Changes In Conductivity Research Articles

Adsorption and sensing properties of TiO2-modified MoSe2 monolayer for SF6 decomposition components based on the first-principles

As crucial components in power systems, SF6 gas-insulated equipment inevitably develops insulation defects and faults over long-term operation, leading to the decomposition of SF6 gas. Therefore, detecting SF6 decomposition components is essential for assessing the operational status of SF6 gas-insulated equipment and ensuring the safe and stable operation of power systems. Based on first principles, the modification mechanism of TiO2 on MoSe2 was explored, and optimal adsorption models for gases SO2, H2S, SOF2, and SO2F2 on this composite material were established. The adsorption characteristics of each system were analyzed through parameters such as adsorption energy, adsorption distance, charge transfer, electron density difference, and density of states, and the sensing properties of the systems were discussed using parameters including changes in frontier molecular orbitals, desorption time, and sensitivity. The results indicate that TiO2–MoSe2 has difficulty capturing SO2F2, but exhibits chemisorption for SO2, H2S, and SOF2 with adsorption energies of −1.25 eV, −0.99 eV, and −3.40 eV, respectively. Upon adsorption of SO2, TiO2–MoSe2 shows a significant change in electrical conductivity, a desorption time of 4.51 s (at 498 K), and a strong sensitivity response. However, the response to H2S is relatively weak, with lower sensitivity. Therefore, this material is suitable for detecting SO2 but not H2S. TiO2–MoSe2 demonstrates a significant response and excellent sensitivity to SOF2; however, the adsorption process lacks repeatability. Therefore, it can be used as a disposable gas sensor or solid adsorbent for SOF2 detection. The results of this study provide theoretical support for the application of MoSe2-based sensors in the monitoring and fault diagnosis of SF6 gas-insulated equipment.

Tunable memory behavior in light stimulated artificial synapse based on ZnO thin film transistors

Abstract Optoelectronic synapses are inevitable for realizing neuromorphic vision systems, which require the integration of image recognition, memory and image processing into a single platform. In this work, we present a three terminal optoelectronic synapse created using zinc oxide (ZnO) thin film transistor. The persistent photoconductivity (PPC) of ZnO thin film is utilized to demonstrate the synaptic behaviour. The change in conductance of the device under UV illumination has been interpreted as the weight change in the synapse. The basic synaptic functions such as sensory memory, short term memory, long term memory, duration time dependent plasticity and paired pulse facilitation have been successfully demonstrated. The device shows a PPF index of 160\%, comparable to other optoelectronic synapses reported in literature. Further, to corroborate the existing theory that PPC is caused by oxygen vacancies, additional characterizations are carried out and the presence of oxygen vacancies is detected in the fabricated ZnO device. Subsequently, pattern recognition of MNIST handwritten dataset has been performed using the conductance tuning curves of the proposed ZnO TFT based synapses in a neural network architecture, thereby demonstrating their feasibility to be used in neuromorphic applications.

Caught between two states: The compromise in acclimation of photosynthesis, transpiration and mesophyll conductance to different amplitudes of fluctuating irradiance.

While dynamic regulation of photosynthesis in fluctuating light is increasingly recognized as an important driver of carbon uptake, acclimation to realistic irradiance fluctuations is still largely unexplored. We subjected Arabidopsis thaliana (L.) wild-type and jac1 mutants to irradiance fluctuations with distinct amplitudes and average irradiance. We examined how irradiance fluctuations affected leaf structure, pigments and physiology. A wider amplitude of fluctuations produced a stronger acclimation response. Large reductions of leaf mass per area under fluctuating irradiance framed our interpretation of changes in photosynthetic capacity and mesophyll conductance as measured by three separate methods, in that photosynthetic investment increased markedly on a mass basis, but only a little on an area basis. Moreover, thermal imagery showed that leaf transpiration was four times higher under fluctuating irradiance. Leaves growing under fluctuating irradiance, although thinner, maintained their photosynthetic capacity, as measured through light- and CO2-response curves; suggesting their photosynthesis may be more cost-efficient than those under steady light, but overall may incur increased maintenance costs. This is especially relevant for plant performance globally because naturally fluctuating irradiance creates conflicting acclimation cues for photosynthesis and transpiration that may hinder progress towards ensuring food security under climate-related extremes of water stress.

