DFT studies on armchair (5, 5) SWCNT functionalization. Modification of selected structural and spectroscopic parameters upon two-atom molecule attachment

Density functional theory (DFT) studies on adsorption of several gaseous homo- and hetero-diatomic molecules (AB) including H₂, O₂, N₂, NO and CO on external surface of H-capped pristine armchair (5, 5) single-walled carbon nanotube (SWCNT) were conducted. Structures of C₇₀H₁₀ and the corresponding C₇₀H₁₀–AB adducts were fully optimized at the B3LYP/6-311G* level of theory. Calculated HOMO/LUMO energy gaps (E_g), ¹³C NMR chemical shifts and IR/Raman parameters were analyzed and critically compared with available experimental data. Significant changes of carbon NMR atom chemical shifts (up to −100 ppm) and shielding anisotropies (up to −180 ppm) at sites of addition were observed. Functionalized SWCNTs produced IR and Raman spectra different from the pristine nanotube model. The selective changes in vibrational spectra will help in assigning the topology of functionalization at the nanotube wall.     

Passive single chip wireless microwave pressure sensor

A novel micromachined passive wireless pressure sensor is presented. The device consists of a tuned circuit operating at 10 GHz fabricated on to a SiO₂ membrane, supported on a silicon wafer. A pressure difference across the membrane causes it to deflect so that an antenna circuit detunes. The circuit is remotely interrogated to read off the sensor data wirelessly. The chip area is 5 mm × 4 mm and the membrane area is 2 mm² with a thickness of 4 μm. Two on-chip passive resonant circuits were investigated: a meandered dipole and a zigzag antenna. Both have a physical length of 4.25 mm. The sensors show a shift in their resonant frequency in response to changing pressure of 10.28–10.27 GHz for the meandered dipole, and 9.61–9.58 GHz for the zigzag antenna. The sensitivities of the meandered dipole and zigzag sensors are 12.5 kHz/mbar and 16 kHz/mbar respectively.     

Separation based adsorption of ethanol–water mixture in azeotropic solution by single–walled carbon,boron-nitride and silicon-carbide nanotubes

The separation of the azeotropic ethanol-water mixture (95.57 wt% ethanol) over a wide range of pressures (100–100000 kPa) was studied on armchair SWCNTs, SWSiCNTs and SWBNNTs with different diameters at 351.30 K using GCMC simulations. The GCMC results demonstrated that ethanol and water molecules form a monolayer single-file, chain together in the center of (6,6) SWCNT, while a spiral ring of ethanol and water is formed in the center of (8,8), (10,10) and (12,12) SWCNTs. It was found that in SWCNTs, the adsorption of ethanol reduces the function of pressure, while water adsorption increases its function. Water selectivity rises as a function of pressure. Also, in SWBNNTs, the adsorption of water increases as a function of pressure, while ethanol adsorption is almost constant. However, in the case of SWSiCNTs, ethanol and water adsorptions are very similar to those of SWBNNTs, whereas the adsorptivities of SWSiCNTs are more than those of SWBNNTs. Our findings regarding adsorption and slope of adsorption indicate that higher pressures are favorable for separating water and ethanol by SWCNTs, while SWBNNTs and SWSiCNTs are demonstrate higher ethanol adsorptivities in lower pressures. Also, MD simulations have been performed to study the microscopic structure and diffusion of binary mixtures of water and ethanol within SWCNTs, SWSiCNTs and SWBNNTs. The MD simulations imply that the oxygen atoms are highly well-organized around themselves. Also, the MD results illustrate a similar tendency for oxygen of water (OW) and oxygen of ethanol (OE) to the wall of the nanotubes in all the pressures. In addition, from the MD results, self-diffusion of water and ethanol in all nanotubes were calculated and discussed.     

