF exhibited a NH3 uptake capacity of 7.7 mmol g-1 and a NH3/N2 selectivity of 158. In the adsorbed NH3, about 29.0 % could be removed by vis-light irradiated cis-trans isomerization at 25 °C, and the remaining NH3 might be released at 25 °C under vacuum. Almost no decrease in adsorption capacity was observed after eight adsorption-desorption cycles. As such, an efficient NH3 capture and low energy release strategy was established thanks to the multiple hydrogen bond interactions (which are strong in total but weak in individuals) between NH3 and the smart COF, as well as the increased polarity and number of hydrogen bond sites after the trans-cis isomerization.

MXene-Based Skin-Like Hydrogel Sensor and Machine Learning-Assisted Handwriting Recognition.

Conductive hydrogels are widely used in flexible sensors owing to their adjustable structure, good conductivity, and flexibility. The performance of excellent mechanical properties, high sensitivity, and elastic modulus compatible with human tissues is of great interest in the field of flexible sensors. In this paper, the functional groups of trisodium citrate dihydrate (SC) and MXene form multiple hydrogen bonds in the polymer network to prepare a hydrogel with mechanical properties (Young's modulus (23.5-92 kPa) of similar human tissue (0-100 kPa)), sensitivity (stretched GF is 4.41 and compressed S1 is 5.15 MPa-1), and durability (1000 cycles). The hydrogel is able to sensitively detect deformations caused by strain and stress and can be used in flexible sensors to detect human movement in real time such as fingers, wrists, and walking. In addition, the combination of matrix sensing and machine learning was successfully used for handwriting recognition with an accuracy of 0.9744. The combination of machine learning and flexible sensors shows great potential in areas such as healthcare, information security, and smart homes.

Sodium alginate/guar gum/multi-walled carbon nanotubes-based hydrophobic Joule thermal textiles for enhanced body thermal management and dehumidification

A sodium alginate (SA), guar gum, and multi-walled carbon nanotube composite solution was prepared using a simple blending method, followed by the construction of alginate/guar gum/multi-walled carbon nanotube fibers and fabrics through complexation with various metal ions (zinc, copper, calcium), featuring multiple hydrogen bonds and ionic complex structures. Specifically, the sodium alginate zinc/guar gum/multi-walled carbon nanotube (SA/GG/Zn2+/MWCNTs) composite fibers exhibited a significant Joule heat effect. By integrating with electronic components such as a temperature control switch, a wearable device capable of operating in cold and humid environments for dehumidification and warming was fabricated. The SA/GG/Zn2+/MWCNTs composite fabric demonstrated excellent hydrophobicity, with a contact angle reaching 145.4°, significantly enhancing its resistance to droplet penetration. Under a 5V voltage, the composite fabric rapidly generated Joule heat, increasing the surface temperature by 5∼7 °C (sensible heat) within just 40 s. Additionally, part of its energy (latent heat) transformed the tiny droplets adhering to the fabric's surface from liquid to gas, achieving dehumidification. This research provides an important theoretical and practical foundation for enhancing the performance and energy efficiency of personal thermal management textiles.

Nucleophilic Aromatic Substitution of Halobenzenes and Phenols with Catalysis by Arenophilic π Acids.

