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Aims Although overt hyperthyroidism adversely affects a patient’s prognosis, thyroid function tests (TFTs) are not routinely conducted. Furthermore, vague symptoms of hyperthyroidism often lead to hyperthyroidism being overlooked. An electrocardiogram (ECG) is a commonly used screening test, and the association between thyroid function and ECG is well known. However, it is difficult for clinicians to detect hyperthyroidism through subtle ECG changes. For early detection of hyperthyroidism, we aimed to develop and validate an electrocardiographic biomarker based on a deep learning model (DLM) for detecting hyperthyroidism. Methods and results This multicentre retrospective cohort study included patients who underwent ECG and TFTs within 24 h. For model development and internal validation, we obtained 174 331 ECGs from 113 194 patients. We extracted 48 648 ECGs from 33 478 patients from another hospital for external validation. Using 500 Hz raw ECG, we developed a DLM with 12-lead, 6-lead (limb leads, precordial leads), and single-lead (lead I) ECGs to detect overt hyperthyroidism. We calculated the model’s performance on the internal and external validation sets using the area under the receiver operating characteristic curve (AUC). The AUC of the DLM using a 12-lead ECG was 0.926 (0.913–0.94) for internal validation and 0.883(0.855–0.911) for external validation. The AUC of DLMs using six and a single-lead were in the range of 0.889–0.906 for internal validation and 0.847–0.882 for external validation. Conclusion We developed a DLM using ECG for non-invasive screening of overt hyperthyroidism. We expect this model to contribute to the early diagnosis of diseases and improve patient prognosis.

Peripartum cardiomyopathy (PPCM) is a fatal maternal complication, with left ventricular systolic dysfunction (LVSD; Left ventricular ejection fraction 45% or less) occurring at the end of pregnancy or in the months following delivery. The scarcity of screening tools for PPCM leads to a delayed diagnosis and increases its mortality and morbidity. We aim to evaluate an electrocardiogram (ECG)-deep learning model (DLM) for detecting cardiomyopathy in the peripartum period.For the DLM development and internal performance test for detecting LVSD, we obtained a dataset of 122,733 ECG-echocardiography pairs from 58,530 male and female patients from two community hospitals. For the DLM external validation, this study included 271 ECG-echocardiography pairs (157 unique pregnant and postpartum period women) examined in the Ajou University Medical Center (AUMC) between January 2007 and May 2020. All included cases underwent an ECG within two weeks before or after the day of transthoracic echocardiography, which was performed within a month before delivery, or within five months after delivery. Based on the diagnostic criteria of PPCM, we analyzed the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) to evaluate the model effectiveness.The ECG-based DLM detected PPCM with an AUROC of 0.877. Moreover, its sensitivity, specificity, PPV, and NPV for the detection of PPCM were 0.877, 0.833, 0.809, 0.352, and 0.975, respectively.An ECG-based DLM non-invasively and effectively detects cardiomyopathies occurring in the peripartum period and could be an ideal screening tool for PPCM.

BACKGROUND Mood disorder has emerged as a serious concern for public health, especially, bipolar disorder has a less favorable prognosis than depression. Although prompt recognition of depression conversion to bipolar disorder is needed, early prediction is challenging due to overlapping symptoms. Recently, there are attempts to develop the prediction model using federated learning. Federated learning in medical fields is a method for training multi-institutional machine learning models without patient-level data sharing. OBJECTIVE This study aimed to develop and validate a federated, differentially private multi-institutional bipolar transition prediction model. METHODS This retrospective study enrolled patients diagnosed with a first depressive episode at five tertiary hospitals in South Korea. We developed model for predicting bipolar transition using data from four institutions, 17,631 patients. Also, we used data from 4,541 patients for external validation from one institution. We created standardized pipelines to extract large-scale clinical features from four institutions without any code modification. Further, we performed feature selection in a federated environment for computational efficiency and applied differential privacy to gradient updates. The study performed feature selection in a federated environment and applied differential privacy to gradient updates. We compared the federated and four local models developed with each hospital data on internal and external validation datasets. RESULTS In the internal dataset, 279 out of 17,631 patients showed bipolar disorder transition. In the external dataset, 60 out of 4,541 patients showed bipolar disorder transition. The average performance of the federated model in the internal test (area under the curve [AUC]: 0.726) and external validation (AUC: 0.719) datasets was higher than other locally developed models (AUC: 0.642-0.707 and AUC: 0.642-0.699, respectively). CONCLUSIONS This study developed and validated a differentially private federated model using distributed multi-institutional psychiatric data with standardized pipelines in a real-world environment. The federated model performed better than models using local data only.

