S-cysteinylated albumin and methionine-oxidized apolipoprotein A-I (apoA-I) have been posed as candidate markers of diseases associated with oxidative stress. Here, a dilute-and-shoot form of LC–electrospray ionization–MS requiring half a microliter of blood plasma was employed to simultaneously quantify the relative abundance of these oxidized proteoforms in samples stored at −80 °C, −20 °C, and room temperature and exposed to multiple freeze-thaw cycles and other adverse conditions in order to assess the possibility that protein oxidation may occur as a result of poor sample storage or handling. Samples from a healthy donor and a participant with poorly controlled type 2 diabetes started at the same low level of protein oxidation and behaved similarly; significant increases in albumin oxidation via S-cysteinylation were found to occur within hours at room temperature and days at −20 °C. Methionine oxidation of apoA-I took place on a longer time scale, setting in after albumin oxidation reached a plateau. Freeze–thaw cycles had a minimal effect on protein oxidation. In matched collections, protein oxidation in serum was the same as that in plasma. Albumin and apoA-I oxidation were not affected by sample headspace or the degree to which vials were sealed. ApoA-I, however, was unexpectedly found to oxidize faster in samples with lower surface-area-to-volume ratios. An initial survey of samples from patients with inflammatory conditions normally associated with elevated oxidative stress-including acute myocardial infarction and prostate cancer—demonstrated a lack of detectable apoA-I oxidation. Albumin S-cysteinylation in these samples was consistent with known but relatively brief exposures to temperatures above −30 °C (the freezing point of blood plasma). Given their properties and ease of analysis, these oxidized proteoforms, once fully validated, may represent the first markers of blood plasma specimen integrity based on direct measurement of oxidative molecular damage that can occur under suboptimal storage conditions.

In recent years, a substantial amount of research has been focused on identifying suitable interfacial layers in organic light-emitting diodes and organic solar cells which has efficient charge transport properties. In this work, a very thin layer of AgOx is deposited on top of the ITO layer along with PEDOT:PSS and is observed that the solar cells having the AgOx interfacial layer showed a 28% increase in power conversion efficiency in comparison to that of the control cell. The enhancement in efficiency has been ascribed to improvements in fill factor as well as the increase in shunt resistance and decrease in the series resistance of the solar cells. An equivalent circuit model is also provided to understand the changes in the series and shunt resistances in the AgOx modified devices.

Extreme events, a type of collective behavior in complex networked dynamical systems, often can have catastrophic consequences. To develop effective strategies to control extreme events is of fundamental importance and practical interest. Utilizing transportation dynamics on complex networks as a prototypical setting, we find that making the network “mobile” can effectively suppress extreme events. A striking, resonance-like phenomenon is uncovered, where an optimal degree of mobility exists for which the probability of extreme events is minimized. We derive an analytic theory to understand the mechanism of control at a detailed and quantitative level, and validate the theory numerically. Implications of our finding to current areas such as cybersecurity are discussed.

We develop a completely data-driven approach to reconstructing coupled neuronal networks that contain a small subset of chaotic neurons. Such chaotic elements can be the result of parameter shift in their individual dynamical systems and may lead to abnormal functions of the network. To accurately identify the chaotic neurons may thus be necessary and important, for example, applying appropriate controls to bring the network to a normal state. However, due to couplings among the nodes, the measured time series, even from non-chaotic neurons, would appear random, rendering inapplicable traditional nonlinear time-series analysis, such as the delay-coordinate embedding method, which yields information about the global dynamics of the entire network. Our method is based on compressive sensing. In particular, we demonstrate that identifying chaotic elements can be formulated as a general problem of reconstructing the nodal dynamical systems, network connections and all coupling functions, as well as their weights. The working and efficiency of the method are illustrated by using networks of non-identical FitzHugh–Nagumo neurons with randomly-distributed coupling weights.

Signatures of nonlinear and non-Gaussian dynamics in time-resolved linear and nonlinear (correlation) 2D spectra are analyzed in a model considering a linear plus quadratic dependence of the spectroscopic transition frequency on a Gaussian nuclear coordinate of the thermal bath (quadratic coupling). This new model is contrasted to the commonly assumed linear dependence of the transition frequency on the medium nuclear coordinates (linear coupling). The linear coupling model predicts equality between the Stokes shift and equilibrium correlation functions of the transition frequency and time-independent spectral width. Both predictions are often violated, and we are asking here the question of whether a nonlinear solvent response and/or non-Gaussian dynamics are required to explain these observations. We find that correlation functions of spectroscopic observables calculated in the quadratic coupling model depend on the chromophore’s electronic state and the spectral width gains time dependence, all in violation of the predictions of the linear coupling models. Lineshape functions of 2D spectra are derived assuming Ornstein–Uhlenbeck dynamics of the bath nuclear modes. The model predicts asymmetry of 2D correlation plots and bending of the center line. The latter is often used to extract two-point correlation functions from 2D spectra. The dynamics of the transition frequency are non-Gaussian. However, the effect of non-Gaussian dynamics is limited to the third-order (skewness) time correlation function, without affecting the time correlation functions of higher order. The theory is tested against molecular dynamics simulations of a model polar–polarizable chromophore dissolved in a force field water.