ConspectusLewis π acids, particularly high-valent transition metals with vacant orbitals, can coordinate with unsaturated compounds such as alkynes and alkenes by means of π-bonding. The coordination enhances the electrophilicity of the bound compounds, thereby facilitating reactions─such as nucleophilic addition─that take place at the ligated carbon-carbon multiple bonds. This activation phenomenon occurs at the ligand rather than at the metal atom, and it has been extensively utilized in the development of catalytic methods. In addition to alkynes and alkenes, aromatic compounds featuring a phenyl ring can be activated by an electrophilic transition-metal unit (e.g., Cr(CO)3, [Mn(CO)3]+, [CpFe]+, or [CpRu]+, where Cp = cyclopentadienyl) through π coordination. Over the past several decades, remarkable advances have been achieved in the development of reactions occurring on bound arenes, capitalizing on the highly electron-withdrawing nature of these transition-metal units and on the thermodynamic stability of η6-arene complexes. A prime example is the extension of nucleophilic aromatic substitution (SNAr) reactions to electron-neutral and -rich halobenzenes. Such arenes, which are normally inert to classical SNAr, can undergo sequences involving complex formation, substitution, and complex decomposition. Despite the successes achieved through the utilization of preformed complexes, the application of reversible arene coordination to catalytic systems has seen only limited progress. Consequently, in π-coordination activation, transition-metal units are commonly considered to be components of bound arene complexes rather than π-acid catalysts.In this Account, we summarize our recent research on catalytic SNAr reactions of halobenzenes and phenols enabled by reversible π-coordination of the arenes with electrophilic Ru or Rh catalysts, which we refer to as arenophilic π-acids. First, we developed a method for SNAr amination of fluorobenzenes with catalysis by a Ru(II) complex with a hemilabile P,O-bidentate ligand. The use of the hemilabile ligand significantly enhanced catalytic efficiency, allowing electron-rich and -neutral arenes to undergo amination without the need of excess fluorobenzenes. In a subsequent study of hydroxylation and alkoxylation reactions, we found that Rh(III) catalysts bearing a Cp-type ligand had a substantial activating effect. In addition, by isolating an η5 complex as the reaction intermediate, we obtained evidence in support of the long-standing hypothesis that SNAr of η6-arene complexes proceeds via a stepwise mechanism. Next, we extended the Rh-catalyzed SNAr to chloro- and bromobenzenes, which are abundant and readily available but are less reactive than corresponding fluorides toward SNAr. When the weakly nucleophilic alcohol hexafluoroisopropanol was used as a reaction partner, we were able to synthesize hexafluoroisopropyl aryl ethers, which are challenging to obtain by means of conventional approaches. Beyond halobenzenes, we successfully applied π-coordination strategy to achieve umpolung substitution reactions of phenols, which are typically nucleophilic. We found that an arenophilic Rh or Ru catalyst activated the phenol ring by π coordination instead of κ-O coordination, generating transient η5-phenoxo complexes that subsequently underwent carbonyl-amine condensation to produce anilines without the need for an exogenous oxidant or reductant. We anticipate that our research on catalyst development and reactions involving π-coordination activation will facilitate further advances in the application of arenophilic π acids.

Ultra-Fast-Healing Glassy Hyperbranched Plastics Capable of Restoring 26.4 MPa Tensile Strength within One Minute at Room Temperature.

The growing concern regarding widespread plastic pollution has propelled the development of sustainable self-healing plastics. Although considerable efforts have been dedicated to fabricating self-healing plastics, achieving rapid healing at room temperature is extremely challenging. Herein, we have developed an ultra-fast-healing glassy polyurethane (UGPU) by designing a hyperbranched molecular structure with a high density of multiple hydrogen bonds (H-bonds) on compliant acyclic heterochains and introducing trace water to form water bridge across the fractured surfaces. The compliant acyclic heterochains allow the dense multiple hydrogen bonds to form a frozen network, enabling tensile strength of up to 70 MPa and storage modulus of 2.5 GPa. The hyperbranched structure can drive the reorganization of the H-bonding network through the high mobility of the branched chains and terminals, thereby leading to self-healing ability at room temperature. Intriguingly, the presence of trace water vapor facilitates the formation of activated layers and the rearrangement of networks across the fractured UGPU sections, thereby enabling ultra-fast self-healing at room temperature. Consequently, the restored tensile strength after healing for 1 minute achieves a historic-record of 26.4 MPa. Furthermore, the high transparency (>90 %) and ultra-fast healing property of UGPU make it an excellent candidate for advanced optical and structural materials.

Ultra‐Fast‐Healing Glassy Hyperbranched Plastics Capable of Restoring 26.4 MPa Tensile Strength within One Minute at Room Temperature

AbstractThe growing concern regarding widespread plastic pollution has propelled the development of sustainable self‐healing plastics. Although considerable efforts have been dedicated to fabricating self‐healing plastics, achieving rapid healing at room temperature is extremely challenging. Herein, we have developed an ultra‐fast‐healing glassy polyurethane (UGPU) by designing a hyperbranched molecular structure with a high density of multiple hydrogen bonds (H‐bonds) on compliant acyclic heterochains and introducing trace water to form water bridge across the fractured surfaces. The compliant acyclic heterochains allow the dense multiple hydrogen bonds to form a frozen network, enabling tensile strength of up to 70 MPa and storage modulus of 2.5 GPa. The hyperbranched structure can drive the reorganization of the H‐bonding network through the high mobility of the branched chains and terminals, thereby leading to self‐healing ability at room temperature. Intriguingly, the presence of trace water vapor facilitates the formation of activated layers and the rearrangement of networks across the fractured UGPU sections, thereby enabling ultra‐fast self‐healing at room temperature. Consequently, the restored tensile strength after healing for 1 minute achieves a historic‐record of 26.4 MPa. Furthermore, the high transparency (>90 %) and ultra‐fast healing property of UGPU make it an excellent candidate for advanced optical and structural materials.