Anisotropic and shape-stable sugarcane-based phase change composites in the application of solar thermal energy storage

The study of anisotropically thermal conductive phase change composites (PCCs) with shape-stability is of great importance to improve the intermittent issues in solar thermal utilization, while some drawbacks like the high cost, low thermal conductivity, and poor durability of current PCCs restrains their practical applications. Herein, a cheap and scalable biomass-based PCC containing carbonized sugarcane (CSC), polyethylene glycol (PEG), and graphene (GF) is proposed. The abundant vessels inside CSC result in a high PEG loading rate of 82.48 %. The naturally occurring anisotropic structure inside CSC can drive GF to be oriented along the lengthwise direction, which gives PCC a high thermal conductivity anisotropy degree of 1.45. The prominent anisotropy improves the lengthwise thermal diffusion and restrains the transverse heat leakage to the environment. Besides, the loaded GF can further enhance the light absorption of PCC to 98 %. As a result, the prepared PCC can achieve high solar-thermal energy storage efficiencies of 79.3–91.7 % with variable solar intensity from 1 to 2 suns. Meanwhile, good anti-leakage and durability performances promote the practical utilization of PCCs. The present work paves a promising road for manufacturing biomass-based anisotropic PCCs in efficient solar thermal energy storage.

Do gravel highways affect water quality and invertebrate communities in Arctic lakes?

ABSTRACT Gravel roads are a common feature in developed areas of Canada’s Arctic. These roads can be a source of calcareous dust that drifts to roadside lakes, causing significant changes in conductivity, calcium, and pH levels. In this study, we examined if road proximity was associated with differences in water quality and invertebrate communities in lakes along the Dempster and Inuvik-Tuktoyaktuk Highways in the Northwest Territories, Canada. We collected biological and water quality data from 18 lakes selected using a stratified random sampling design, with distance from the road (0–300 m, 300–600 m, and >600 m) and region of study (boreal forest, tundra) as the two factors. We hypothesized that lakes closer to the road would exhibit differences in water quality and invertebrate communities associated with road dust pollution and other stressors caused by roads. We found no clear differences in water quality or invertebrate communities among lakes based on distance from the highways. In addition, while there were differences between regions, these did not appear to be related to the effects of the roadways. Our results suggest that variability in lake morphometry and water quality in this region might be more important than the influence of roads.

Modeling the thermal behavior of multifunctional syntactic foams containing phase change materials for heat management applications

AbstractSyntactic foams are lightweight porous composites obtained by the addition of hollow glass microspheres (HGMs) to a polymer matrix. This work experimentally and numerically investigates the thermal behavior of epoxy‐based syntactic foams with enhanced multifunctionality produced by incorporating microencapsulated phase change materials (PCMs) as functional fillers, providing thermal energy storage and temperature stabilization due to its large latent heat of melting. Foams are fabricated using varying ratios of HGMs (0–40 vol%) and PCM (0–40 vol%) to achieve tuned densities and thermal properties with a total filler content of up to 40 vol%. A one‐dimensional numerical heat transfer model based on the enthalpy method is presented to simulate the thermal behavior of these materials. The model accurately incorporates the specific heat and thermal conductivity of the foam components, as well as the latent heat absorption during PCM melting (up to 68 J/g). The model is experimentally validated by demonstrating good agreement with the measurements of transient temperature profiles during heating tests on the foam samples. The validated tool is then leveraged to model the thermal response of the foams under a sinusoidally varying heat source, similar to the possible real applicative scenarios. These results can help optimize foam compositions balancing energy storage density, heat transfer properties, and structural support for lightweight energy storage systems. Potential applications include high‐efficiency thermal management in battery packs, electronic cooling, solar energy storage, and sustainable temperature‐controlled buildings.Highlights Foams with 0–40 vol% PCM and 0–40 vol% HGM show up to 68 J/g latent heat. 1D numerical model accurately captures thermal conductivity and PCM phase change. Validated model simulates thermal response under sinusoidal heat, optimizing foam composition. Foams act as thermal reservoirs, maintaining up to 5°C lower surface temperatures than epoxy.