Background: Machine-learning techniques are useful for creating prediction models in clinical practice. This study aimed to construct a prediction model of postoperative 30-day mortality based on an automatically extracted electronic preoperative evaluation sheet. Methods: We used data from 276,341 consecutive adult patients who underwent non-cardiac surgery between January 2011 and December 2020 at a tertiary center for model development and internal validation, and another dataset from 63,384 patients between January 2011 and October 2021 at another center for external validation. Postoperative 30-day mortality was 0.16%. We developed an extreme gradient boosting (XGB) prediction model using only variables from preoperative evaluation sheets. Results: The model yielded an area under the curve of 0.960 and an area under the precision and recall curve of 0.216, which were 0.932 and 0.122, respectively, in the external validation set. The optimal threshold calculated by Youden’s J statistic had a sensitivity of 0.885 and specificity of 0.914. In an additional analysis with balanced distribution, the model showed a similar predictive value. Conclusion: We presented a machine-learning prediction model for 30-day mortality after non-cardiac surgery using preoperative variables automatically extracted from electronic medical records and validated the model in a multi-center setting. Our model may help clinicians predict postoperative outcomes.

Chronic kidney disease–mineral bone disorder (CKD-MBD) is the most common complication in CKD patients. Although there is a consensus on treatment guidelines for CKD-MBD, it remains uncertain whether these treatment recommendations reflect actual practice. Therefore, the aim of this study was to investigate the CKD-MBD medication trend in real-world practice. This was a retrospective and observational study using a 12-year period database transformed into a common data model from three tertiary university hospitals. Study populations were subjects initially diagnosed as CKD. The date of diagnosis was designated as the index date. New patients were categorized year to year from 2008 to 2019 with a fixed observation period of 365 days to check the prescription of CKD-MBD medications including calcium-containing phosphate binder, noncalcium-containing phosphate binder, aluminium hydroxide, vitamin D receptor activator (VDRA), and cinacalcet. The numbers of CKD patients in the three hospitals were 7555, 2424, and 5351, respectively. The proportion for patients with CKD-MBD medication prescription decreased yearly regardless of hospital and CKD stage ( p for trend < 0.05). The use of aluminium hydroxide disappeared steadily while the use of VDRA increased annually in all settings. Despite these changes in prescription patterns, the mean value for CKD-MBD-related serologic markers was almost within target range. The proportion of the population within the target value was not significantly changed. Irrespective of hospital and CKD stage, similar trends of prescription for CKD-MBD medications were observed in real-world practice. Further research with a distributed network study may be helpful to understand medication trends in CKD-MBD treatment.

To investigate how museums will change after the coronavirus disease 2019 (COVID-19) pandemic, and to seek opinions on how well they can secure sustainable competitiveness, this study conducted 15 in-depth interviews using snowball sampling. The interviewees consisted of: an artists’ group, a museum-related group, and a users’ group. Our findings revealed that museums are working hard to gain competitiveness in the face of COVID-19, for which they changed their existing business models to a great extent. One of the most significant changes they introduced, was considering their users as internal, rather than external, stakeholders. For promoting museum products, encouraging users to participate makes them strong supporters who are more active across online platforms and engage and motivate new users, thereby configuring the network effects. The study concludes that this innovative trend will contribute to museums’ sustainable competitiveness during the pandemic as well as in the post-pandemic era.