Cross-Validation of Lipid Structure Assignment Using Orthogonal Ion Activation Modalities on the Same Mass Spectrometer.

The onset and progression of cancer is associated with changes in the composition of the lipidome. Therefore, better understanding of the molecular mechanisms of these disease states requires detailed structural characterization of the individual lipids within the complex cellular milieu. Recently, changes in the unsaturation profile of membrane lipids have been observed in cancer cells and tissues, but assigning the position(s) of carbon-carbon double bonds in fatty acyl chains carried by membrane phospholipids, including the resolution of lipid regioisomers, has proven analytically challenging. Conventional tandem mass spectrometry approaches based on collision-induced dissociation of ionized glycerophospholipids do not yield spectra that are indicative of the location(s) of carbon-carbon double bonds. Ozone-induced dissociation (OzID) and ultraviolet photodissociation (UVPD) have emerged as alternative ion activation modalities wherein diagnostic product ions can enable de novo assignment of position(s) of unsaturation based on predictable fragmentation behaviors. Here, for the first time, OzID and UVPD (193 nm) mass spectra are acquired on the same mass spectrometer to evaluate the relative performance of the two modalities for lipid identification and to interrogate the respective fragmentation pathways under comparable conditions. Based on investigations of lipid standards, fragmentation rules for each technique are expanded to increase confidence in structural assignments and exclude potential false positives. Parallel application of both methods to unsaturated phosphatidylcholines extracted from isogenic colorectal cancer cell lines provides high confidence in the assignment of multiple double bond isomers in these samples and cross-validates relative changes in isomer abundance.

Toward Realistic Models of the Electrocatalytic Oxygen Evolution Reaction.

The electrocatalytic oxygen evolution reaction (OER) supplies the protons and electrons needed to transform renewable electricity into chemicals and fuels. However, the OER is kinetically sluggish; it operates at significant rates only when the applied potential far exceeds the reversible voltage. The origin of this overpotential is hidden in a complex mechanism involving multiple electron transfers and chemical bond making/breaking steps. Our desire to improve catalytic performance has then made mechanistic studies of the OER an area of major scientific inquiry, though the complexity of the reaction has made understanding difficult. While historically, mechanistic studies have relied solely on experiment and phenomenological models, over the past twenty years ab initio simulation has been playing an increasingly important role in developing our understanding of the electrocatalytic OER and its reaction mechanisms. In this Review we cover advances in our mechanistic understanding of the OER, organized by increasing complexity in the way through which the OER is modeled. We begin with phenomenological models built using experimental data before reviewing early efforts to incorporate ab initio methods into mechanistic studies. We go on to cover how the assumptions in these early ab initio simulations─no electric field, electrolyte, or explicit kinetics─have been relaxed. Through comparison with experimental literature, we explore the veracity of these different assumptions. We summarize by discussing the most critical open challenges in developing models to understand the mechanisms of the OER.

Mechanistic investigation and machine learning exploration of structure-activity relationship of NHC-Mn catalyzed ester hydrosilylation reaction

This study aims to deeply investigate the mechanism of NHC-Mn catalyzed ester hydrosilylation reaction and the impact of different ligands on reaction activity through the comprehensive application of DFT calculations and machine learning technologies. We initially proposed four possible reaction mechanisms including two radical mechanisms and two 2e mechanisms. Then calculate the potential energy surfaces of them. It was demonstrated through calculations that the radical mechanisms were implausible, while the 2e outer-sphere mechanism was proven to be reasonable. A detailed analysis of the Si–H bond activation step in different reaction mechanisms highlighted the crucial role of steric effects in the process. Furthermore, the rate-determining step in the 2e mechanism (outer-sphere), the hydride transfer process, was also thoroughly studied. Eventually, a predictive model was successfully constructed using machine learning methods, which is a model based on the Ridge algorithm with 17 features that accurately predict the impact of different types of ligands on the reaction barrier. Analysis of the model revealed important factors affecting reaction activity, such as the vertical ionization potential of the metal hydride intermediate, the charge on the metal hydride, and the bond order of the Mn–H bond.

