bluuebunny/crossref_metadata_2025_split huggingface dataset:
Investigate Thermodynamic and Kinetic Degradation of Lithium-Ion Batteries through a Combined Experimental and Modeling Approach
Authors: Jin Xinfang , Duan Xiting 
Published: July 2021

With the significant cost reduction of Li-ion battery (LIB) (85% in the past 10 years), 50-fold growth in market penetration of the technology has been projected through to 2030 for modern transportation and renewable energy integration [1]. LIB is a high energy density, high round-trip efficiency, high reliability energy storage device [2]. Different from Internal Combustion Engine, as a power generator, LIB pack is sealed in a certain container and its status need to be estimated by additional diagnostic instruments with specific electrochemical performance data. The traditional methods to estimate State of Charge (SOC) and State of Health (SOH) include current integration method and open circuit voltage method, which are time-consuming (over 1 day) and rely on historical information of the system [3]. Impedance spectroscopy technique can capture both thermodynamic and kinetic information of the LIB in a relatively short time, however, it is difficult and sometimes unreliable to interpret the data by using a simplified equivalent circuit method (ECM). In our recent publication [4], we also find that nonideality effect caused by interaction among high concentration Li ions could lead to nonlinear transport and kinetic parameters as a function of SOC. In this research work, we would like to use the perturbation form of a physics-based LIB model considering the nonideality feature of the cell to deconvolute the electrochemical impedance spectroscopy (shown in Fig.1) of a LIB coin cell operating under different SOCs and different number of cycles. After proper status evaluation of beginning of life (BOL) of the cell, parasitic side reactions, the solid electrolyte interface (SEI) formation in anode and cathode, are included as the indicators of degradation. A nonlinear optimization procedure will be used to identify the parameters associated with the cell capacity loss in each electrode, solid/liquid interface reaction, Li-ion diffusion, ohmic resistance, etc. The results will map out the cell capacity loss caused by thermodynamic degradation and deteriorated reaction kinetics and diffusivity caused by the growth of the SEI layers during cycling. It will provide important guidance in designing fast and accurate LIB real-time diagnostic devices.
References:
[1] BloombergNEF, https://about.bnef.com/blog/behind-scenes-take-lithium-ion-battery-prices/

[2] Jing Xie, Yi-Chun Lu, A retrospective on lithium-ion batteries, Nature Communications volume 11, Article number: 2499 (2020)
[3] Wen-Yeau Chang, The State of Charge Estimating Methods for Battery: A Review, ISRN Applied Mathematics. Volume 2013, Article ID 953792, 7 pages
[4] B Ng, X Duan, F Liu, E Agar, RE White, WE Mustain, X Jin, Investigation of Transport and Kinetic Nonideality in Solid Li-Ion Electrodes through Deconvolution of Electrochemical Impedance Spectra, Journal of The Electrochemical Society 167 (2), 020523

Figure 1
Detection of Volatile Organic Compounds in Contaminated Air Using Gas Sensors with Machine Learning
Authors: Shin Woosuck , Itoh Toshio , Koyama Yutaro , Akamatsu Takafumi , Tsuruta Akihiro , Masuda Yoshitake , Uchiyama Kazuhisa 
Published: July 2021