A micro shear stress sensor based on laterally aligned carbon nanotubes

This paper reports the development of a micro thermal shear stress sensor that utilizes multiwalled carbon nanotubes as the sensing element. The sensor was fabricated by laterally aligning randomly distributed nanotubes into a 360 μm long and 90 μm wide conductive trace between two triangular shaped micro electrodes through the use of a high frequency AC electric field. During operation, the aligned nanotubes are electrically heated to an elevated temperature and surface shear stress is measured indirectly by the amount of convective heat transfer from the heated nanotubes to the surrounding fluid flow.The nanotube alignment process was primarily controlled by three different phenomena: dielectrophoresis, joule heating, and Brownian motion. Numerical simulations, together with experimental verifications, indicated that a successful alignment could only be realized if: (1) the dielectrophoretic force was positive, (2) the electro-thermal force was also positive, and (3) the dielectrophoretic force was high enough to overcome Brownian motion. The aligned nanotube trace has a room-temperature resistance of 580 Ω, which corresponds to a conductivity of 2.7 × 10⁴ S/m. The absolute temperature coefficient of resistivity ranges from 0.01 to 0.04% °C⁻¹. This is about one order of magnitude smaller than the highly doped polysilicon sensing material used in the MEMS micro shear stress sensor. The shear stress sensitivity of the nanotube trace operated at a 3% overheat ratio is found to follow the theoretical sensor power ∝ (shear stress)^1/3 relationship, provided the shear stress level is higher than 0.34 mPa. This result confirms the feasibility of using aligned multi-walled carbon nanotubes as a thermal shear stress sensing material.     

Vibrational analysis of graphene based nanostructures

This paper deals with the molecular mechanics simulations of graphene nanostructures and their vibration behavior for potential applications on nano-electronics and nanocomposites. The fundamental frequencies for CNTs range from 10 to 250 GHz and 100 to 1000 GHz for the cantilevered and bridged conditions, respectively. As the ratio L/d increases the fundamental frequency decreases, as expected. A decrease on fundamental frequencies with the bending waviness was noticed for all conditions. The mode shape for bent carbon nanotubes seems to be a superposition of the vibration mode and the bending mode for the zigzag configuration. Multi-layered graphene nanosheets were also investigated. The fundamental frequencies ranged from 50 to 150 GHz, with an odd/even shape mode switch.     

Electrical properties of nickel oxide thin films for flow sensor application

In this work, Ni oxide thin films, with thermal sensitivity superior to Pt and Ni thin films, were formed through annealing of Ni films deposited by a r.f. magnetron sputtering. The annealing was carried out in the temperature range of 300–500 °C under atmospheric conditions. Resistivity of the resulting Ni oxide films were in the range of 10.5 μΩ cm/°C to 2.84 × 10⁴ μΩ cm/°C, depending on the extent of Ni oxidation. The temperature coefficient of resistance (TCR) of the Ni oxide films also depended on the extent of Ni oxidation; the average TCR of Ni oxide resistors, measured between 0 and 150 °C, were 5630 ppm/°C for the 300 °C and 2188 ppm/°C for 500 °C films. Because of their high resistivity and very linear TCR, Ni oxide thin films are superior to pure Ni and Pt thin films for flow and temperature sensor applications.     

Consensus scoring model for the molecular docking study of mTOR kinase inhibitor

The discovery of mammalian target of rapamycin (mTOR) kinase inhibitors has always been a research hotspot of antitumor drugs. Consensus scoring used in the docking study of mTOR kinase inhibitors usually improves hit rate of virtual screening. Herein, we attempt to build a series of consensus scoring models based on a set of the common scoring functions. In this paper, twenty-five kinds of mTOR inhibitors (16 clinical candidate compounds and 9 promising preclinical compounds) are carefully collected, and selected for the molecular docking study used by the Glide docking programs within the standard precise (SP) mode. The predicted poses of these ligands are saved, and revaluated by twenty-six available scoring functions, respectively. Subsequently, consensus scoring models are trained based on the obtained rescoring results by the partial least squares (PLS) method, and validated by Leave-one-out (LOO) method. In addition, three kinds of ligand efficiency indices (BEI, SEI, and LLE) instead of pIC₅₀ as the activity could greatly improve the statistical quality of build models. Two best calculated models 10 and 22 using the same BEI indice have following statistical parameters, respectively: for model 10, training set R² = 0.767, Q² = 0.647, RMSE = 0.024, and for test set R² = 0.932, RMSE = 0.026; for model 22, raining set R² = 0.790, Q² = 0.627, RMSE = 0.023, and for test set R² = 0.955, RMSE = 0.020. These two consensus scoring model would be used for the docking virtual screening of novel mTOR inhibitors.     

