Number Of Atoms Research Articles

Adsorption characteristics of Ni cluster Modified-InN monolayer for indoor noxious gases (CH2O, C6H6, and NH3)

With the continuous enhancement of people's health awareness and the pursuit of high-quality life, the noxious gases generated after the renovation of new houses are receiving more attention. The gas sensing properties of InN monolayer modified with Ni clusters (1–3 Ni atoms) on CH2O, C6H6, and NH3 were analyzed based on DFT calculations, including the adsorption energy, charge transfer, density of states, bandgap structure, molecular orbitals, electrostatic potential, and desorption time investigation of gas adsorption systems. Moreover, different numbers of Ni atoms were introduced and their adsorption energies were analyzed. The results showed that the intrinsic InN and Ni–InN were suitable materials for gas sensors, while Ni2–InN and Ni3–InN were more suitable as adsorbents for clearing these three gases. The change in conductivity upon gas adsorption provides a theoretical basis for identifying the concentration of mixed gases of CH2O, C6H6, and NH3.

Effects of vacancies on quantum transport of zigzag graphene nanoribbons

Using the Green’s function method, we investigate the effect of vacancies on conductance and local density of states (LDOS) in zigzag graphene nanoribbons within a symmetric and asymmetric potential wells. The results differ from ideal ribbons in that there are no conductance plateaus and a decline in conductance for varied N (number of atoms in the unit cell) and M (number of unit cells) values following vacancy incorporation. LDOS and conductance in symmetrical and asymmetrical circumstances are examined alongside vacancies and accordingly, they are affecting the respective edge states.

EspalomaCharge: Machine Learning-Enabled Ultrafast Partial Charge Assignment.

Atomic partial charges are crucial parameters in molecular dynamics simulation, dictating the electrostatic contributions to intermolecular energies and thereby the potential energy landscape. Traditionally, the assignment of partial charges has relied on surrogates of ab initio semiempirical quantum chemical methods such as AM1-BCC and is expensive for large systems or large numbers of molecules. We propose a hybrid physical/graph neural network-based approximation to the widely popular AM1-BCC charge model that is orders of magnitude faster while maintaining accuracy comparable to differences in AM1-BCC implementations. Our hybrid approach couples a graph neural network to a streamlined charge equilibration approach in order to predict molecule-specific atomic electronegativity and hardness parameters, followed by analytical determination of optimal charge-equilibrated parameters that preserve total molecular charge. This hybrid approach scales linearly with the number of atoms, enabling for the first time the use of fully consistent charge models for small molecules and biopolymers for the construction of next-generation self-consistent biomolecular force fields. Implemented in the free and open source package EspalomaCharge, this approach provides drop-in replacements for both AmberTools antechamber and the Open Force Field Toolkit charging workflows, in addition to stand-alone charge generation interfaces. Source code is available at https://github.com/choderalab/espaloma-charge.

DFT Study on Effect of Metal Type and Coordination Environment on CO2 ECR to C1 Products over M-N-C Catalysts.

Electrocatalytic reduction (ECR) of CO2 to chemical products is an important carbon emission reduction method. This work uses DFT to study the stability of N-doped graphene-supported four metal single-atom catalysts (M-N-C) and the effects of the coordination environment and metal centers on the selectivity of CO2 ECR to C1 products. The results show that Fe, Co, Ni, and Cu have good stability. The coordination environment has a significant modulating effect on product selectivity, and the change of the number of ligand nitrogen atoms will affect the size of the potential-limiting step of each product. When the number of nitrogen ligands is the same, the different metal centers of the M-N-C catalyst have a significant effect on the selectivity of different products. In addition, the introduction of nitrogen atom ligands can adjust the electronic structure of the graphene-supported metal center, increase the d-band center of most metals, and improve the reaction activity.