Intermediate Intercluster Bond Orders. Electronic Communication in Au38(SR)24 Superatomic Molecules.

Ligand-protected gold clusters remain potential building blocks for envisaged molecular materials. The archetypal Au38(SR)24 cluster can be viewed as a robust template for the fusion of two Au25(SR)18 - cluster units, retaining a bi-icosahedral Au23 core. Via electrochemical properties, the overall charge state can be selectively tuned, enabling the access of 14 valence electron (ve) species featuring a single intercluster bond and nearby charge from -1 to +3, achieving related species bearing 15- to 11-ve with variable intercluster bond orders. Here, we explore the characteristics of intermediate intercluster bond orders in order to provide insights into the plausible electron communication between the constituent building blocks, with Au38(SR)24, as a representative template. Our results denote a small structural variation along -1 to +3 charge states, provided by the core-protecting ligand interaction, which is enhanced towards more oxidized species. The remaining unpaired electron from intermediate intercluster bond orders of 1.5 for Au38(SR)24 1-, 1.5 for Au38(SR)24 1+, and 2.5 for Au38(SR)24 3+, holds delocalized characteristics between the building block units, favoring electron communication for conductive and cooperative cluster aggregates. Such features are relevant for the formation of molecular electronic device applications, favoring the rationalization prior to engaging in explorative synthesis of larger ligand-protected cluster aggregates.

By Introducing Multiple Hydrogen Bonds Endows MOF Electrodes with an Enhanced Structural Stability.

Recently, metal-organic frameworks (MOFs) have attracted great interest in energy storage areas. However, the poor structural stability of MOFs derived from weak coordination bonds limits their applications. Here, quadruple hydrogen bonds (H-bonds) were introduced onto the MOFs to enhance their structural stability. Cross-linked networks could be formed between molecules owing to multiple H-bonds, strengthening the framework stability. Moreover, the dynamic reversibility of H-bonds could endow MOFs with self-healing ability. Furthermore, due to lower binding energy compared to coordination bonds, H-bonds break preferentially when subjected to internal stress, thus protecting the MOFs. Consequently, the as-prepared self-healing hybrid (SHH-Cu-MOF@Ti3C2TX) exhibited high capacitance retention (89.4%) after 5000 cycles at 1 A g-1, while that hybrid without dynamic H-bonds (H-Cu-MOF@Ti3C2TX) presented a 79.9% retention, delivering an enhancement in cycling stability. Moreover, an asymmetric supercapacitor (ASC) was fabricated by employing SHH-Cu-MOF@Ti3C2TX and activated carbon (AC) as the electrodes. The ASC delivered a specific capacitance (47.4 F g-1 at 1 A g-1), an energy density (16.9 Wh kg-1), and a power density (800 W kg-1) as well as good rate ability (retains 81% of its initial capacitance from 0.2 A g-1 to 5 A g-1).

Isoreticular Covalent Organic Pillars: Engineered Nanotubular Hosts for Tailored Molecular Recognition.

In the realm of nanoscale materials design, achieving precise control over the dimensions of nanotubular architectures poses a substantial challenge. In our ongoing pursuit, we have successfully engineered a novel class of single-molecule nanotubes─isoreticular covalent organic pillars (iCOPs)─by stacking formylated macrocycles through multiple dynamic covalent imine bonds, guided by principles of reticular chemistry. Our strategic selection of rigid diamine linkers has facilitated the synthesis of a diverse array of iCOPs, each retaining a homologous structure yet offering distinct cavity shapes influenced by the linker choice. Notably, three of these iCOP variants feature continuous one-dimensional channels, exhibiting length-dependent host-guest interactions with α,ω-dibromoalkanes, and each presenting a distinct critical guest alkyl chain length threshold for efficient guest encapsulation. This newfound capability not only provides a platform for tailoring nanotubular structures with precision, but also opens new avenues for innovative applications in molecular recognition and the purification of complex mixtures.