Spin-valve planar Hall sensor for single bead detection

The planar Hall effect (PHE) sensor with a junction size of 3 μm × 3 μm for a single micro-bead detection has been fabricated successfully using a typical spin-valve thin film Ta(5)/NiFe(16)/Cu(1.2)/NiFe(2)/IrMn(15)/Ta(5) nm. The PHE sensor exhibits a sensitivity of about 7.2 μV Oe^?1 in the magnetic field range of ±7 Oe approximately. We have performed an experiment to illustrated the possibility of single micro-bead detection by using a PHE sensor. A single micro-bead of 2.8 μm diameter size is secluded from 0.1% dilute solution of the Dynabeads^® M-280 dropped on the sensor surface and is located on the sensor junction by using a micro magnetic needle. The comparison of the PHE voltage profiles in the field range from 0 to 20 Oe in the absence and presence of a single micro-bead identifies a single Dynabeads^® M-280, the maximal signal change as large as ΔV ～ 1.1 μV can be obtained at the field ～6.6 Oe. The results are well described in terms of the reversal of a basic single domain structure.     

ANN based simulation and experimental verification of analytical four- and five-parameters models of PV modules

In this article, artificial neural network (ANN) is adopted to predict photovoltaic (PV) panel behaviors under realistic weather conditions. ANN results are compared with analytical four and five parameter models of PV module. The inputs of the models are the daily total irradiation, air temperature and module voltage, while the outputs are the current and power generated by the panel. Analytical models of PV modules, based on the manufacturer datasheet values, are simulated through Matlab/Simulink environment. Multilayer perceptron is used to predict the operating current and power of the PV module. The best network configuration to predict panel current had a 3–7–4–1 topology. So, this two hidden layer topology was selected as the best model for predicting panel current with similar conditions. Results obtained from the PV module simulation and the optimal ANN model has been validated experimentally. Results showed that ANN model provide a better prediction of the current and power of the PV module than the analytical models. The coefficient of determination (R²), mean square error (MSE) and the mean absolute percentage error (MAPE) values for the optimal ANN model were 0.971, 0.002 and 0.107, respectively. A comparative study among ANN and analytical models was also carried out. Among the analytical models, the five-parameter model, with MAPE = 0.112, MSE = 0.0026 and R² = 0.919, gave better prediction than the four-parameter model (with MAPE = 0.152, MSE = 0.0052 and R² = 0.905). Overall, the 3–7–4–1 ANN model outperformed four-parameter model, and was marginally better than the five-parameter model.     

Adsorption of plasma proteins onto PEGylated single-walled carbon nanotubes: The effects of protein shape,PEG size and grafting density

Single-walled carbon nanotubes (SWCNTs) covalently functionalized or noncovalently coated with polyethylene glycol (PEG) of different sizes (M_w = 2000 and 5000) and grafting densities (5–16 PEGs per SWCNT) are simulated with human fibrinogen (HFG) and serum albumin (HSA). Proteins migrate toward the SWCNT, but their adsorption extents differ. The extent of the HFG-SWCNT binding decreases with increasing PEG size and grafting density because PEGs more completely cover SWCNTs and thus block hydrophobic interactions between HFGs and SWCNTs, which occurs on PEG-functionalized SWCNTs but not on PEG-coated ones. In particular, the HFG-SWCNT binding significantly decreases in the transition region of PEG conformation from mushroom to brush, where PEGs extend like brushes as described in the Alexander-de Gennes theory. While the HFG adsorption is modulated by PEG conformation, the HSA adsorption is much weaker and less influenced by PEG, because spherical HSAs can bind to the restricted area of the SWCNT and thus cannot bind to the SWCNT as tightly as do linear HFGs. These findings agree with experiments showing less adsorption of proteins on the SWCNT functionalized with larger and more PEGs, and support experimental suggestions regarding the dependence of protein adsorption on protein shape and the mushroom-brush transition of PEG conformation.     

Effect of temperature on humido-sensitive conducting polymer actuators

Temperature dependence of water vapor sorption and electro-active polymer actuating behavior of free-standing films made of poly(3,4-ethylenedioxythiophene) doped with poly(4-styrenesulfonate) (PEDOT/PSS) was investigated by means of sorption isotherm and electromechanical analyses. The non-porous PEDOT/PSS film, having a specific surface area of 0.13 m² g^?1, sorbed water vapor of 1080 cm³(STP) g^?1, corresponding to 87 wt%, at relative water vapor pressure of 0.95. A temperature rise from 25 °C to 40 °C lowered sorption degree, indicative of an exothermic process, where isosteric heat of sorption decreased with increasing water vapor sorption and the value reached 43.9 kJ mol^?1, being consistent with the heat of water condensation (44 kJ mol^?1). Upon application of 10 V, the film underwent contraction of 2.46% at 5 °C caused by desorption of water vapor due to Joule heating, which slightly decreased to 2.10% at 45 °C. The speed of contraction was one order of magnitude faster than that of expansion and less dependent on the temperature since water vapor sorbed in the film were forced to desorb by Joule heating. In contrast, the higher the temperature the faster the film expansion because diffusion coefficient increased as the temperature became higher.     