Interpretable machine learning for selection of site-specific soil constitutive models and consolidation settlement analysis

Machine learning (ML) has achieved great breakthroughs in many fields, such as computer vision and speech recognition, and it also gains an increasing attention in geotechnical engineering. However, it is often criticized for a lack of interpretability. This study proposes an interpretable sparse dictionary learning (ISDL) approach for selection of suitable soil constitutive models (SCMs) and geotechnical consolidation analysis in a specific site. ISDL is inherently interpretable since it expresses prediction of geotechnical responses (e.g., consolidation settlement) as a weighted summation of many elementary datasets (i.e., dictionary atoms in ISDL). The dictionary atoms are constructed using numerical analyses (e.g., finite element model) with different candidate SCMs and parameters. Interpretability of ISDL is improved by a small number of non-trivial atoms (or SCMs) selected for model prediction using site-specific monitoring data and further enhanced by contribution ranking of the selected SCMs using a game theory-based approach. The proposed method is illustrated using a real reclamation project in Hong Kong, and it is shown to effectively select the most suitable constitutive models for the given site, quantify contributions of the selected models, and significantly improve settlement predictions with rigorously quantified prediction uncertainty, particularly at locations without monitoring or future time steps.

Impact of the terminal fluorination of bisthienylethene-based liquid crystals on the self-assembly and mechanochromic behaviors

Four novel bisthienylethene-based rod-like liquid crystals containing different numbers of terminal fluorine atoms were designed and the effect of the terminal fluorination on liquid crystalline self-assembly, optical characteristics in solid state and mechanochromic behaviors was explored. The transition from non-liquid crystal via monotropic liquid crystal with smectic A phase to enantiotropic liquid crystal with smectic A phase can be achieved and the temperature ranges of smectic A phase gradually increase by increasing the terminal fluorination, which might be due to the increase in intermolecular interactions by increasing the number of terminal fluorine atom. All the compounds exhibited AIE characteristic and distinct solid-state fluorescence with yellow or orange color. In addition, the non-fluorosubstituted and difluorosubstituted bisthienylethene-based liquid crystals exhibited reversible mechanochromism, whereas the monofluorosubstituted and trifluorosubstituted bisthienylethene-based liquid crystals exhibited extremely weak mechanochromism due to different intermolecular interaction, molecular symmetry and molecular conformation. These investigations implied that the incorporation of fluorine atom could result in interesting self-assemblies and photophysical properties in different states, which also provided the understanding of the relationship between chemical structures and properties.

Treatment of flexibility of protein backbone in simulations of protein–ligand interactions using steered molecular dynamics

To ensure that an external force can break the interaction between a protein and a ligand, the steered molecular dynamics simulation requires a harmonic restrained potential applied to the protein backbone. A usual practice is that all or a certain number of protein’s heavy atoms or Cα atoms are fixed, being restrained by a small force. This present study reveals that while fixing both either all heavy atoms and or all Cα atoms is not a good approach, while fixing a too small number of few atoms sometimes cannot prevent the protein from rotating under the influence of the bulk water layer, and the pulled molecule may smack into the wall of the active site. We found that restraining the Cα atoms under certain conditions is more relevant. Thus, we would propose an alternative solution in which only the Cα atoms of the protein at a distance larger than 1.2 nm from the ligand are restrained. A more flexible, but not too flexible, protein will be expected to lead to a more natural release of the ligand.

Investigating the temperature dependence of helium bubble dynamics in plasma exposed tungsten via in-situ TEM annealing

In-situ TEM annealing and imaging were performed on tungsten samples pre-exposed at either 573 K or 1013 K in a pure helium plasma to a fluence of 1025 m−2. To investigate the thermal evolution of helium bubbles, the samples were imaged for annealing temperatures up to 998 K in steps of 100 K. It is found that annealing temperatures had little effect on the bubble dynamics for the lower temperature exposure of 573 K. Elongated bubble structures remained in the sample throughout the annealing process and there is minimal change in both the average bubble size and cross-sectional number density. However, annealing had a significant effect on the higher temperature exposure of 1013 K. Bubbles were found to be trapped in large clusters and increasing annealing temperature caused a significant increase in the average bubble size accompanied by a decrease in the cross-sectional bubble number density. The experimental observations are supported by theoretical calculations that show a simultaneous increase in the average number of He atoms per bubble in the higher exposure temperature sample but an overall decrease in He atoms retained within bubbles as the annealing temperature increased. These trends in bubble dynamics with annealing temperature suggest that Ostwald ripening dominates, whereby bubble growth is determined by the different pressures of different bubble sizes, rather than through bubble migration and coalescence.