We describe for the first time molecular rearrangements in a highly stable and porous Ni-rich layered oxide cathode material (LiNi0.80Co0.15Mn0.05O2, Ni-rich NCM) using a thermally reactive, Co-embedded metal–organic framework (MOF). The thermal decomposition of the MOF on the surface of the active material forms a molecular-level thin layer of CoOx species, which are thought to act as seeds for the dramatic transformation of the surface of the Ni-rich NCM from a layered oxide (Rm) to a more stable spinel-like phase (Fdm) before cycling and the formation of multi-scale (nano-to-micro) pores in the active particles. These phase transformations and morphology changes are associated with a galvanic replacement reaction between Co ions from the MOF and Ni ions near the surface of Ni-rich NCM, where some of the Ni ions migrate to the neighboring vacant Li sites by the diffusion of Co ions through melted residual lithium. Therefore, the resultant Co-/Ni-rich surface domains with a more stable spinel-like phase as well as a porous microstructure improve the cyclability and thermal stability of the MOF-inspired Ni-rich NCM.

l-tryptophan (TrP) was investigated as a functional film-forming additive on a lithium-rich layered oxide cathode because it has a much lower oxidation potential than other common carbonate-based electrolytes. Owing to its prior oxidation to a base electrolyte, an artificial cathode-electrolyte interphase (CEI) was formed on the cathode surface, which could be confirmed via X-ray photoelectron spectroscopy and scanning electron microscopy and verified through density functional theory calculations. The functional film formed on the cathode surface suppressed the side reactions between the cathode and electrolyte during cell cycling. As a result, the film prevented CEI thickening and performance deterioration. The optimum weight of TrP was determined to be 0.4 wt % for obtaining the best performance.

The formation of lithium residuals in Ni-rich nickel cobalt manganese oxide is inevitable, due to of the cation mixing by Ni2+ and excess Li required for high energy density. The purpose of this study is to examine the role of Li content in such materials after the washing process, which could reduce Li residuals and enhance the electrochemical performance of lithium ion batteries containing Li1.0Ni0.91Co0.06Mn0.03O2 (NCM) as the cathode. The washing process reduced the Li residuals on the surface as well as the Li in the NCM structure. When a low Li content is used in the NCM, the Li content remaining in its structure after washing is lower than 1 mol. This removal of Li residues from the surface after washing leads to a capacity drop in spite of the improved cycle retention. Therefore, a high Li content is required in order to obtain enhanced electrochemical performance of high-Ni NCM. This trend regarding the Li content is also shown for NCM with small particle size.

Optimizing synthesis parameters is the key to successfully design ideal Ni-rich cathode materials that satisfy principal electrochemical specifications. We herein implement machine learning algorithms using 330 experimental datasets, obtained from a controlled environment for reliability, to construct a predictive model. First, correlation values showed that the calcination temperature and the size of the particles are determining factors for achieving a long cycle life. Then, we compared the accuracy of seven different machine learning algorithms for predicting the initial capacity, capacity retention rate, and amount of residual Li. Remarkable predictive capability was obtained with the average value of coefficient of determinant, R2 = 0.833, from the extremely randomized tree with adaptive boosting algorithm. Furthermore, we propose a reverse engineering framework to search for experimental parameters that satisfy the target electrochemical specification. The proposed results were validated by experiments. The current results demonstrate that machine learning has great potential to accelerate the optimization process for the commercialization of cathode materials.

