Common Experimental Techniques Research Articles

Tricritical behavior of the two-dimensional intrinsically ferromagnetic semiconductor CrGeTe3

CrGeTe3 recently emerges as a new two-dimensional (2D) ferromagnetic semiconductor that is promising for spintronic device applications. Unlike CrSiTe3 whose magnetism can be understood using the 2D-Ising model, CrGeTe3 exhibits a smaller van der Waals gap and larger cleavage energy, which could lead to a transition of magnetic mechanism from 2D to 3D. To confirm this speculation, we investigate the critical behavior CrGeTe3 around the second-order paramagnetic-ferromagnetic phase transition. We obtain the critical exponents estimated by several common experimental techniques including the modified Arrott plot, Kouvel-Fisher method and critical isotherm analysis, which show that the magnetism of CrGeTe3 follows the tricritical mean-field model with the critical exponents \b{eta}, {\gamma}, and {\delta} of 0.240, 1.000, and 5.070, respectively, at the Curie temperature of 67.9 K. We therefore suggest that the magnetic phase transition from 2D to 3D for CrGeTe3 should locate near a tricritical point. Our experiment provides a direct demonstration of the applicability of the tricritical mean-field model to a 2D ferromagnetic semiconductor.

Collective motion of bacteria and their dynamic assembly behavior

In recent years, active matter systems have attracted considerable attentions due to their complex dynamic behaviors in physical and material science. In particular, microorganism systems have served as model systems for observing dynamic assembly and collective motility of active particles and significant progresses have been made on in-depth understanding of how high density bacteria colony behaves in the non-equilibrium state. In this mini-review, we mainly focus on the collective motion of bacteria and their dynamic assembly from four aspects: (1) the general phenomenon and biological mechanism of bacterial collective motion; (2) the common experimental techniques for studying bacterial motility; (3) some active systems on exploring bacterial collective behavior, which include both non-restricted free suspensions and those in relative confined geometric space; (4) the phenomenological and descriptive statistical methods and physical models on the underlying laws that lead to large-scale coordinate patterns in multicellular systems. This review aims to give a general picture of the collective motion in bacterial active matter systems experimentally and theoretically in order to reflect the interplays between individuals among populations in motion. It is expected that the general regulation rules related to the boundary effects in the complex systems and materials can be elucidated to some extent.

Review—Physicochemical Hydrodynamics of Gas Bubbles in Two Phase Electrochemical Systems

Electrochemical systems suffer from poor management of evolving gas bubbles. Improved understanding of bubbles behavior helps to reduce overpotential, save energy and enhance the mass transfer during chemical reactions. This work investigates and reviews the gas bubbles hydrodynamics, behavior, and management in electrochemical cells. Although the rate of bubble growth over the electrode surface is well understood, there is no reliable prediction of bubbles break-off diameter from the electrode surface because of the complexity of bubbles motion near the electrode surface. Particle Image Velocimetry (PIV) and Laser Doppler Anemometry (LDA) are the most common experimental techniques to measure bubble dynamics. Although the PIV is faster than LDA, both techniques are considered expensive and time-consuming. This encourages adapting Computational Fluid Dynamics (CFD) methods as an alternative to study bubbles behavior. However, further development of CFD methods is required to include coalescence and break-up of bubbles for better understanding and accuracy. The disadvantages of CFD methods can be overcome by using hybrid methods. The behavior of bubbles in electrochemical systems is still a complex challenging topic which requires a better understanding of the gas bubbles hydrodynamics and their interactions with the electrode surface and bulk liquid, as well as between the bubbles itself.

A Simple Model for Interpreting the Reaction–Diffusion Characteristics of Li-Air Batteries

With the goal of creating a model of a Li-air battery that is consistent with voltammetry data, we develop a full battery model capable of giving insight into details of cell operation otherwise inaccessible to common experimental techniques. With this model, we investigate the dependence of the current on: the diffusion characteristics of the electrolyte, the solubility of the ambient oxygen, the structure of the cathode, and aspects of the primary surface reaction. We explore modifications to a basic reaction-diffusion model of a full cell that bring better agreement with experimental data, including gas-electrolyte surface limited diffusion and a partially active cathode. We discuss how the basic form of the model and the simulated reaction and concentration profiles affect cell dynamics.

Refining Disordered Peptide Ensembles with Computational Amide I Spectroscopy: Application to Elastin-Like Peptides.