Interatomic Potential For Carbon Based Quantum‐Technology Applications

AbstractTo optimize parameters for laser processing of quantum‐technology relevant materials, such as diamond, precise atomistic simulations of the light‐matter interaction on large scales (on the order of atoms) are essential. Classical empirical interatomic potentials are commonly employed for simulating such a large number of atoms, however they fail to accurately capture all relevant effects of light‐matter interaction. Conversely, ab initio methods like Density Functional Theory (DFT) can effectively incorporate quantum properties arising from photon excitations, but their applicability is limited to small systems containing at most approximately atoms. Consequently, bridging the gap between achieving DFT precision and handling millions of atoms necessitates the development of innovative classes of interatomic potentials. In this paper, the construction of a highly accurate interatomic potential for diamond is presented, derived from an extensive dataset of DFT calculations. The parameters of the interatomic potential depend on the electronic temperature (). The findings demonstrate that this newly developed interatomic potential can aptly describe the laser processing of diamond for nanophotonic applications, achieving accuracy comparable to ab initio methods for large systems.

SAF: Smart Aggregation Framework for Revealing Atoms Importance Rank and Improving Prediction Rates in Drug Discovery.

Machine learning, and representation learning in particular, has the potential to facilitate drug discovery by screening a large chemical space in silico. A successful approach for representing molecules is to treat them as graphs and utilize graph neural networks. One of the key limitations of such methods is the necessity to represent compounds with different numbers of atoms, which requires aggregating the atom's information. Common aggregation operators, such as averaging, result in a loss of information at the atom level. In this work, we propose a novel aggregating approach where each atom is weighted nonlinearly using the Boltzmann distribution with a hyperparameter analogous to temperature. We show that using this weighted aggregation improves the ability of the gold standard message-passing neural network to predict antibiotic activity. Moreover, by changing the temperature hyperparameter, our approach can reveal the atoms that are important for activity prediction in a smooth and consistent way, thus providing a novel regulated attention mechanism for graph neural networks. We further validate our method by showing that it recapitulates the functional group in β-lactam antibiotics. The ability of our approach to rank the atoms' importance for a desired function can be used within any graph neural network to provide interpretability of the results and predictions at the node level.

A Volcano Correlation between Catalytic Activity for Sulfur Reduction Reaction and Fe Atom Count in Metal Center.

Sulfur reduction reaction (SRR) facilitates up to 16 electrons, which endows lithium-sulfur (Li-S) batteries with a high energy density that is twice that of typical Li-ion batteries. However, its sluggish reaction kinetics render batteries with only a low capacity and cycling life, thus remaining the main challenge to practical Li-S batteries, which require efficient electrocatalysts of balanced atom utilization and site-specific requirements toward highly efficient SRR, calling for an in-depth understanding of the atomic structural sensitivity for the catalytic active sites. Herein, we manipulated the number of Fe atoms in iron assemblies, ranging from single Fe atom to diatomic and triatomic Fe atom groupings, all embedded within a carbon matrix. This led to the revelation of a "volcano peak" correlation between SRR catalytic activity and the count of Fe atoms at the active sites. Utilizing operando X-ray absorption and X-ray diffraction spectroscopies, we observed that polysulfide adsorption-desorption and electrochemical conversion kinetics varied up and down with the incremental addition of even a single iron atom to the catalyst's metal center. Our results demonstrate that the metal center with exactly two iron atoms represents the optimal configuration, maximizing atom utility and adeptly handling the conversion of varied intermediate sulfur species, rendering the Li-S battery with a high areal capacity of 23.8 mAh cm-2 at a high sulfur loading of 21.8 mg cm-2. Our results illuminate the pivotal balance between atom utilization and site-specific requirements for optimal electrocatalytic performance in SRR and diverse electrocatalytic reactions.

Accelerating material property prediction using generically complete isometry invariants

Periodic material or crystal property prediction using machine learning has grown popular in recent years as it provides a computationally efficient replacement for classical simulation methods. A crucial first step for any of these algorithms is the representation used for a periodic crystal. While similar objects like molecules and proteins have a finite number of atoms and their representation can be built based upon a finite point cloud interpretation, periodic crystals are unbounded in size, making their representation more challenging. In the present work, we adapt the Pointwise Distance Distribution (PDD), a continuous and generically complete isometry invariant for periodic point sets, as a representation for our learning algorithm. The PDD distinguished all (more than 660 thousand) periodic crystals in the Cambridge Structural Database as purely periodic sets of points without atomic types. We develop a transformer model with a modified self-attention mechanism that combines PDD with compositional information via a spatial encoding method. This model is tested on the crystals of the Materials Project and Jarvis-DFT databases and shown to produce accuracy on par with state-of-the-art methods while being several times faster in both training and prediction time.