A retrospective observational study is one of the most widely used research methods in medicine. However, evidence postulated from a single data source likely contains biases such as selection bias, information bias, and confounding bias. Acquiring enough data from multiple institutions is one of the most effective methods to overcome the limitations. However, acquiring data from multiple institutions from many countries requires enormous effort because of financial, technical, ethical, and legal issues as well as standardization of data structure and semantics. The Observational Health Data Sciences and Informatics (OHDSI) research network standardized 928 million unique records or 12% of the world's population into a common structure and meaning and established a research network of 453 data partners from 41 countries around the world. OHDSI is a distributed research network wherein researchers do not own or directly share data but only analyzed results. However, sharing evidence without sharing data is difficult to understand. In this review, we will look at the basic principles of OHDSI, common data model, distributed research networks, and some representative studies in the cardiovascular field using the network. This paper also briefly introduces a Korean distributed research network named FeederNet.

The growing popularity of Li-ion batteries in the field of energy storage necessitates the constant development of methods for improving their performance. To address this task, this study describes the effect of transition metal (TM = Al, Co, Fe) phosphate coatings on the electrochemical performance of Li1.0Ni0.8Co0.15Mn0.05O2 positive electrode materials, highlighting the importance of the TM/P mole ratio, and reveals that decreased amounts of Li residuals were observed for all coated samples. Considering Li residual removal as well as capacity and capacity retention upon cycling, the most effective surface modification was achieved in the case of a 1:1 Co/P coating, which increased initial capacity and capacity retention by 10 mAh g−1 and 3%, respectively, as compared to the uncoated sample. Moreover, the beneficial effect of the above Co/P coating is also confirmed for the more Ni-rich Li1.0Ni0.91Co0.06Mn0.03O2.

Objectives Schoolchildren in developing countries are at greater risk of intestinal parasitic infections. This study aimed to estimate the prevalence and assess the risk factors of intestinal parasite infection among schoolchildren in rural areas of Peru. Methods A volunteer team from the Korea International Cooperation Agency (KOICA) conducted a campaign for parasite eradication called “Chao parasitos” at five schools in the peripheral highland regions of Huanuco in October 2013. The study collected questionnaires and stool samples from children of participating schools. Entamoeba coli, Iodamoeba buschii, and Chilomastix mesnil were classified as nonpathogenic parasites. Results The overall prevalence of intestinal parasite infection in the students was 100% (185/185). Among them, 25.9% (48/185) were infected only with nonpathogenic parasites whereas 74.1% (137/185) were infected with at least one pathogenic parasite. Ascaris lumbricoides was the most commonly detected (37.3%, 69/185), followed by Giardia lamblia (15.1%, 28/185) and I. buschii (11.9%, 22/185). Among lifestyle practices associated with parasitic infection, the rate of washing hands before meals was significantly lower in the students with pathogenic parasites compared to those with nonpathogenic parasites (77.4%, 106/137 vs. 93.8%, 45/48, p = 0.025). Conclusion The prevalence of intestinal parasite was 100%. Both personal hygiene and water supply facilities are required to eradicate parasite infection in rural areas of Peru.