We investigate selective detection of target volatile organic compounds (VOCs) as unpleasant odor substances in the presence of interference VOCs from car interiors. We selected four unpleasant odor substances as target gases, such as age-related body odors and fungi, and two interference VOC substances, such as the main olfactory components from car interiors, in this study. We used eight semiconductive gas sensors including six general semiconductive gas sensors and two bulk-response type semiconductive gas sensors. Discrimination of each target gases was carried out using machine learnings, such as principal component analysis (PCA) and linear discriminant analysis (LDA), which are dimensionality reduction methods. PCA is an unsupervised learning method, and finds the axis that maximizes the data’s variance. LDA is a supervised learning method, and finds maximizes the separation between multiple classes. The machine learnings used sensor responses (r=Ra/Rg; r: sensor responses, Ra: resistance in humid pure air or in contaminant, Rg: resistance in the target gases) from all sensors.
The plots of PCA scores tended to move radially outward as concentrations of target gases increased. PCA scores of target gases with contaminants showed almost the same as those of lower concentration target gases without contaminants. However, the PCA scores in the presence of contaminants were within the area of each target gas so that the discrimination between the target gases was almost achieved. The LDA scores were classified by target gases without concentrations of target gas and contamination, so the LDA scores tended to aggregate with target gases. The plots of LDA scores seemed to be more gathered in each class (target gas) area than those of PCA scores.
Moreover, we investigated to reduce the number of sensors using another machine learning, i.e. feature selection technique, which used the sensor responses for obtaining sensor weights against all data. Based on the results of another machine learning, we selected three sensors which possess high scores in another machine learning. The PCA and LDA scores from selected three sensors showed almost the same qualities as those from eight sensors, whereas those from non-selected sensors cannot discriminate target gases, so the amount of information of the target gases has been reduced by excluding the selected three sensors for separating each target gas. From this result, it is possible to reduce the number of sensors to keep the discrimination between target gas by selecting sensors obtained using feature selection techniques.
Ref. T. Itoh, Y. Koyama, W. Shin, T. Akamatsu, A. Tsuruta, Y. Masuda, K. Uchiyama, Sensors 2020, 20, 2687.

Figure 1
Use of Statistical Methods for the Interpretation of Peruvian Wines Measurement Results with MOS Electronic Noses
Authors: Sun-Kou Rosario , Paredes-Doig Ana , Picasso Gino , La Rosa-Toro Adolfo , Doig-Camino Elizabeth 
Published: July 2021

The use of statistical methods for the treatment of information from a data set has been used to be able to better interpret the results obtained in the analysis of different samples. An example, the most common, of these statistical methods used is the Principal Component Analysis (PCA). But, other techniques like Cluster and Factorial analysis allow to have too a better idea of the results.
The tin oxides palladium or platinum doped were first prepared by the wet impregnation method. The sensors were made with the screen-printing method to print gold electrodes on alumina plates and finally the oxides, in a mixed with terpineol and ethyl celulose, were put on the electrodes surface. Then, some oxides were modified with a coating of Zeolite Y.
With the sensors made in the laboratory, two electronic noses were built. One of them, of platinum doped tin oxides and the other of Palladium (with and without Zeolite Y coating). The sensing of the wines was monitored with the LabVIEW software and the statistical methods were applied with the XLSTAT software. It was observed that for the nose of platinum sensor a difference in the groups of commercial and unknown wines was shown. This difference is attributed to the brand or origin of the wines. While the nose of palladium sensors allowed to distinguish the strains because it locked the Borgoña wines in a different conglomerate. This could be observed with the PCA, cluster and Factorial methods, complementing the three methods the statistical information.
The cluster method allowed classifying the results obtained with the detection of Peruvian wines, both when using palladium- and platinum-doped oxides. In both cases, it was possible to differentiate commercial wines from those of unknown brand. In addition, in each case one of the wines was distinguished by some characteristic. For example, the DM (Dulce Mistela) is different than the other wines of unknown brand since these other three are Borgoña. It was observed that the distances between classes are optimal when platinum-doped sensors are used, since the interclass variation exceeds 80%.
It is important to note that the results obtained with the factorial analysis mainly reinforce and complement the Cluster analysis, with which very similar groups were obtained, if not practically the same.
For example, using Factorial analysis, it is clearly observed that E-Nose 1 (Electronic nose made up of tin oxide-based sensors doped with different concentrations of platinum (0.1; 0.2; 0.3 and 0.5% Pt) with and without zeolite coating, and with three additional commercial sensors) separates the wines into two groups that are well differentiated: the known commercial wine (blue circle) from the unknown (red circle). It is also observed that the variance value of the two factors is high 99.26%. That could be seen in Figure 1.