Sleep in a live-in mining operation: The influence of start times and restricted non-work activities

The amount of sleep obtained between shifts is influenced by numerous factors including the length of work and rest periods, the timing of the rest period relative to the endogenous circadian cycle and personal choices about the use of non-work time. The current study utilised a real-world live-in mining environment to examine the amount of sleep obtained when access to normal domestic, family and social activities was restricted. Participants were 29 mining operators (26 male, average age 37.4 ± 6.8 years) who recorded sleep, work and fatigue information and wore an activity monitor for a cycle of seven day shifts and seven night shifts (both 12 h) followed by either seven or fourteen days off. During the two weeks of work participants lived on-site. Total sleep time was significantly less (p < 0.01) while on-site on both day (6.1 ± 1.0 h) and night shifts (5.7 ± 1.5 h) than days off (7.4 ± 1.4 h). Further, night shift sleep was significantly shorter than day-shift sleep (p < 0.01). Assessment of subjective fatigue ratings showed that the sleep associated with both days off and night shifts had a greater recovery value than sleep associated with day shifts (p < 0.01). While on-site, participants obtained only 6 h of sleep indicating that the absence of competing domestic, family and social activities did not convert to more sleep. Factors including shift start times and circadian influences appear to have been more important.     

Simulation of solid thermal explosion and liquid thermal explosion of dicumyl peroxide using calorimetric technique

Dicumyl peroxide (DCPO), is produced by cumene hydroperoxide (CHP) process, is utilized as an initiator for polymerization, a prevailing source of free radicals, a hardener, and a linking agent. DCPO has caused several thermal explosion and runaway reaction accidents in reaction and storage zone in Taiwan because of its unstable reactive property. Differential scanning calorimetry (DSC) was used to determine thermokinetic parameters including 700 J g^–1 of heat of decomposition (ΔH_d), 110 °C of exothermic onset temperature (T₀), 130 kJ mol^–1 of activation energy (E_a), etc., and to analyze the runaway behavior of DCPO in a reaction and storage zone. To evaluate thermal explosion of DCPO with storage equipment, solid thermal explosion (STE) and liquid thermal explosion (LTE) of thermal safety software (TSS) were applied to simulate storage tank under various environmental temperatures (T_e). T_e exceeding the T₀ of DCPO can be discovered as a liquid thermal explosion situation. DCPO was stored under room temperature without sunshine and was prohibited exceeding 67 °C of self-accelerating decomposition temperature (SADT) for a tank (radius = 1 m and height = 2 m). SADT of DCPO in a box (width, length and height = 1 m, respectively) was determined to be 60 °C. The TSS was employed to simulate the fundamental thermal explosion behavior in a large tank or a drum. Results from curve fitting demonstrated that, even at the earlier stage of the reaction in the experiments, ambient temperature could elicit exothermic reactions of DCPO. To curtail the extent of the risk, relevant hazard information is quite significant and must be provided in the manufacturing process.     

Evolution of structure,stability, and nonlinear optical properties of the heterodinuclear CNLin (n = 1–10) clusters

The lowest-energy structures and stabilities of the heterodinuclear clusters, CNLi_n (n = 1–10) and relevant CNLi_n⁺ (n = 1–10) cations, are studied using the density functional theory with the 6-311 + G(3df) basis set. The CNLi₆ and CNLi₅⁺ clusters are the first three-dimensional ones in the CNLi_n^0/+ series, respectively, and the CN group always caps the Li_n^0/+ moiety in the CNLi_n^0/+ (n = 1–9) configurations. The CN triple bond is found to be completely cleaved in the CNLi₁₀^0/+ clusters where the C and N atoms are bridged by two Li atoms. The CNLi_n (n = 2–10) clusters are hyperlithiated molecules with delocalized valence electrons and consequently possess low VIP values of 3.780–5.674 eV. Especially, the CNLi₈ and CNLi₁₀ molecules exhibit lower VIPs than that of Cs atom and can be regarded as heterobinuclear superalkali species. Furthermore, these two superalkali clusters show extraordinarily large first hyperpolarizabilities of 19,423 and 42,658 au, respectively. For the CNLi_n⁺ cationic species, the evolution of the energetic and electronic properties with the cluster size shows a special stability for CNLi₂⁺.     