Micromechanical effects of substrate hardness on graphene nano-cutting quality

In the nanocutting process of graphene, the mechanism on the atomic scale of the effect of substrate hardness on the edge quality of graphene nanoribbons has not been thoroughly investigated. In this paper, we construct a diamond probe (radius of 5Å) to cut graphene (190Å × 113Å) models with different substrates (silicon/sapphire/silicon carbide), molecular dynamics simulations were used to study the effect of substrate hardness and cutting depth on the atomic structure of graphene nanoribbon edges by observing the atomic morphology, the number of C-C bonds, the radial distribution function and friction. The results show that during the nanoindentation stage, when graphene is in the critical damage state, the substrate with high hardness has high deformation strength, the substrate deformation is small, and graphene undergoes deformation is also small, but the force Fz on the probe, the critical graphene stress σz and the equivalent stress are all large, and the number of C atoms with CN=3 is large; At the nano-cutting stage, the breakage of C-C bonds and the generation of edge defects are caused by local stress concentration, and the graphene stress σxx along the x-direction has a clear directionality. With the same cutting depth (critical damage depth of silicon substrate), silicon substrate hardness is small, the graphene nanoribbon obtained has less number of circular defects generated at the edge, less total number of defective atoms, more number of CN=3 atoms, large peak of radial distribution function of graphene after cutting, and graphene integrity is better. The hardness of the silicon carbide substrate is large, and the number of C-C bond breaks in graphene during the cutting process is high. Each substrate is cut at its own critical damage depth, and the substrate hardness has no significant effect on the number of C-C bond breaks, the number of circular defects, the total number of defective atoms, and the number of CN=3 atoms in graphene. When the silicon substrate was used, the stable force of the probe is small, the edge structure of graphene has little impact, the edge effect obtained is good. The cutting depth and substrate hardness have little effect on the graphene temperature during the nano-cutting process. In this paper, the atomic structure of graphene edges after cutting is analysed and investigated on the atomic scale, and the above results are combined to provide theoretical guidance for obtaining efficient and high-quality and low-damage graphene nanoribbon cutting.

Prediction of a new 2D Janus monolayer MX2Si2Y2 (M = Mo, W and V; X/Y = N, P; X ≠ Y) from density functional theory

We devised the Janus monolayer MX2A2Y2 (M = Mo, W, V, Nb, Ta, Ti, Zr, Hf and Cr; X/Y = N, P and As; A = Si and Ge) and systematically investigated the structural parameters, phonon vibrations, thermal stability, the electronic and mechanical properties. Among the 26 systems, there are six stable Janus monolayers at room temperature: MoN2Si2P2, WN2Si2P2, VN2Si2P2, MoP2Si2N2, WP2Si2N2, CrN2Si2P2. Intriguingly, the MoP2Si2N2 and WP2Si2N2 show non-magnetic semiconductor properties. The MoN2Si2P2 and WN2Si2P2 demonstrate non-magnetic metallic ground states. The VN2Si2P2 and CrN2Si2P2 are magnetic metal with magnetic moments of 1.24 μB and 0.53 μB. Furthermore, the elastic constants of the MSi2N4 and five Janus monolayer MX2Si2Y2 (M = Mo, W and V; X/Y = N, P) meet the stability criteria, indicating they are mechanically stable. The in-plane stiffness of the MX2Si2Y2 increases with the increase of the number of metal atoms and are greatly reduced, compared with the corresponding MSi2N4. In addition, according to Poisson's ratio, the MN2Si2P2 with metallic characteristics are ductile, whereas the MP2Si2N2 with semiconductor characteristic are brittle. In short, the Janus monolayer MX2Si2Y2 enriches the family members of 2D materials. The rich electronic and mechanical properties make the MX2Si2Y2 have high application prospects for future novel mechanical electronic device applications.