Figure 1
(Invited) Potential Applications of Nano-Electrochemistry in Point-of-Care Sensing Devices
Authors: Chung Julia , Hertler Pheobe , Plaxco Kevin W. , Sepunaru Lior 
Published: July 2021

Using low noise electronics and micro-electrodes we can now electrochemically detect and count single, nanometer-scale objects in solution.1,2 This talk will cover recent discoveries in the field of single entity electrochemistry showing that electro-inactive species, including proteins, DNA, and even cells, can be detected individually using unmodified electrodes.3–5 Despite these advances, however, limitations persist. The location upon which the individual analyte adsorbs onto the electrode surface influences the magnitude of the electrochemical signal it produces.6,7 Consequently, the electronic output does not reflect the true physical properties of the biological entity, thus inhibiting the analytical resolution desired for applications.
In response, we have shown that coupling the electrochemical reaction to a homogeneous, rate-limiting chemical reaction ameliorates this problem, improving the precision of single-entity electrochemical blocking measurements. Using both finite element simulations and experimental statistical analysis we provide guidance for the sizing of redox-inactive materials with high analytical precision under a range of conditions, including variations in electrode size and analyte concentration. The presented nanoscale electrocatalytic approach could offer a novel means of achieving cost-effective, rapid, real-time detection of nanometer-scale “insulators” in complex media, an advance that could be of significant relevance to point-of-care medical diagnostics.
References:
(1) M. Crooks, R. Concluding Remarks: Single Entity Electrochemistry One Step at a Time. Faraday Discuss. 2016, 193 (0), 533–547. https://doi.org/10.1039/C6FD00203J.
(2) V. Sokolov, S.; Eloul, S.; Kätelhön, E.; Batchelor-McAuley, C.; G. Compton, R. Electrode–Particle Impacts: A Users Guide. Phys. Chem. Chem. Phys. 2017, 19 (1), 28–43. https://doi.org/10.1039/C6CP07788A.
(3) Bard, A. J.; Zhou, H.; Kwon, S. J. Electrochemistry of Single Nanoparticles via Electrocatalytic Amplification. Isr. J. Chem. 2010, 50 (3), 267–276. https://doi.org/10.1002/ijch.201000014.
(4) Quinn, B. M.; van’t Hof, P. G.; Lemay, S. G. Time-Resolved Electrochemical Detection of Discrete Adsorption Events. J. Am. Chem. Soc. 2004, 126 (27), 8360–8361. https://doi.org/10.1021/ja0478577.
(5) Deng, Z.; Renault, C. Detection of Individual Insulating Entities by Electrochemical Blocking. Curr. Opin. Electrochem. 2021, 25, 100619. https://doi.org/10.1016/j.coelec.2020.08.001.
(6) Fosdick, S. E.; Anderson, M. J.; Nettleton, E. G.; Crooks, R. M. Correlated Electrochemical and Optical Tracking of Discrete Collision Events. J. Am. Chem. Soc. 2013, 135 (16), 5994–5997. https://doi.org/10.1021/ja401864k.
(7) Deng, Z.; Elattar, R.; Maroun, F.; Renault, C. In Situ Measurement of the Size Distribution and Concentration of Insulating Particles by Electrochemical Collision on Hemispherical Ultramicroelectrodes. Anal. Chem. 2018, 90 (21), 12923–12929. https://doi.org/10.1021/acs.analchem.8b03550.
(Invited) Theranostics with Lanthanide Doped Nanoparticles
Authors: Vetrone Fiorenzo 
Published: July 2021

In recent years, a significant amount of research has focused on nanoparticle-based “theranostic” agents for the treatment of a wide array of diseases, including cancer. This new paradigm in personalized medicine intends to exploit nanoplatforms that carry both therapeutic and diagnostic (theranostic) modalities. Compared with delivering drugs or imaging agents separately, theranostic agents can simultaneously deliver them to specific sites, enabling detection and treatment of disease in a single procedure. Many theranostic nanoplatforms are triggered by light, however, the vast majority of these are limited in that the ultraviolet or visible excitation light used has minimal applicability in biological applications. Lanthanide doped nanoparticles, on the other can be excited with biologically friendly near-infrared light (in the biological windows) and can emit in the ultraviolet, visible or near-infrared regions through a multiphoton upconversion process while simultaneously emitting in the near-infrared region through a Stokes or downshifted process. Hence, the upconversion luminescence can be used to trigger the therapeutic application (drug delivery, photodynamic therapy, etc.) while the near-infrared luminescence can be used for the diagnostic modality (bioimaging, nanothermometry, etc).
In this presentation, we will introduce lanthanide doped nanoparticles and demonstrate their usefulness in theranostics. In particular, we will show complex nanoparticle architectures can endow further functionality to these nanoparticles including the ability to decouple the theranostic processes that are conventionally delivered simultaneously.
(Invited) Tailoring 2D Materials As Artificial Enzymes for Developing Electrochemical Biosensors
Authors: Ebrahimi Aida 
Published: July 2021

