Revisiting the conversion reaction voltage and the reversibility of the CuF<Subscript>2</Subscript> electrode in Li-ion batteries

Deviation between thermodynamic and experimental voltages is one of the key issues in Li-ion conversion-type electrode materials; the factor that affects this phenomenon has not been understood well in spite of its importance. In this work, we combine first principles calculations and electrochemical experiments with characterization tools to probe the conversion reaction voltage of transition metal difluorides MF₂ (M = Fe, Ni, and Cu). We find that the conversion reaction voltage is heavily dependent on the size of the metal nanoparticles generated. The surface energy of metal nanoparticles appears to penalize the reaction energy, which results in a lower voltage compared to the thermodynamic voltage of a bulk-phase reaction. Furthermore, we develop a reversible CuF₂ electrode coated with NiO. Electron energy loss spectroscopy (EELS) elemental maps demonstrate that the lithiation process mostly occurs in the area of high NiO content. This suggests that NiO can be considered a suitable artificial solid electrolyte interphase that prevents direct contact between Cu nanoparticles and the electrolyte. Thus, it alleviates Cu dissolution into the electrolyte and improves the reversibility of CuF₂.

Open image in new window

     

The Design of a Coprime‐Factorized Predictive Functional Controller for Unstable Fractional Order Systems

In this paper, a new approach, called coprime‐factorized predictive functional control method (CFPFC‐F) is proposed to control unstable fractional order linear time invariant systems. To design the controller, first, a prediction model should be synthesized. For this purpose, coprime‐factorized representation is extended for unstable fractional order systems via a reduced approximated model of unstable fractional order (FO) system. That is, an approximated integer model of fractional order system is derived via the well‐known Oustaloup method. Then, the high order approximated model is reduced to a lower one via a balanced truncation model order reduction method. Next, the equivalent coprime‐factorized model of the unstable fractional‐order plant is employed to predict the output of the system. Then, a predictive functional controller (PFC) is designed to control the unstable plant. Finally, the robust stability of the closed‐loop system is analyzed via small gain theorem. The performance of the proposed control is investigated via simulations for the control of an unstable non‐laminated electromagnetic suspension system as our simulation test system.     

Fuzzy cognitive map based approach for determining the risk of ischemic stroke

Stroke is the third major cause of mortality in the world. The diagnosis of stroke is a very complex issue considering controllable and uncontrollable factors. These factors include age, sex, blood pressure, diabetes, obesity, heart disease, smoking, and so on, having a considerable influence on the diagnosis of stroke. Hence, designing an intelligent system leading to immediate and effective treatment is essential. In this study, the soft computing method known as fuzzy cognitive mapping was proposed for diagnosis of the risk of ischemic stroke. Non‐linear Hebbian learning method was used for fuzzy cognitive maps training. In the proposed method, the risk rate for each person was determined based on the opinions of the neurologists. The accuracy of the proposed model was tested using 10‐fold cross‐validation, for 110 real cases, and the results were compared with those of support vector machine and K ‐nearest neighbours. The proposed system showed a superior performance with a total accuracy of (93.6 ± 4.5)%. The data used in this study is available by emailing the first author for academic and non‐commercial purposes.Inspec keywords: patient diagnosis, fuzzy logic, diseases, medical computing, cognition, learning (artificial intelligence), fuzzy set theory, Hebbian learning, neural nets, support vector machinesOther keywords: ischemic stroke, controllable factors, uncontrollable factors, blood pressure, heart disease, intelligent system, immediate treatment, soft computing method, fuzzy cognitive mapping, nonlinear Hebbian learning method, fuzzy cognitive maps training, risk rate     

Superabsorbent polymers achieved by surface cross linking of poly(sodium acrylate) using microwave method

Surface cross linking is a post-treatment process for superabsorbent polymers (SAPs) leading to an increase in the absorbency under load (AUL). This process is typically carried out through conventional heating method. In the current study, for the first time, microwave method was used for surface treatment process of SAPs based on poly(sodium acrylate). Diglycidyl materials such as 1,4-butanediol diglycidyl ether (BDDGE), polyethylene diglycidyl ether (PEGDGE), and ethylene glycol diglycidyl ether (EGDGE) were utilized as surface cross-linking agents. Also, N,N-Dimethylaniline was used as a catalyst for surface treatment of poly(sodium acrylate) SAP with diglycidyl materials as the external cross linkers. The results showed that surface treatment time can be reduced from 1 to 3 h in the conventional heating to a few minutes by microwave method. The use of catalyst in surface treatment solution resulted in higher AULs. The AULs of SAPs were increased from 14 g/g for unmodified SAP to 17.5, 19 and 20.7 g/g after surface treatment for surface-treated SAPs with BDDGE, PEGDGE and EGDGE, respectively. These results present the microwave method as an effective alternative candidate for thermal surface treatment of SAP which can have economic benefits from the viewpoint of time and energy consumption industrially.

     

Species identification and animal authentication in meat products: a review

Meat species identification and animal authentication in meat products is a significant subject, attention to which would contribute to fair-trade, and would enable consumers to make informed choices. Analytical methods are often based on protein or DNA measurements. Methods based on protein fractions include electrophoretic, chromatographic and immunological techniques and are often not suitable for compound food products, nor are they sensitive in processed products to differentiate closely related meat species. Advances in DNA technology have led to the rapid development of alternative approaches to species identification. Recently, application of polymerase chain reaction in food analysis has increased in the light of their simplicity, specificity and sensitivity. This review discusses a wide range of analytical methods with a focus on their ability to quantify meat and authentication of meat products.     

Diversity achieving full-duplex DF relaying with joint relay-antenna selection under Nakagami-m fading environment

One of the main imperfections degrading the performance of full-duplex (FD) relaying systems is the outage floor. In this work, a power scaling method is proposed, which overcomes this effect even when there does not exist a direct channel between source and destination nodes. The system is composed of $K$ decode-and-forward (DF) FD relays over the Nakagami-m fading environment. To promote system performance, joint antenna and relay selection methods are employed in the FD relaying systems. Each FD relay is equipped with multiple antennas for receiving and the other for transmitting the information. The transmitting and receiving antennas are chosen based on the instantaneous channel variations, and one relay is selected to improve the signal-to-interference and noise ratio (SINR) of the FD relaying system. The performance of the proposed design is investigated. Moreover, the closed-form equations of the ergodic capacity and outage probability are attained. The analytical results are confirmed by different simulations. Results indicate that the proposed design achieves an additional spatial diversity gain because of using the antenna selection at the relay nodes. Moreover, by power scaling (PS) method, the system performance is effectively improved compared to the conventional FD relaying structures.     

Engineering a drug eluting ocular patch for delivery and sustained release of anti-inflammatory therapeutics

Ocular inflammation is commonly associated with eye disease or injury. Effective and sustained ocular delivery of therapeutics remains a challenge due to the eye physiology and structural barriers. Herein, we engineered a photocrosslinkable adhesive patch (GelPatch) incorporated with micelles (MCs) loaded with loteprednol etabonate (LE) for delivery and sustained release of drug. The engineered drug loaded adhesive hydrogel, with controlled physical properties, provided a matrix with high adhesion to the ocular surfaces. The incorporation of MCs within the GelPatch enabled solubilization of LE and its sustained release within 15 days. In vitro studies showed that MC loaded GelPatch supported cell viability and growth. In addition, subcutaneous implantation of the MC loaded GelPatch in rats confirmed its in vivo biocompatibility and stability within 28 days. This non-invasive, adhesive, and biocompatible drug eluting patch can be used as a matrix for the delivery and sustained release of hydrophobic drugs.