Anode humidity trade-offs between proton conductivity and concentration overpotentials in protonic ceramic fuel cell: Experiments and numerical simulations

In protonic ceramic fuel cells (PCFCs), the H2 and H2O partial pressures in the anode side vary along the gas flow direction, particularly during high-fuel-utilization operation. This results in different species conductivities and overpotentials, which renders the quantitative evaluation of local performance based on H2 and H2O mole fractions important. A numerical model is developed that can reflect changes in species conductivities and overpotentials resulting from varying gas mole fractions. The numerical current density–voltage characteristic results obtained numerically agree well with those obtained via measurement in a wide range of H2–H2O gas conditions. Numerical modeling confirms a tradeoff, i.e., higher supply humidity levels result in lower electrolyte resistances but higher concentration overpotentials. The maximum output is achieved at a modest humidification level of 20%. The developed numerical model is particularly useful for optimizing the operating conditions of PCFCs with high fuel utilization.

Analysis of charging performance of thermal energy storage system with graded metal foam structure and active flip method

The latent heat thermal energy storage (LHTES) technology based on solid-liquid phase change material (PCM) is characterized by high energy storage density, small volume change, and constant operation temperature, which is widely employed in waste heat recovery, solar thermal utilization, and equipment thermal management. This paper introduces active flipping and graded porous to strengthen natural convection and heat conduction to alleviate the issue of low thermal conductivity and non-uniform phase change of PCM. Based on the fixed grid system, the enthalpy-porosity method combined with time-dependent gravity and non-Darcy porous effect is employed to solve the solid-liquid phase change problem under different porosity gradients, dimensionless flipping times (t*), and modified Ra (Ra*) numbers. Furthermore, the effects of the Ra* on the optimal t* and porosity gradient are also discussed. The results show that the flipping method can significantly enhance flow and heat transfer within the container, improve temperature field uniformity, and increase the proportion of latent heat storage. With the increase of porosity gradient and t*, the melting time initially decreases and then increases and the optimal thermal performance is achieved at a 6 % porosity gradient and t* = 0.4. Compared to the uniform porous structure without flipping, it can shorten melting time by 49.37 % and improve thermal energy storage rate (TESR) by 76.23 %. Additionally, the optimal porosity gradient is 6 % under different Ra*, but the optimal t* decreases with the increase of Ra*. This paper explores the charging performance of the thermal energy storage system with the graded metal foam structure and active flip method, which can contribute to the study of heat transfer enhancement in LHTES technology.

Novel investigation on adsorption analysis of safranal interacting with boron nitride and aluminum nitride fullerene-like cages: Drug delivery system

This study illustrates the effective control of COVID-19 infection through the adsorption of safranal (SAF) on B16N16 and Al16N16 fullerene-like cages. The SAF adsorption onto the B16N16 and Al16N16 surfaces in gas, water (H2O), and chloroform (CHCl3) environments were assessed using density functional theory (DFT) and time-dependent (TD) density functional theory methods, analyzing the substrates and their complexes. The Al16N16/SAF complex exhibited the most negative binding energy and structural stability in the water phase compared to the B16N16/SAF complex at the PBE0-D3 level. The thermodynamic parameters indicated that the adsorption of SAF onto the fullerene-like cages is exothermic, particularly for the Al16N16/SAF complex. Additionally, the interaction of SAF with the fullerene-like cages in the water phase is more pronounced than in gas and chloroform environments. The complexes' energy gap (Eg) decreases in all three environments compared to the perfect systems, with a significant reduction of over 21 % in all phases. This substantial decrease in the energy gap suggests that the complexes have increased reactivity and sensitivity to SAF, likely due to a significant change in electronic conductivity. The results of molecular docking indicate that the Al16N16/SAF complex in the water phase exhibited a strong binding affinity compared to the other compounds studied. These findings suggest that the Al16N16/SAF complex holds promise as a potential inhibitor for COVID-19 and as a valuable material for biomedical applications and drug delivery systems.