Recently fabricated two-dimensional phosphorene crystal structures have demonstrated great potential in applications of electronics. In this paper, strain effect on the electronic band structure of phosphorene was studied using first-principles methods including density functional theory (DFT) and hybrid functionals. It was found that phosphorene can withstand a tensile stress and strain up to 10 N/m and 30%, respectively. The band gap of phosphorene experiences a direct-indirect-direct transition when axial strain is applied. A moderate −2% compression in the zigzag direction can trigger this gap transition. With sufficient expansion (+11.3%) or compression (−10.2% strains), the gap can be tuned from indirect to direct again. Five strain zones with distinct electronic band structure were identified, and the critical strains for the zone boundaries were determined. Although the DFT method is known to underestimate band gap of semiconductors, it was proven to correctly predict the strain effect on the electronic properties with validation from a hybrid functional method in this work. The origin of the gap transition was revealed, and a general mechanism was developed to explain energy shifts with strain according to the bond nature of near-band-edge electronic orbitals. Effective masses of carriers in the armchair direction are an order of magnitude smaller than that of the zigzag axis, indicating that the armchair direction is favored for carrier transport. In addition, the effective masses can be dramatically tuned by strain, in which its sharp jump/drop occurs at the zone boundaries of the direct-indirect gap transition.

Sliding clamps are ring-shaped oligomeric proteins that are essential for processive deoxyribonucleic acid replication. Although crystallographic structures of several clamps have been determined, much less is known about clamp structure and dynamics in solution. Here, we characterized the intrinsic solution stability and oligomerization dynamics of the homodimeric Escherichia coli β and the homotrimeric Saccharomyces cerevisiae proliferating cell nuclear antigen (PCNA) clamps using single-molecule approaches. We show that E. coli β is stable in solution as a closed ring at concentrations three orders of magnitude lower than PCNA. The trimeric structure of PCNA results in slow subunit association rates and is largely responsible for the lower solution stability. Despite this large difference, the intrinsic lifetimes of the rings differ by only one order of magnitude. Our results show that the longer lifetime of the E. coli β dimer is due to more prominent electrostatic interactions that stabilize the subunit interfaces.

The dopamine-TiO₂ system shows a specific spectroscopic response, surface enhanced Raman scattering (SERS), whose mechanism is not fully understood. In this study, the goal is to reveal the key role of the molecule–nanoparticle interface in the electronic structure by means of ab initio modeling. The dopamine adsorption energy on anatase surfaces is computed and related to changes in the electronic structure. Two features are observed: the appearance of a state in the material band gap, and charge transfer between molecule and surface upon electronic excitation. The analysis of the energetics of the systems would point to a selective adsorption of dopamine on the (001) and (100) terminations, with much less affinity for the (101) plane.

Two pentacoordinate mononuclear iron carbonyls of the form (bdt)Fe(CO)P₂ [bdt = benzene-1,2-dithiolate; P₂ = 1,1′-diphenylphosphinoferrocene (1) or methyl-2-{bis(diphenylphosphinomethyl)amino}acetate (2)] were prepared as functional, biomimetic models for the distal iron (Fe_d) of the active site of [FeFe]-hydrogenase. X-ray crystal structures of the complexes reveal that, despite similar ν(CO) stretching band frequencies, the two complexes have different coordination geometries. In X-ray crystal structures, the iron center of 1 is in a distorted trigonal bipyramidal arrangement, and that of 2 is in a distorted square pyramidal geometry. Electrochemical investigation shows that both complexes catalyze electrochemical proton reduction from acetic acid at mild overpotential, 0.17 and 0.38 V for 1 and 2, respectively. Although coordinatively unsaturated, the complexes display only weak, reversible binding affinity toward CO (1 bar). However, ligand centered protonation by the strong acid, HBF₄·OEt₂, triggers quantitative CO uptake by 1 to form a dicarbonyl analogue [1(H)-CO]⁺ that can be reversibly converted back to 1 by deprotonation using NEt₃. Both crystallographically determined distances within the bdt ligand and density functional theory calculations suggest that the iron centers in both 1 and 2 are partially reduced at the expense of partial oxidation of the bdt ligand. Ligand protonation interrupts this extensive electronic delocalization between the Fe and bdt making 1(H)⁺ susceptible to external CO binding.

The relation between flux and fluctuation is fundamental to complex physical systems that support and transport flows. A recently obtained law predicts monotonous enhancement of fluctuation as the average flux is increased, which in principle is valid but only for large systems. For realistic complex systems of small sizes, this law breaks down when both the average flux and fluctuation become large. Here we demonstrate the failure of this law in small systems using real data and model complex networked systems, derive analytically a modified flux-fluctuation law, and validate it through computations of a large number of complex networked systems. Our law is more general in that its predictions agree with numerics and it reduces naturally to the previous law in the limit of large system size, leading to new insights into the flow dynamics in small-size complex systems with significant implications for the statistical and scaling behaviors of small systems, a topic of great recent interest.