A new method for characterizing the atomic composition distribution and interface structure of ultrathin multilayer films using molecular dynamics simulation assisted ARXPS

Characterizing ultrathin multilayer films' atom distribution and interface structure remains challenging. This paper innovatively characterized the chemical composition and distribution of Pt/Co/W ultrathin multilayer films by ARXPS. Molecular dynamics simulated the growth process of films to correspond to that in experiment. The results show that through ARXPS, the actual structure of Pt (2 nm)/Co (t = 0.5–3 nm)/W (2 nm) film could be obtained. For Pt (2 nm)/Co (2 nm)/W (2 nm) film, the actual structure was Pt/Co (1.73 nm)/W (0.78 nm)/WO3 + WO2 (1.48 nm). W at the top layer significantly diffused to the bottom layers, and oxidized W reduced from the surface to the interior of multilayer films. Molecular dynamics simulation confirmed that the interdiffusion degree of Co and W atoms was improved with the increase of Co atoms. The growth modes of Co and W layers were respectively layer-by-layer and island. As the number of Co atoms increased, the relative surface roughness of Co and W layers and the relative interface roughness of Co/W were increased. The in-plane magnetic anisotropy and coercivity were respectively enhanced and decreased due to the change in the surface/interface structure. This paper provides a new idea for analyzing ultrathin multilayer films' atom distribution and interface structure.

Genotoxicity assessments of N-nitrosoethylisopropylamine (NEIPA) and N-nitrosodiisopropylamine (NDIPA) in the C57BL/6J mouse

N-Nitrosamines, known as drug impurities and suspected carcinogens, have drawn significant public concern. In response to drug regulatory needs, the European Medicines Agency (EMA) has previously proposed a carcinogenic potency categorization approach based on the N-nitrosamine α-hydroxylation hypothesis, i.e., that N-nitrosamine mutagenicity increases with the number of α-hydrogen atoms. However, this structure-activity relationship has not been fully tested in vivo. NEIPA (N-nitrosoethylisopropylamine) and NDIPA (N-nitrosodiisopropylamine) are small N-Nitrosamines with similar structures, differing in that the former compound has an additional α-hydrogen atom. In this study, NEIPA and NEIPA doses, 25–100 mg/kg, were administered orally to C57BL/6 J mice for seven consecutive days, and their mutation and DNA damage effects were compared. Compared with NDIPA, the mutagenicity and DNA damage potencies of NEIPA (which contains one more α-hydrogen) were much greater. These differences may be related to their distinct metabolic pathways and target organs. This case study confirms the role of α-hydroxyl modification in the mutagenicity of nitrosamines, with oxidation at the α-hydrogen being a crucial step in the formation of mutagens from N-Nitrosamines, and can inform mutagenicity risk assessment and the formulation of regulatory standards for N-nitrosamine impurities.

Benzodipyrrolidone-based Donor-Acceptor Semiconducting Polymers with High Hole Mobility and Noncovalent Conformational Locks

Constructing highly coplanar polymer backbone plays an important role in boosting the carrier mobility of organic field effect transistors (OFETs) devices. In this study, based on noncovalent conformational locks strategy, we synthesized the electron-acceptor (A) monomer via introducing 3-dodecylthiophene on strong electron-deficient planar benzodipyrrolidone and the electron-donor (D) monomers containing different numbers (0-2) of fluorine atoms at different positon. A series of novel D-A copolymers, namely PBDP-F0, PBDP-F1 and PBDP-F2 with different backbone conformational locks through F···S and F···H-C noncovalent interactions, were obtained via Stille polycondensation. For the application in an organic field-effect transistor, all the polymers exhibited p-type charge transport characteristics and good performance with the highest hole mobilities as 0.24 cm2 V-1 s-1 for PBDP-F0, 0.87 cm2 V-1 s-1 for PBDP-F1 and 1.93 cm2 V-1 s-1 for PBDP-F2, respectively. AFM and GIXRD investigations indicated that all polymers take predominantly edge-on orientation packing mode because of the rigid and coplanar backbone conformation and the annealing films exhibited small π-π stacking distances of 3.73, 3.71 and 3.69 Å for PBDP-F0, PBDP-F1 and PBDP-F2 respectively. This work demonstrates that modified BDP-based D-A copolymers with conformational locks has a potential application in the high-performance semiconductor materials.