Discrete return lidar-based prediction of leaf area index in two conifer forests

Leaf area index (LAI) is a key forest structural characteristic that serves as a primary control for exchanges of mass and energy within a vegetated ecosystem. Most previous attempts to estimate LAI from remotely sensed data have relied on empirical relationships between field-measured observations and various spectral vegetation indices (SVIs) derived from optical imagery or the inversion of canopy radiative transfer models. However, as biomass within an ecosystem increases, accurate LAI estimates are difficult to quantify. Here we use lidar data in conjunction with SPOT5-derived spectral vegetation indices (SVIs) to examine the extent to which integration of both lidar and spectral datasets can estimate specific LAI quantities over a broad range of conifer forest stands in the northern Rocky Mountains. Our results show that SPOT5-derived SVIs performed poorly across our study areas, explaining less than 50% of variation in observed LAI, while lidar-only models account for a significant amount of variation across the two study areas located in northern Idaho; the St. Joe Woodlands (R² = 0.86; RMSE = 0.76) and the Nez Perce Reservation (R² = 0.69; RMSE = 0.61). Further, we found that LAI models derived from lidar metrics were only incrementally improved with the inclusion of SPOT 5-derived SVIs; increases in R² ranged from 0.02–0.04, though model RMSE values decreased for most models (0–11.76% decrease). Significant lidar-only models tended to utilize a common set of predictor variables such as canopy percentile heights and percentile height differences, percent canopy cover metrics, and covariates that described lidar height distributional parameters. All integrated lidar-SPOT 5 models included textural measures of the visible wavelengths (e.g. green and red reflectance). Due to the limited amount of LAI model improvement when adding SPOT 5 metrics to lidar data, we conclude that lidar data alone can provide superior estimates of LAI for our study areas.     

Red emitting MTiO3 (M = Ca or Sr) phosphors doped with Eu3+ or Pr3+ with some cations as co-dopants

Ca (or Sr)TiO₃:Eu³⁺, M (Li⁺ or Na⁺ or K⁺) and CaTiO₃:Pr³⁺, M (Li⁺ or Na⁺ or Ag⁺ or K⁺ or Gd³⁺ or La³⁺) powders were prepared by combustion synthesis method and the samples were further heated to ～1000 °C to improve the crystallinity. The structure and morphology of materials were examined by X-ray diffraction (XRD) and a scanning electron microscopy (SEM). The morphologies of SrTiO₃:Eu³⁺, CaTiO₃:Eu³⁺ or CaTiO₃:Pr³⁺ powders co-doped with other metal ions were very similar. Small and coagulated particles of nearly cubical shapes with small size distribution having smooth and regular surface were formed. Photo-luminescence spectra of CaTiO₃:Pr³⁺ and co-doped either with Li⁺, Na⁺, K⁺, Ag⁺, La³⁺ or Gd³⁺ ions showed red emissions at 613 nm due to the ¹D₂ → ³H₄ transition of Pr³⁺. The variation of intensity of emission peak with different co-doping follows the order: K⁺ > Ag⁺ > Na⁺ > Li⁺ > La³⁺ > Gd³⁺. The characteristic emissions of CaTiO₃:Eu³⁺ lattices had strong emission at 614 and 620 nm for ⁵D₀ → ⁷F₂ with other weak transitions observed at 580, 592, 654, 705 nm for ⁵D₀ → ⁷F_n transitions where n = 0, 1, 3, 4 respectively in all host lattices. Photoluminescence intensity in SrTiO₃:Eu³⁺ is more than CaTiO₃:Eu³⁺ lattices. A remarkable increase of photoluminescence intensity (in ⁵D₀ → ⁷F₂ transition) was observed if co-doped with Li⁺ ions in CaTiO₃:Eu³⁺ and SrTiO3:Eu³⁺.     