Transient characteristics of ultra-high frequency adjustable multi-pulse gas tungsten welding arc

In response to the shortcomings of traditional Gas Tungsten Arc Welding (GTAW), such as low penetration, slow welding speed, and low production efficiency, an Ultra-High-Frequency Adjustable Multi-Pulse Gas Tungsten Arc Welding (UFMP-GTAW) process is proposed. This process more effectively concentrates the temperature distribution of the welding arc. To characterize the arc morphology and its dynamic changes, high-speed photography captured ten images of the UFMP-GTAW welding arc within one cycle. The arc image processing algorithm proposed in the paper was used to study the arc's variation characteristics over one cycle. This algorithm includes binarization, arc segmentation, and edge detection to obtain arc morphology parameters. The arc symmetry algorithm is used to symmetrize the arc images for Abel inversion, resulting in the emission coefficient distribution. Further calculations provide the temperature distribution, electrical conductivity distribution, and current density distribution of the arc. Analysis reveals that changes in arc temperature, electrical conductivity, and current density lag behind changes in current. The temperature of the arc spreads from the central axis to the surrounding arc space. When high-frequency current variations occur, the heat input generated by the current increase takes time to transfer to the entire arc space. Conversely, if the current suddenly decreases, the heat input rapidly diminishes. However, because heat transfer takes time, the arc space remains relatively stable for a short period, making the ultra-high-frequency arc form more stable.

The electric-field-induced birefringence of hexagonal Cu1.81S flakes influenced by hole carriers

In this work, regular hexagonal copper sulfide (Cu1.81S) flakes are fabricated in two sizes by controlling the dosage of octadecene (ODE). Significant electric-field-induced birefringence of their dispersions is observed, benefitting from the anisotropic shape and the mobility of the hole carriers. The sudden increase and the relative inversion of the electric-field-induced birefringence Δn for the two sizes can be explained by both the constraint of polarized carriers and an increasing electric field. The dominance of the hole carriers in the electric-field-induced birefringence of Cu1.81S flakes is proved by decoration with silver nanoparticles and the corresponding conductivity changes.

A Deep Learning Estimation for Probing Depth of Transient Electromagnetic Observation

The probing depth of the transient electromagnetic method (TEM) refers to the depth range at which the underground conductivity changes can be effectively detected. It typically ranges from tens of meters to several kilometers and is influenced by factors such as instrument parameters and the conductivity of the subsurface structure. Rapid and accurate probing depth is useful for the selection of appropriate inversion parameters and improving survey accuracy. However, mainstream methods suffer from issues such as low computational precision, large uncertainties, or high computational requirements, making them unsuitable for processing massive airborne electromagnetic data. In this study, we propose a prediction model based on deep learning that can directly compute the probing depth from the TEM responses, and its effectiveness and accuracy are validated through synthetic models and field measurements. We compared the performance of classic deep learning models, including CNN, RESNET, and RNN, and found that RNN performed the best overall on both synthetic and field data. Furthermore, we apply this algorithm to deep learning-based ATEM inversion by constraining the one-dimensional resistivity model depths in the training set, to reduce the non-uniqueness of the inversion, accelerate the convergence, and improve its prediction accuracy.