The characterization of intrinsically disordered protein (IDP) ensembles is complicated both by inherent heterogeneity and by the fact that many common experimental techniques function poorly when applied to IDPs. For this reason, the development of alternative structural tools for probing IDP ensembles has attracted considerable attention. Here we describe our recent work in developing experimental and computational tools for characterizing IDP ensembles using Amide I (backbone carbonyl stretch) vibrational spectroscopy. In this approach, the infrared (IR) absorption frequencies of isotope-labeled amide bonds probe their local electrostatic environments and structures. Empirical frequency maps allow us to use this spectroscopic data as a direct experimental test of atomistic structural models. We apply these methods to a family of short elastin-like peptides (ELPs), fragments of the elastin protein based around the Pro-Gly turn motif characteristic of the elastomeric segments of the full protein. Using a maximum entropy analysis that applies constraints from experimental spectra to weighting predicted spectra from molecular dynamics (MD) ensembles, we find that peptides with Ala or Val side chains preceding the Pro-Gly turn unit exhibit a stronger tendency toward extended structures than do Gly-Pro-Gly motifs, suggesting an important role for steric interactions in tuning the molecular properties of elastin.

Abstract # 1771 Experimental procedures performed sequentially exert a stress that activates caspase-1 and impairs memory in mice

Various forms of psychological and physical stress, such as restraint and chronic mild stress, have been shown to activate the neuroimmune system and lead to cognitive impairment and depressive-like behaviors. Additionally, it has been shown that minor environmental changes, and even changes in gender, can induce stress responses in mice. Many of these models show increases in the pro-inflammatory cytokine IL-1 β . IL-1 β is produced in an inactive form and requires activation by caspase-1, which is activated by the sensing of a danger signal by the inflammasome complex. However, the danger signals responsible for these changes remains unknown. Here, we show that a novel stressor paradigm of common experimental procedures (weighing, scruffing, and needle pricks) performed sequentially can activate caspase-1 and impair memory which is dependent on adenosine signaling. 6 hr of stress increases caspase-1 activity 2–3 fold in the amygdala, hippocampus, and prefrontal cortex and impairs novel object recognition. However, 3 hr of stress was unable to change object recognition memory. Additionally, stress-induced memory impairment and caspase-1 activation can be blocked by IP administration of the pan-adenosine receptor antagonist caffeine. Finally, we show that stress-induced memory impairment can also be blocked by genetic knockout of the adenosine A2 receptor (A2AR). Taken together, these data show that common experimental techniques can exert a stress response in mice that is dependent on adenosine-induced caspase-1 activation.

Dominant negative inhibition data should be analyzed using mathematical modeling--re-interpreting data from insulin signaling.

As our ability to measure the complexity of intracellular networks has evolved, it has become increasingly clear that we need new methods for data analysis: methods involving mathematical modeling. Nevertheless, it is still uncontroversial to publish and interpret experimental results without a model-based proof that the reasoning is correct. In the present study, we argue that this attitude probably needs to change in the future. We illustrate this need for modeling by considering the common experimental technique of using dominant-negative constructs. More specifically, we consider published time-series and dose-response data which previously have been used to argue that the protein S6 kinase does not phosphorylate insulin receptor substrate-1 at a specific serine residue. Using a presented general approach to interpret such data, we now demonstrate that the given dominant-negative data are not conclusive (i.e. that in the absence of other proofs, S6 kinase still may be the kinase). Using simulations with uncertainty analysis and analytical solutions, we show that an alternative explanation is centered around depletion of substrate, which can be tested experimentally. This analysis thus illustrates both the necessity and the benefits of using mathematical modeling to fully understand the implications of biological data, even for a small system and relatively simple data.

Initial Responses in Strategic Situations

In this chapter we analyze the behavior of individuals when facing strategic situations for the first time. First, we motivate our study through simple examples where, for different reasons, behavior may differ from the Nash equilibrium prediction. Subsequently, we describe different models that have been used to account for alternative types of behavior in novel situations. Finally, we explain the most common experimental techniques used to analyze the validity of such models in different strategic situations.

The speed-accuracy tradeoff: history, physiology, methodology, and behavior.

There are few behavioral effects as ubiquitous as the speed-accuracy tradeoff (SAT). From insects to rodents to primates, the tendency for decision speed to covary with decision accuracy seems an inescapable property of choice behavior. Recently, the SAT has received renewed interest, as neuroscience approaches begin to uncover its neural underpinnings and computational models are compelled to incorporate it as a necessary benchmark. The present work provides a comprehensive overview of SAT. First, I trace its history as a tractable behavioral phenomenon and the role it has played in shaping mathematical descriptions of the decision process. Second, I present a “users guide” of SAT methodology, including a critical review of common experimental manipulations and analysis techniques and a treatment of the typical behavioral patterns that emerge when SAT is manipulated directly. Finally, I review applications of this methodology in several domains.

Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach

BackgroundAccurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. This problem is intimately linked to identifying the most informative experiments for accomplishing such tasks. While significant progress has been made, effective experimental strategies for parameter identification and for distinguishing among alternative network topologies remain unclear. We approached these questions in an unbiased manner using a unique community-based approach in the context of the DREAM initiative (Dialogue for Reverse Engineering Assessment of Methods). We created an in silico test framework under which participants could probe a network with hidden parameters by requesting a range of experimental assays; results of these experiments were simulated according to a model of network dynamics only partially revealed to participants.ResultsWe proposed two challenges; in the first, participants were given the topology and underlying biochemical structure of a 9-gene regulatory network and were asked to determine its parameter values. In the second challenge, participants were given an incomplete topology with 11 genes and asked to find three missing links in the model. In both challenges, a budget was provided to buy experimental data generated in silico with the model and mimicking the features of different common experimental techniques, such as microarrays and fluorescence microscopy. Data could be bought at any stage, allowing participants to implement an iterative loop of experiments and computation.ConclusionsA total of 19 teams participated in this competition. The results suggest that the combination of state-of-the-art parameter estimation and a varied set of experimental methods using a few datasets, mostly fluorescence imaging data, can accurately determine parameters of biochemical models of gene regulation. However, the task is considerably more difficult if the gene network topology is not completely defined, as in challenge 2. Importantly, we found that aggregating independent parameter predictions and network topology across submissions creates a solution that can be better than the one from the best-performing submission.

Targeted training of the decision rule benefits rule-guided behavior in Parkinson’s disease

The impact of Parkinson's disease (PD) on rule-guided behavior has received considerable attention in cognitive neuroscience. The majority of research has used PD as a model of dysfunction in frontostriatal networks, but very few attempts have been made to investigate the possibility of adapting common experimental techniques in an effort to identify the conditions that are most likely to facilitate successful performance. The present study investigated a targeted training paradigm designed to facilitate rule learning and application using rule-based categorization as a model task. Participants received targeted training in which there was no selective-attention demand (i.e., stimuli varied along a single, relevant dimension) or nontargeted training in which there was selective-attention demand (i.e., stimuli varied along a relevant dimension as well as an irrelevant dimension). Following training, all participants were tested on a rule-based task with selective-attention demand. During the test phase, PD patients who received targeted training performed similarly to control participants and outperformed patients who did not receive targeted training. As a preliminary test of the generalizability of the benefit of targeted training, a subset of the PD patients were tested on the Wisconsin card sorting task (WCST). PD patients who received targeted training outperformed PD patients who did not receive targeted training on several WCST performance measures. These data further characterize the contribution of frontostriatal circuitry to rule-guided behavior. Importantly, these data also suggest that PD patient impairment, on selective-attention-demanding tasks of rule-guided behavior, is not inevitable and highlight the potential benefit of targeted training.

Application and evaluation of the method of ellipses for measuring the orientation of long, semi-flexible fibers

The method of ellipses (MoE) is a common experimental technique utilized to quantitatively determine the orientation state of a population of rigid fibers within a fiber–polymer composite. In this research, the validity of applying the MoE to long, semi-flexible fiber systems in which the majority of fibers are flexible is discussed. The components of the orientation tensor were first determined for a composite formed by a homogenous, simple shear field. The minimum acceptable image analysis width, or bin width, for the selected geometry was found to be ∼5.5 mm, or 1.4 times the average fiber length. This modified bin width was then used to determine the orientation at multiple percentages of flow within an injection-molded, center-gated disc, and compared to orientation values obtained utilizing the traditional, 0.7-mm bin width. The results show that the traditional, 0.7-mm bin width is sufficient for analysis of the center-gated geometry. This fortuitous result is attributed to the axisymmetric nature of the center-gated geometry, and the highly transverse fiber alignment seen within the samples, especially at moderate to high percentages of flow. In more complex flows, it is expected that the conventional bin width will not apply. POLYM. COMPOS., 2013. © 2013 Society of Plastics Engineers

Oxygen gain analysis for proton exchange membrane fuel cells

Oxygen gain is the difference in hydrogen fuel cell performance operating on oxygen-depleted and oxygen-rich cathode fuel streams. Oxygen gain experiments provide insight into the degree of oxygen mass-transport resistance within a fuel cell. By taking these measurements under different operating conditions, or over time, one can determine how oxygen mass transport varies with operating modes and/or aging. This paper provides techniques to differentiate between mass-transport resistance within the catalyst layer and within the gas-diffusion medium for a polymer-electrolyte membrane fuel cell. Two extreme cases are treated in which all mass transfer limitations are located only ( i) within the catalyst layer or ( ii) outside the catalyst layer in the gas-diffusion medium. These two limiting cases are treated using a relatively simple model of the cathode potential and common oxygen gain experimental techniques. This analysis demonstrates decisively different oxygen gain behavior for the two limiting cases. For catalyst layer mass transfer resistance alone, oxygen gain values are limited to a finite range of values. However, for gas-diffusion layer mass transfer resistance alone, the oxygen gain is not confined to a finite range of values. Therefore, this work provides a straightforward diagnostic method for locating the prominent source of mass transfer degradation in a PEMFC cathode.