Growth and electrical properties of highly (0 0 1)-oriented Pb(Zr0.52Ti0.48)O3 thin films on amorphous TiN buffered Si(1 0 0)

Pb(Zr_0.52Ti_0.48)O₃ (PZT) ferroelectric thin films with LaNiO₃ (LNO) as bottom electrodes have been grown on amorphous TiN buffered Si(1 0 0) substrates by pulsed laser deposition. It was found that highly (0 0 1)-oriented LNO films could be obtained even if TiN underlayers were amorphous. XRD analyses showed that the subsequently deposited PZT films were also preferentially (0 0 1)-oriented due to the template effect of the perovskite structured LNO films. Dielectric constant of the PZT thin films remained almost constant with frequency in the range from 10³ to 10⁶ Hz, and tangent loss was as small as 0.02 at high frequencies. The remnant polarization and coercive field of an Au/PZT/LNO capacitor were typically 20 μC/cm² and 30 kV/cm, respectively. C–V and I–V characteristics revealed the capacitance and leakage current variations with applied voltage were asymmetric when the bottom electrode was negatively as well as positively biased, indicating that ferroelectric/electrode interfaces and space charges play an important role in the electrical properties of ferroelectric capacitors.     

Interpretation and topographic compensation of conifer canopy self-shadowing

The self-shadowing of conifer canopies results from the size and arrangement of trees within a stand and is a first-order term controlling radiance from forested terrain at common pixel scales of tens of meters. Although self-shadowing is a useful attribute for forest remote-sensing classification, compensation for the topographic effects of self-shadowing has proven problematic. This study used airborne canopy LiDAR measurements of 80 Pacific Northwest, USA conifer stands ranging in development stage from pre-canopy closure to old-growth in order to model canopy self-shadowing for four solar zenith angles (SZA). The shadow data were compared to physical measurements used to characterize forest stands, and were also used to test and improve terrain compensation models for remotely sensed images of forested terrain. Canopy self-shadowing on flat terrain strongly correlates with the canopy's geometric complexity as measured by the rumple index (canopy surface area/ground surface area) (R² = 0.94–0.87 depending on SZA), but is less correlated with other stand measurements: 95th percentile canopy height (R² = 0.68), mean diameter at breast height (dbh) (R² = 0.65), basal area ha^? 1 (R² = 0.18), and canopy stem count ha^? 1 (R² = 0.18). The results in this paper support interpretation of self-shadowing as a function of canopy complexity, which is an important ecological characteristic in its own right. Modeling of canopy self-shadowing was used to assess the accuracy of the Sun-Canopy-Sensor (SCS) topographic correction, and to develop a new empirical Adaptive Shade Compensation (ASC) topographic compensation model. ASC used measured shadow (as an estimate of canopy complexity) and the SCS term (to describe the illumination geometry) as independent variables in multiple regressions to determine the topographic correction. The ASC model provided more accurate radiance corrections with limited variation in results across the full range of canopy complexities and incidence angles.     

Prediction of flow pattern of gas–liquid flow through circular microchannel using probabilistic neural network

The present study attempts to develop a flow pattern indicator for gas–liquid flow in microchannel with the help of artificial neural network (ANN). Out of many neural networks present in literature, probabilistic neural network (PNN) has been chosen for the present study due to its speed in operation and accuracy in pattern recognition. The inbuilt code in MATLAB R2008a has been used to develop the PNN. During training, superficial velocity of gas and liquid phase, channel diameter, angle of inclination and fluid properties such as density, viscosity and surface tension have been considered as the governing parameters of the flow pattern. Data has been collected from the literature for air–water and nitrogen–water flow through different circular microchannel diameters (0.53, 0.25, 0.100 and 0.050 mm for nitrogen–water and 0.53, 0.22 mm for air–water). For the convenience of the study, the flow patterns available in literature have been classified into six categories namely; bubbly, slug, annular, churn, liquid ring and liquid lump flow. Single PNN model is unable to predict the flow pattern for the whole range (0.53 mm–0.050 mm) of microchannel diameter. That is why two separate PNN models has been developed to predict the flow patterns of gas–liquid flow through different channel diameter, one for diameter ranging from 0.53 mm to 0.22 mm and another for 0.100 mm–0.05 mm. The predicted map and their transition boundaries have been compared with the corresponding experimental data and have been found to be in good agreement. Whereas accuracy in prediction of transition boundary obtained from available analytical models used for conventional channel is less for all diameter of channel as compared to the present work. The percentage accuracy of PNN (～94% for 0.53 mm ID and ～73% for 0.100 mm ID channel) has also been found to be higher than the model based on Weber number (～86% for 0.53 mm ID and ～36% for 0.05 mm ID channel).