Cross-scale interface engineering strategy to achieve self-healing antifouling material strength and toughness symbiosis

The incorporation of self-healing technology into antifouling coatings is crucial for the extension of their service life and durability. However, the challenge lies in the insufficient coexistence of strength and toughness hinders their long-term serviceability. Here, this study proposes a cross-scale interfacial engineering strategy for self-healing antifouling coatings inspired by the multiscale microstructure of natural biomaterials The strategy involves the in-situ growth of antifouling zinc-based metal–organic frameworks (Zn-MOF) within a self-healing polymer network constructed by multiple hydrogen bonds. The synergistic cross-scale interfacial interactions between Zn-MOF and the polymer molecules, combined with the intrinsic antifouling properties of Zn-MOF, significantly enhance the strength, toughness to 27.48 MPa, 277.81 MJ/m3, and benefit improving antifouling performance. Moreover, the molecular integration of Zn2+-pyridine coordination optimizes the microphase structure of the polymer matrix and embeds the advantage of its fast damage response, thereby further enhancing the robustness, and reaching self-healing efficiency to 91.74 % at 40 °C for 24 h. Overall, the proposed strategy represents a viable approach for the fabrication of highly resilient and tough self-healing antifouling coatings.

QC-GN2oMS2: a Graph Neural Net for High Resolution Mass Spectra Prediction.

Predicting the mass spectrum of a molecular ion is often accomplished via three generalized approaches: rules-based methods for bond breaking, deep learning, or quantum chemical (QC) modeling. Rules-based approaches are often limited by the conditions for different chemical subspaces and perform poorly under chemical regimes with few defined rules. QC modeling is theoretically robust but requires significant amounts of computational time to produce a spectrum for a given target. Among deep learning techniques, graph neural networks (GNNs) have performed better than previous work with fingerprint-based neural networks in mass spectra prediction. To explore this technique further, we investigate the effects of including quantum chemically derived information as edge features in the GNN to increase predictive accuracy. The models we investigated include categorical bond order, bond force constants derived from extended tight-binding (xTB) quantum chemistry, and acyclic bond dissociation energies. We evaluated these models against a control GNN with no edge features in the input graphs. Bond dissociation enthalpies yielded the best improvement with a cosine similarity score of 0.462 relative to the baseline model (0.437). In this work we also apply dynamic graph attention which improves performance on benchmark problems and supports the inclusion of edge features. Between implementations, we investigate the nature of the molecular embedding for spectra prediction and discuss the recognition of fragment topographies in distinct chemistries for further development in tandem mass spectrometry prediction.

Exploring fracture of H-BN and graphene by neural network force fields

Extreme mechanical processes such as strong lattice distortion and bond breakage during fracture often lead to catastrophic failure of materials and structures. Understanding the nucleation and growth of cracks is challenged by their multiscale characteristics spanning from atomic-level structures at the crack tip to the structural features where the load is applied. Atomistic simulations offer ‘first-principles’ tools to resolve the progressive microstructural changes at crack fronts and are widely used to explore the underlying processes of mechanical energy dissipation, crack path selection, and dynamic instabilities (e.g. kinking, branching). Empirical force fields developed based on atomic-level structural descriptors based on atomic positions and the bond orders do not yield satisfying predictions of fracture, especially for the nonlinear, anisotropic stress–strain relations and the energy densities of edges. High-fidelity force fields thus should include the tensorial nature of strain and the energetics of bond-breaking and (re)formation events during fracture, which, unfortunately, have not been taken into account in either the state-of-the-art empirical or machine-learning force fields. Based on data generated by density functional theory calculations, we report a neural network-based force field for fracture (NN-F3) constructed by using the end-to-end symmetry preserving framework of deep potential—smooth edition (DeepPot-SE). The workflow combines pre-sampling of the space of strain states and active-learning techniques to explore the transition states at critical bonding distances. The capability of NN-F3 is demonstrated by studying the rupture of hexagonal boron nitride (h-BN) and twisted bilayer graphene as model problems. The simulation results elucidate the roughening physics of fracture defined by the lattice asymmetry in h-BN, explaining recent experimental findings, and predict the interaction between cross-layer cracks in twisted graphene bilayers, which leads to a toughening effect.

Cover Feature: The Peptide Bond: Resonance Increases Bond Order and Complicates Fragmentation (ChemPhysChem 14/2024)

Cover Feature: The Peptide Bond: Resonance Increases Bond Order and Complicates Fragmentation (ChemPhysChem 14/2024)

Relating Bond Orders and Bond Energies to Ionization Energies and Electron Affinities: A Quantitative Approach

Relating Bond Orders and Bond Energies to Ionization Energies and Electron Affinities: A Quantitative Approach

Insights into the variations of kinetic and potential energies in a multi-bond reaction: the reaction electronic flux perspective.