On the Measurement of Fiber Bragg Grating's Phase Responses and the Applicability of Phase Reconstruction Methods

A signal processing algorithm is used to reconstruct the phase response of fiber Bragg gratings from easy-to-obtain module data. The algorithm is applied to optical spectrum analyzer measurements, avoiding the use of costly and complex equipment for the direct measurement of the complete frequency response. Reconstructed results, both in transmission and reflection, are then validated by comparison with three of the most common experimental techniques (interferometric, modulation phase shift, and single-sideband modulation).

ChemInform Abstract: The Role of Synchrotron Radiation in Examining the Self‐Assembly of Crystalline Nanoporous Framework Materials: From Zeolites and Aluminophosphates to Metal Organic Hybrids

AbstractReview: 80 refs.

Lattice Boltzmann simulations of liquid film drainage between smooth surfaces

Exploring the hydrodynamic boundary of a surface by approaching a colloidal sphere and measuring the occurring drag force is a common experimental technique. However, numerous parameters like the wettability and surface roughness influence the result. In experiments these cannot be separated easily. For a deeper understanding of such surface effects a tool is required that predicts the influence of different surface properties. In this paper we present computer simulations based on the lattice Boltzmann method of a sphere submerged in a Newtonian liquid. We show that our method is able to reproduce the theoretical predictions for flat and noninteracting surfaces. In order to provide high precision simulation results the influence of finite size effects has to be well controlled. Therefore we study the influence of the required system size and resolution of the sphere and demonstrate that already moderate computing resources allow the error to be kept below 1%.

Numerical analysis of steady-state and transient charge transport in organic semiconductor devices

A one-dimensional numerical model for the simulation of organic semiconductor devices such as organic light-emitting devices and solar cells is presented. The model accounts for the energetic disorder in organic semiconductors and assumes that charge transport takes place by a hopping process between uncorrelated sites. Therefore a Gaussian density of states and the use of the Fermi-Dirac statistics are introduced. The model includes density-, field- and temperature- dependent mobilities as well as the generalized Einstein relation. The numerical methods to solve the underlying drift-diffusion problem perform well in combination with the novel physical model ingredients. We demonstrate efficient numerical techniques that we employ to simulate common experimental characterization techniques such as current-voltage, dark-injection transient and electrical impedance measurements. This is crucial for physical model validation and for material parameter extraction. We also highlight how the numerical solution of the novel model differs from the analytical solution of the simplified drift-only model.

Tumour angiogenesis: The gap between theory and experiments

A common experimental technique for viewing in vivo angiogenesis utilises tumours implanted into a test animal cornea. The cornea is avascular but the tumour promotes vascularisation from the limbus and the new blood vessels can be readily observed through the transparent cornea. Many of the early mathematical models for tumour angiogenesis used this scenario as their experimental template and as such assumed that there is a large gap, of the order of 2mm, between the tumour and neighbouring vasculature at the onset of angiogenesis. In this work we consider whether the assumption that there is a significant gap between the tumour and neighbouring vasculature is unique to intra-cornea tumour implants, or whether this characterises avascular tumour growth more generally. To do this we utilise a simple scaling argument, derive a multi-compartment model for tumour growth, and consider in vivo images. This analysis demonstrates that the corneal implant experiments and the corresponding mathematical models cannot generally be applied to a clinical setting.

Ordering and site occupancy of D03 ordered Fe3Al–5 at%Cr evaluated by means of atom probe tomography

Addition of ternary elements to the D0(3) ordered Fe(3)Al intermetallic phase is a general approach to optimise its mechanical properties. To understand the physical influences of such additions the determination of the probability of site occupancies of these additions on the lattice site and ordering parameters is of high interest. Some common experimental techniques such as X-ray diffraction or Atom Location by Channelling Enhanced Microanalysis (ALCHEMI) are usually applied to explore this interplay. Unfortunately, certain published results are partly inconsistent, imprecise or even contradictory. In this study, these aspects are evaluated systematically by atom probe tomography (APT) and a special data analysis method. Additionally, to account for possible field evaporation effects that can falsify the estimation of site occupancy and induce misinterpretations, APT evaporation sequences were also simulated. As a result, chromium occupies most frequently the next nearest neighbour sites of Al atoms and local ordering parameters could be achieved.

Generalized Kearns texture factors and orientation texture measurement

A generalization of the Kearns texture factors is presented. Mathematical relationships are derived that relate the generalized texture factors to other measures commonly used in quantitative texture analysis. In addition, values of the generalized texture factors for random orientation texture, numerical bounds for the texture factors, and estimates for experimental uncertainty are given. Kearns’ method for measuring texture factors from θ − 2 θ X-ray diffraction scans is extended for use in measuring higher order texture factors. A comparison of generalized texture factors measured through three common experimental techniques is presented.