There is a significant need for development of low-cost and portable diagnostics for non-invasive monitoring of biochemical markers. Among various biosensors, electrochemical sensors are at the forefront of point of care diagnostics, due to simple and real-time readout, low cost, high sensitivity, and portability, as evident from the dominance of enzymatic glucose sensors in the healthcare sector.(1) Despite their significant success, one of the main challenges with enzyme-based sensors is the limited (or none) choice of enzymes for analytes beyond glucose and their incompatibility with device engineering processes.(2)
Over the past few years, there have been significant advances in flexible sensors, printed electronics, and wireless devices based on graphene and other 2D materials.(2–5) Being atomically thin, with high surface-to-volume ratio, 2D materials are extremely sensitive to surface perturbation, making them suitable for highly sensitive and selective electrochemical sensors. Moreover, their physical, electronic, and electrochemical properties can be modified via doping, defects, and heterostructures to enable new functionalities for biochemical sensing. We have recently made significant progress in developing non-enzymatic electrochemical sensors based on 2D materials and hybrids as ‘artificial enzymes’, with special focus on device scalability and ease of fabrication. In this regard, we have studied the role of substrate in material-analyte interface characteristic,(6) the effect of post-deposition low-temperature annealing of 2D inks on material-analyte interaction, and the importance of defect configuration in tuning the material-analyte band alignment.(7) In this talk, I will present our recent progress and undergoing research in this area.

References:

C. Chen, et al., Current and emerging technology for continuous glucose monitoring. Sensors (Switzerland)
17, 1–19 (2017).

A. Bolotsky, et al., Two-Dimensional Materials in Biosensing and Healthcare: From In Vitro Diagnostics to Optogenetics and Beyond. ACS Nano, acsnano.9b03632 (2019).

S. Khan, S. Ali, A. Bermak, Recent developments in printing flexible and wearable sensing electronics for healthcare applications. Sensors (Switzerland)
19 (2019).

X. Zhang, et al., Two-dimensional MoS2-enabled flexible rectenna for Wi-Fi-band wireless energy harvesting. Nature
566, 368–372 (2019).

D. W. Park, et al., Graphene-based carbon-layered electrode array technology for neural imaging and optogenetic applications. Nat. Commun.
5, 1–11 (2014).

A. Ebrahimi, et al., FeSx-graphene heterostructures: Nanofabrication-compatible catalysts for ultra-sensitive electrochemical detection of hydrogen peroxide. Sensors Actuators, B Chem. (2019) https:/doi.org/10.1016/j.snb.2018.12.033.

Y. Lei, et al., Single-atom doping of MoS2 with manganese enables ultrasensitive detection of dopamine: Experimental and computational approach. Sci. Adv.
6, eabc4250 (2020).
Decorating Metal Oxide Nanostructures with Noble Metal NP for Bio-Sensing Applications
Authors: Almog Rakefet , Shashar Eden , Sverdlov Yelena , Shacham-Diamand Yosi 
Published: July 2021