The debate over whether kinetic energy (KE) or potential energy (PE) are the fundamental energy components that contribute to forming covalent bonds has been enduring and stimulating over time. However, the supremacy of these energy components in reactions where multiple bonds are simultaneously formed or broken has yet to be explored. In this study, we use the reaction electronic flux (REF), an effective tool for investigating changes in driving electronic activity when bond formation or dissociation occurs in a chemical reaction, to examine the fluctuations in the KE and PE in a multi-bond reaction. To that end, the activation of CO by low-valent group 14 catalysts through a concerted -bond metathesis mechanism is analyzed. The findings of this preliminary study suggest that the REF can be utilized as a tool to rationalize alterations in the KE and PE in a multi-bond reaction. Specifically, analyses across the reaction coordinate reveal that changes in the KE and PE precede activation in the REF, stimulating the electronic activity where bond formation or dissociation processes dominate. The activation of CO by the low-valent LEH catalysts (L = N,N'-bis(2,6-diisopropyl phenyl)- -diketiminate; E = Si, Ge, Sn, and Pb) was studied along the reaction coordinate at the M06-2X/6-31G(d,p)-LANL2DZ(E) level of theory. The respective minimum energy path calculations were obtained using the intrinsic reaction coordinate (IRC) procedure. The reaction electronic flux (REF) was calculated through the computation of the electronic chemical potential using the frontier molecular orbital approximation. Mayer bond orders along the reaction coordinate have been determined using the NBO 3.1 program in Gaussian16. Most of the reaction coordinate quantities reported in this study (REF, KE, PE, among others) have been determined using the Kudi program and custom Python scripts.

Synthesis and Characterization of Octacyano-Cu-Phthalocyanine.

Octacyano-metal-substituted phthalocyanine MPc(CN)8 is a promising n-type stable organic semiconductor material with eight cyano groups, including a strong electron-withdrawing group at its molecular terminals. However, most MPc(CN)8 have not been thoroughly investigated. Therefore, CuPc(CN)8 was synthesized in this study and its crystal structure, chemical and electronic states, thermal stability, and electrical properties were investigated. This article discusses the various properties of CuPc(CN)8, as compared to those of CuPc and FePc(CN)8. The previously reported FePc(CN)8 is an organic semiconductor molecule with a molecular structure similar to that of CuPc(CN)8. X-ray diffraction (XRD) measurements revealed that CuPc(CN)8 has a crystalline structure in the P1̅ space group. The crystal structure forms an in-plane network parallel to the molecular plane through multiple hydrogen bonds by the cyano groups at the molecular terminals. Interestingly, the crystal structure, especially the molecular stacking, of CuPc(CN)8 differs from that of FePc(CN)8. The absorption edge observed in the ultraviolet-visible spectrum of CuPc(CN)8 shifted to a longer wavelength than that of CuPc, which was attributed to the energy gap of CuPc(CN)8 being smaller than that of CuPc owing to the influence of the cyano groups at the molecular terminals, according to the molecular orbital calculation results using density functional theory. Ultraviolet photoelectron spectroscopy measurements confirmed that CuPc(CN)8 had a stronger n-type character than CuPc because of the orbital energy stabilization by the cyano groups. Thermogravimetry/differential thermal analysis measurements revealed that the thermal stability of CuPc(CN)8 was significantly higher than that of FePc(CN)8. CuPc(CN)8 exhibited photoconduction upon visible-light irradiation, and its electrical conductivity was higher than that of CuPc, which was attributed to a reduction in the electron injection barrier at the electrode interfaces.

Binuclear Lantern Complexes of All First-Row 3d Metals Scandium to Zinc: Metal-Metal Bond Lengths and Bond Orders, with Measures of Intrinsic Metal-Metal Bond Strength.

The B3LYP and M06-L functionals with the cc-pVTZ basis set are used to study lantern-type binuclear complexes of all the first-row (3d block) metals scandium to zinc in various low-energy spin states, out of which the ground states are predicted. These complexes are studied as models using mostly the unsubstituted formamidinate ligand. For each metal, metal-metal (MM) bond lengths are related to the formal MM bond orders (zero to five), derived by MO analysis and by electron counting. The predicted ground-state spin multiplicities and MM bond lengths of the model complexes generally agree fairly well with available experimental results on substituted analogues. Finally, values of the formal shortness ratio and Wiberg index for the MM bonds in all of these complexes in all spin states studied are categorized into ranges according to the MM bond orders 0 to 5 in steps of 0.5. The trends shown validate their use in estimating intrinsic metal-metal bond strength regardless of the metal.