Complete degradation of propranolol by a water falling film non-thermal plasma reactor: The effects of input power and plasma gases on transformation pathway

Propranolol, a beta-blocker, is a widely used drug for cardiovascular diseases. A synthetic sample containing propranolol (PRO) solution at native pH was recirculated up to ten times through an original coaxial non-thermal plasma reactor with a water falling film. Different discharge conditions, feed gasses, and power greatly influenced the extent of PRO degradation and the abundance of degradation products. The highest degradation rates were achieved in Ar plasma, either pure or in a mixture with O2, with over 70% of PRO degraded after only two passes through the DBD reactor. The full non-thermal plasma treatment was followed by a minimal change of pH value and conductivity. On the other hand, ambient air as the feed gas resulted in 60% of PRO degradation rate after the full treatment and contributed to a major drop in pH value and an increase in the conductivity of the treated solution. The structures of 47 degradation products in all three gases were identified and the degradation pathway is proposed, supported by DFT methodology. Scavenging experiments demonstrated that hydroxyl radical was predominant when plasma was generated using ambient air and Ar, while it had a minimal effect on the degradation in the mixture of Ar and O2. The desired level of propranolol decomposition was achieved only by the adjustment of the nonchemical parameters of the treatment, such as plasma gas composition and power. These results imply that a non-thermal plasma reactor is a highly effective method for pharmaceutical wastewater treatment, with no catalysts added.

Constructing Si@CN@MXene from silicon waste as high-performance lithium-ion battery anodes

Silicon represents one of the most attractive materials for anodes in lithium-ion batteries (LIBs) due to highest theoretical specific capacity. Thus, there is a most urgent need to prepare Si-based nano materials in a very efficient way, and develop some reasonable approaches for their modification in order to resolve the short-falls of Si anode, which include both low conductivity and huge volume changes during intercalation of lithium ions. In this work, the kerf loss silicon (KL Si) from the photovoltaic industry has been used as an inexpensive Si source for the synthesis of the Si@CN@MXene (SCM) composite to be applied as an anode material. Meanwhile, the chitosan works as the charge bridge in sequence with the Si and MXene nanosheets under electrostatic force during composite preparation. The MXene nanosheet and N-doped carbon layer that build up during chitosan calcination can be attributed to forming a hierarchical high-speed conductive network over the surface of silicon, which contributed to alleviate the volume expansion of silicon. The SCM electrode discharge capacity was obtained at a stable value of 900.8 mAh g−1 at 0.5 A g−1, 861.9 mA g−1 at 1 A g−1 and 657.6 mA g−1 even at 2 A g−1 after 100 cycles. The SCM electrode also exhibited good rate performance from 0.2 to 5 A g−1. Therefore, this study recommends that the method is very promising for producing the silicon anode material for LIBs from KL Si.

First-principles study on thermal transport properties of GaN under different cross-plane strain

With the development of power devices towards high integration and power, the electrical and thermal stresses per unit area become more concentrated and intensified. The impact of cross-plane strain resulting from inverse piezoelectric effect and thermal expansion on power devices cannot be ignored. Cross-plane strain has a substantial influence on the thermal properties of GaN. However, the research on the influence of cross-plane strain on the thermal conductivity of GaN has not been reported in the literature. Based on the first-principles calculation method and the phonon Boltzmann transport equation, the influence of cross-plane strain on the thermal conductivity and phonon characteristics of the GaN lattice is systematically studied in this study. The thermal conductivity of GaN has anisotropy and increases significantly with the decrease in temperature. The thermal conductivity of GaN at room temperature under the free state is calculated to be 257 and 275 W/(m·K) for in-plane (k⊥) and cross-plane (k∥) directions, respectively. Compared with previous theoretical reports, our calculation results are more consistent with the existing experiment values. Under the state of cross-plane strain, the lattice thermal conductivity changes remarkably. In detail, the average thermal conductivity at room temperature decreased by 35 % under a 5 % cross-plane tensile strain state, while it increased by 11.5 % under a 5 % cross-plane compressive strain state. According to the calculation results, the influence of cross-plane on lattice thermal conductivity is mainly due to the change in phonon lifetime. The analysis of two mechanisms of phonon lifetime suppression indicates that the cross-plane strain will significantly change the frequency of the high-frequency optical acoustic branch. This result leads to a change in the phonon scattering process and thus affects the phonon lifetime. Besides, the anisotropy of thermal conductivity changes under different strain values, which may be due to the weakened piezoelectric polarization effect induced by strain.