Biosensors based on functionalized metal oxide nanostructures, such as zinc oxide (ZnO) and Nickel Oxide (NiO), and have been demonstrated using electrochemical methods. Those nanostructures can be deposited on both rigid and flexible substrates at low temperature using rather simple and low-cost processes. Recently, it was discovered that decorating the metal oxide nanostructures with noble metal nanoparticles opens the doors to fabricating biosensors to detect volatile organic compounds.
The aim of this work is to improve the knowledge and understanding of a novel composite material structure where wide bandgap semiconductor metal oxide nanostructures are decorated with gold nanoparticles that their surface can be either decorated by functional biological material or converted to catalytic Pt (or Pd) shell. The metal nanoparticle surface functionalization poses some material-related questions regarding the behavior of that nano-structured material, especially when exposed to biological electrolytes or volatile organic compounds (e.g. Ethylene). This will allow us to develop a novel family of biosensors that enable sensitivity, rapidity, and selectivity for the detection of a wide range of target analytes, from pathogens to volatile organic gases, compared to existing bio-electrochemical sensors.
The final goal is a flexible all-in-one , highly sensitive and selective biosensors using relatively simple and low cost materials and processing. The sensors will use recent advances in functionalized Gold NP decorated ZnO and NiO electrodes based sensors.
ZnO nanowalls was deposited both on rigid (Silicon/ SiO2) substrate and on flexible (Polyimide) substrates. A Scanning Electron Microscopy study of the nucleation and growth of the ZnO nanowalls, and of the NP decoration was conducted. Different conditions such as growth temperature, concentration and time were used. RBS (Rutherford backscattering spectrometry) was performed on the ZnO nanowalls on Polyimide substrate.
After mastering the best methods for ZnO nanowalls growth and decoration with Au NP, decoration with different NP such as Platinum or Palladium will be compared. We believe that decoration of metal oxide nanostructures with noble metal nanoparticles will enhance the electrochemical performance of the sensors, for example - highly sensitive detection of VOCs such as ethylene.
This new structure should be further explored to reveal its full potential for flexible bio-sensor applications.

Figure 1
Enhanced Nitrogen Monoxide Gas-Sensing Performance of Ti-Doped ZnO Nanostructures By SILAR
Authors: Soltabayev Baktiyar , Yergaliuly Gani , Mentbayeva Almagul , Acar Selim 
Published: July 2021

Fig. 1. Schematic illustration of SILAR fabrication process [1].
The use of semiconducting metal oxides to develop highly sensitive NO gas sensors remains an important approach in the field of gas sensing applications [2]. In this research, we describe the synthesis, characterization, and application of a very promissing NO gas sensing material ZnO doped with different atomic percentages of Ti and prepared by a simple SILAR [3] method. Synthesized specimens were structurally and morphologically characterized. The NO sensing characteristics of pristine ZnO and Ti-doped ZnO were compared using a gas sensing measurement system. The sensitivity, operating temperature, and response/recovery time were systematically investigated based on the change in electrical resistance of the materials in the presence of NO. Experimental results confirmed that 50 at% Ti-doped ZnO showed a maximum response to NO gas at an operating temperature. The sensing mechanism of the pristine and Ti-doped ZnO nanostructures is discussed in detail. We believe that the Ti-doped, flower-like ZnO nanostructure is a potential material for semiconductor-oxide-based NO gas sensors.

Key words: NO gas sensor, SILAR method, flower-like, Ti-doped ZnO.

Acknowledgement

This research was supported by the research grant 021220FD2201 “Development of highly sensitive MOS based nano-film gas sensors” from Nazarbayev University.

Reference

[1] B. Soltabayev, M.A. Yıldırım, A. Ateş, S. Acar, The effect of indium doping concentration on structural, morphological and gas sensing properties of IZO thin films deposited SILAR method, Mater. Sci. Semicond. Process. 101 (2019) 28–36. https://doi.org/10.1016/j.mssp.2019.05.026.
[2] V.S. Bhati, M. Hojamberdiev, M. Kumar, Enhanced sensing performance of ZnO nanostructures-based gas sensors: A review, Energy Reports. 6 (2020) 46–62. https://doi.org/10.1016/j.egyr.2019.08.070.
[3] T. Çorlu, I. Karaduman, M.A. Yildirim, A. Ateş, S. Acar, Effect of Doping Materials on the Low-Level NO Gas Sensing Properties of ZnO Thin Films, J. Electron. Mater. 46 (2017) 3995–4002. https://doi.org/10.1007/s11664-017-5503-z.

Figure 1