Optoelectronic and transport properties of Lead-Free halide based Na2AgTlZ6 (Z = Cl, Br, I) double perovskites for energy harvesting applications

The bandgap engineering and deliberate band edge manipulation of perovskites make them potential candidates for the photovoltaic industry. Here we theoretically investigate the structure, density of states, and photovoltaic and thermoelectric characteristics of Na2AgTlZ6 (Z = Cl, Br, I) using WIEN2k code. The incorporation of Br and I at the Cl site caused the increase in lattice constant which leads to the decrease in bulk modulus and Debye temperature. The computed values of Poisson (<0.26) and Pugh (<1.75) ratio verified their brittle nature. The bandgap analysis reveals direct bandgap nature and bandgap value lie in the visible region for parent composition and infrared and far infrared when Cl is replaced with Br and I. The density of state plots uncovers the shifting of energy states in valence and conduction bands towards the Fermi level with the increasing number of electrons in the halogens. The increase in the value of dielectric constant, refractive index, and shifting of peaks towards lower energy value is witnessed with atomic number of halogen atoms. The viability of these compositions for thermoelectric devices is further confirmed when electrical conductivity and Seebeck coefficient dependent Figure of merit and power factor are evaluated in the temperature range of 200–800 K. In addition, the anisotropic nature of studied compositions is explored through the computation of the modulus of elasticity including Young’s modulus, Shear modulus in addition to compressibility, and Poisson’s ratio.

Nature of C-C coupling and strategy of tuning the catalytic activity of Cu-N-C catalysts for electro-reduction of CO2 to ethanol

With high atomic utilization and remarkable catalytic activity, Cu-N-C type catalysts display great potential for electro-catalysis in CO2 reduction. However, the relationship between the active moiety and catalytic activity of generating high-value C2 products is still unclear, and the explicit screening criteria is scarcity. Herein, based on the first-principle simulation, the structure-performance relationship on Cu-N-C type catalysts has been investigated by modulating the CO2 reduction process as the number of Cu atom (Cu1, Cu2, Cu3) and the ligand environment (B, C, N, O, P, S) changed. We find the adsorption strength of intermediate *CO strongly affect the possibility of C-C coupling, which can be determined by Bader charge on Cu atom, mainly depending on the number of loaded atomic Cu on Cu-N-C catalysts. Furthermore, the Bader charge can be refined by adjusting the coordination atom of Cu, thus optimizing catalytic activity for the CO2 to ethanol. The moderate Bader charge value, between +0.35 and +0.45, enables the catalyst to behave as a potentially excellent activity with low limiting potential for generating ethanol. More importantly, an intrinsic descriptor, composed of the radius, electronegativity, and number of valence electrons of coordination atoms (φ=∑χ∑r⁎∑np), was established to characterize the catalytic activity of Cu-N(X)-C catalysts for producing ethanol. Two excellent catalysts, Cu3-N2O2 (-0.51V) and Cu3-N3S (-0.64V), are screened out for the CO2RR to generate ethanol. This work discloses theoretical basis for catalytic selectivity of C2 products on Cu-N-C catalysts and provides a regulating and screening principle for high performance catalysts to ethanol.

Sphingolipid profiling reveals differential functions of sphingolipid biosynthesis isozymes of C. elegans

Multiple isozymes are encoded in the C. elegans genome for the various sphingolipid biosynthesis reactions, but the contributions of individual isozymes are characterized only in part. We developed a simple but effective reversed-phase liquid chromatography-tandem mass spectrometry (RPLC-MS/MS) method that enables simultaneous identification and quantification of ceramides (Cer), glucosylceramides (GlcCer), and sphingomyelins (SM), three important classes of sphingolipids from the same MS run. Validating this sphingolipid profiling method, we show that nearly all 47 quantifiable sphingolipid species found in young adult worms were reduced upon RNA interference (RNAi) of sptl-1 or elo-5, which are required for synthesis of the id17:1 sphingoid base. We also confirm that HYL-1 and HYL-2, but not LAGR-1, constitute the major ceramide synthase activity with different preference for fatty acid substrates, and that CGT-3, but not CGT-1 and CGT-2, plays a major role in producing glucosylceramides. Deletion of sms-5 hardly affects SM levels. RNAi of sms-1, -2, and -3 all lower the abundance of sphingomyelins with an odd number of carbon atoms (mostly C21 and C23, with or without hydroxylation) in the N-acyl chain, and only sms-1 RNAi does not elevate sphingomyelins containing even-numbered N-acyl chains. This suggests that sphingolipids containing even-numbered N-acyl chains could be regulated separately, sometimes in opposite directions, with those containing odd-numbered N-acyls, presumably monomethyl branched chain fatty acyls. We also find that ceramide levels are kept in balance with those of glucosylceramides and sphingomyelins.