Superior selectivity for NH3 (NO2) gas molecules in In2SSe (Ga2SSe) Janus materials: a first-principles study

Anthropogenic gasses are very detrimental, requiring superior sensitive and selective materials to sense and segregate them. Using first-principles density functional theory (DFT) tools we have explored the sensitivity and selectivity of CO2, CO, NH3, NO2, H2S, and SO2 gases in the promising group-III Janus Ga2SSe and In2SSe nanostructured materials. We have explored all the possible adsorption sites in the Ga2SSe and In2SSe monolayer for sensing the gases and found that all the gasses are physisorbed in the sites with the lowest adsorption energy of −0.392 eV (−0.167 eV) for NH3 (NO2) on top of Indium (on the bridge-3 site) site of In2SSe (Ga2SSe). All adsorbed gasses significantly alter the bandgap of Ga2SSe and In2SSe from their pristine value and NO2-adsorbed M2SSe (M = Ga, In) structure exhibits significant bandgap changes: ∼0.16 eV reduction in Ga2SSe and ∼0.3 eV reduction in In2SSe from the pristine value, signifying substantial increase in conductivity. Additionally, analyzing the total density of states (TDOS), it can be concluded that NH3 at the Indium site of In2SSe and NO2 at the Bridge-3 site of Ga2SSe exhibit the most significant conductivity changes. Considering charge transfer, it is determined that 0.727 e/Å−3 of charge is transferred from In2SSe to NH3, while 1.05 e/Å−3 of charge is transferred from Ga2SSe to NO2 gas molecules, inferring that both NH3 and NO2 act as electron acceptors. Through this analysis, we found that NH3 is very selective on In2SSe while NO2 is selective on Ga2SSe Janus materials among the control gasses. This selectivity toward NH3 (NO2) gas on In2SSe (Ga2SSe) Janus material can open the new possibility of these materials for noxious gas sensing as well as NO2 utilization applications.

Phase change material properties identification for the design of efficient thermal management system for cylindrical Lithium-ion battery module

Performance and life of Lithium-ion battery packs in EVs and energy storage applications are limited by the thermal profile of cells during its life. Passive cooling technique based on Phase Change Material (PCM) is employed for mitigating thermal issues associated with Lithium-ion cells. The challenge with PCM materials is the trade-off between its latent heat of fusion, thermal conductivity and specific heat. Detailed CFD analysis is carried out to study the impact of PCM thermal properties on battery pack thermal profile and provide guidelines for PCM properties selection and usage of existing PCM materials. Impact of critical PCM properties such as latent heat (100–250 kJkg−1), thermal conductivity (0.2–23 Wm−1K−1), melting temperature and thickness of PCM material on thermal performance of a battery module during rapid discharge rate of 25 A (3.2C) is investigated. Battery module maximum temperature (Tmax) reaches 54°C and temperature difference (∆T) to 5.5°C while operating at 35°C with 124 kJkg−1and 0.2 Wm−1K−1. It is found that, PCM with higher latent heat (≥ 175 kJkg−1), thermal conductivity (≥ 3 Wm−1K−1), PCM thickness (≥ 3 mm) and appropriate phase change temperature 41-44°C exhibit improved heat dissipation for high discharge rates by effectively reducing the Tmax to 44.4°C and ∆T to 2.4°C (25 A and 35°C ambient). Further, ways to mitigate low latent heat capacity and thermal conductivity of PCM through increasing PCM material usage (i.e. inter cell distance) is emphasized. Results of the present study can be considered as a design tool for PCM development engineers which could be cost effective both in-terms of development time and effort.

(Invited) Electrochemical Random Access Memory for Neuromorphic Computing

Electrochemical random access memory (ECRAM) is a three terminal device that functions by tuning electronic conductance in functional materials through solid-state electrochemical redox reactions, and is one of several emerging device concepts that are currently being investigated in academic and industrial laboratories for enabling energy efficient brain inspired computing. Early demonstrations of nearly ideal analog switching and the realization that electrochemical ion insertion can be used to tune the electronic properties of many types of materials including transition metal oxides, layered 2D material, organic and coordination polymers, has sparked tremendous interest in ECRAM. Depending on the specific material, the resulting changes in conduction can range from gradual increments needed for analog elements, to large, abrupt changes for dynamically reconfigurable adaptive architectures. At its core, ECRAM shares many fundamental aspects with rechargeable batteries, where ion insertion materials are used extensively for their ability to reversibly store charge and energy. Computing applications, however, present drastically different requirements: systems will require many millions of devices, scaled down to less than 100 nanometers, all while achieving reliable electronic-state tuning at scaled-up rates (~109 Hz) and endurances (>1012 cycles), and with minimal energy dissipation and noise. In my presentation, I will briefly cover the history of ECRAM, the recent progress in devices spanning various types of materials, circuits, architectures, and discuss the rich portfolio of challenging, fundamental questions and how we can harness ECRAM to realize a new paradigm for low power neuromorphic computing.

(Invited) Optimising CNT-FET Biosensor Design: Tuning Biomolecule Interfaces for Aptamer-Biomarker Sensing and Beta-Lactamase Detection

We will present different strategies to control the assembly of single walled carbon nanotube (SWCNT)-biomolecule interfaces towards the fabrication of bioelectronic devices, with a focus on the real-time biosensors with engineered aptamer and protein interfacing. In particular, we will report on the fabrication of nanoscale biosensing devices designed to present biorecognition moieties (aptamers and proteins) in defined orientations; this allowed us to control the local electrostatic surface presented within the Debye length, and thus modulate the conductance gating effect upon binding incoming targets, from biomarkers associated to stress and (neuro) trauma, to classes of enzyme relevant to antimicrobial resistance (beta-lactamases).Our efforts focused in particular on using beta-lactamase binding protein (BLIP2) as receptor due to its potential as a biosensor for the most common antibiotic degrading enzymes, the beta-lactamases (BLs). Attaching BLIP2 at single designed residues positions directly to SCNTs using genetically encoded phenyl azide photochemistry, our devices were able to successfully detect the two different BLs, TEM-1 and KPC-2. The changes in conductance closely matched the predicted electrostatic profile sampled by the CNTs on BL binding.These studies highlight the key role transducer-biomolecule interfaces play in biosensing devices, and the need to control and tune by design the electrostatic gating, for more effective and optimal CNT-FET biosensor construction.

The Influence of Calendering Process on the Graphite Anode

Calendering, or pressing, is the final property-defining process for battery electrode manufacturing to maximize the volumetric capacity of the cell through the compaction of an electrode between two large calender rollers12. On one hand, it can reduce the porosity of battery materials thus increase the conductivity of the materials. On the other hand, it can control the thickness of the electrodes, making sure it is uniform and proper thickness to guarantee the minimal variation in the cell thickness, reaching even stack pressure for module assembly and impacting the thermal management [3]. The calendering process significantly influence the performance of lithium-ion battery (LIB).Graphite anode coating on Cu foil was chosen as our baseline coating for this calendering process investigation. The slurry was mixed in a planetary and dispersive mixer and then coated using reverse comma coating method by a scale-up coater with drying zones of 70 °C. Then the coating was calendered by a calendering machine at various conditions.Calendering, though seemingly a simply process, is influenced by several different parameters significantly. In this study, we systematically investigated the calendering process by carrying-out a Design-of-experiment (DOE). The following calendering parameters were investigated: the parameters of calendering machine gap size, calendering pressure, and the speed of the coating web. The calendered graphite coating was then measured for the morphology by scanning electron microscopy (SEM), the pore size change by BET surface area measurement, and Direct current internal resistance (DCIR) by building half cells and cycling and Electrochemical Impedance Spectroscopy (EIS) at 50% State-of-Charge (SOC).The result showed that pressure is the most critical parameter for calendering and the internal resistance and ionic conductivity changes in response to the calendering parameters. The half-cell with calendering at the pressure of 37 MPa, gap size targeted for 30% porosity, the BET surface area around 0.75 m2/g showed highest cell capacity and lowest internal resistance.