본 논문은 역사적으로 발전해온 의학과 미술의 협업 관계를 고려해서 라 스페콜라 자연사 박물관에서 소장하고 있는 가에타노 줌보의 4점의 소극장과 〈두상의 해부학〉을 재조명한다. 특히 〈두상의 해부학〉은 의학사 분야에서 높이 평가받아왔지만 미술사 분야에서는 많이 연구되지 않았다. 따라서 최초의 다색 해부학 밀랍 모형으로서 접근하는 대신 문화적 의미를 담고 있는 미술품으로서 접근했다. 이를 위해 〈두상의 해부학〉과 같은 시기에 제작된 4점의 소극장은 죽음에 대한 줌보의 생각을 보여준다. 〈두상의 해부학〉은 소극장을 작업한 후 볼로냐에서 인체를 재현한 과학적 지식을 접한 후 제작된 것으로 추정된다. 따라서 소극장 그룹과 〈두상의 해부학〉이 연결고리가 있다고 판단했다. 이 과정에서 소극장 그룹과 연결될 수 있는 부분은 〈두상의 해부학〉의 표정이었다. 〈두상의 해부학〉은 해부학 밀랍 모형이라는 점에서 다시점이 적용된 내부 구조가 중요했다. 반면 작품에서 표피를 구성하는 인물의 표정이 지식의 전달에 반드시 필요한 부분이 아니었기 때문이다. 이를 분석하는 과정에서 동시대의 작품들이나 이후 해부학 모델에서 지속적으로 발견되는 명확하지 않은 감정 표현이 담긴 이 표정은 르네상스 시대 가우리코 이후의 평론에서 설명하는 ‘모호성’과 서사 구조의 특징이 담겨있으며 이는 바로크 시대의 극화된 서사 구조의 특성과도 잘 부합한다는 점에 주목했다. 이를 토대로 동시대 가톨릭 종교개혁의 신학과 이미지에 대한 관점이나 죽음이라는 테마와 연관된 아졸리니와 같이 밀랍을 활용해서 제작한 종교적 봉헌물을 비교해본다면 이 죽음과 연옥의 의미를 환기하는 문화적 혼유라는 사실을 보여준다. 그리고 이 연구는 의학과 미술 두 분야가 상호 융합된다기보다 상호 관점의 차용과 교환 속에서 각 분야를 발전시키면서 인간에 대한 의미체계를 함께 구성해나간다는 점을 보여주었다.

Spinel-embedded lithium-rich oxides are synthesized and their structural phases are analyzed. The type of spinel LiM0.5Mn1.5O4 (M = Ni, Co, Mn) embedded is varied by controlling the spinel composition and content. Of the various composites fabricated with different spinel phases, the LiCo0.5Mn1.5O4-embedded over-lithiated layered oxide (OLO) shows the best electrochemical performance as a cathode because of the absence of a parasitic phase and its high structural stability. The formation energy of the LiCo0.5Mn1.5O4-embedded oxide is determined through first-principles calculations and is found to be lower than that of the pristine oxide as well as other spinel-phase-embedded oxides. It is also observed that use of OLO with optimal embedded spinel LiCo0.5Mn1.5O4 in a cylindrical 18650-type cell results in improvement in the full-cell electrochemical performance.

Ni-rich layered oxides are attractive materials owing to their potentially high capacity for cathode applications. However, when used as cathodes in Li-ion batteries, they contain a large amount of Li residues, which degrade the electrochemical properties because they are the source of gas generation inside the battery. Here, we propose a computational approach to designing optimal coating materials that prevent gas evolution by removing residual Li from the surface of the battery cathode. To discover promising coating materials, the reactions of 16 metal phosphates (MPs) and 45 metal oxides (MOs) with the Li residues, LiOH, and Li2CO3 are examined within a thermodynamic framework. A materials database is constructed according to density functional theory using a hybrid functional, and the reaction products are obtained according to the phases in thermodynamic equilibrium in the phase diagram. In addition, the gravimetric efficiency is calculated to identify coating materials that can eliminate Li residue...

The composite material Li x Ni 0.25 Co 0.10 Mn 0.65 O (3.4 + x) / 2 (x = 1.6, 1.4, 1.2, 1.0, 0.8) were synthesized and characterized for their structural, morphological, and performance as cathode materials in Li-ion batteries. The Rietveld refinement results indicate the presence of two phases at high lithium levels (x = 1.6 and 1.4): Li 2 MnO 3 ( C 2/ m ) and Li M O 2 ( M Ni, Co, Mn) ( R 3¯ m ); the latter contains Ni 2 + and Ni 3 + . At low lithium levels (x = 1.2, 1.0, and 0.8) an additional spinel phase Li M 2 O 4 ( Fd 3¯ m ) emerges, which is known to affect the electrochemical performance of the oxide. Structural analysis reveals that the spinel phase contains mixed transition metals Ni, Co, and Mn as [Li + ,Co 2 + ][Ni 2 + ,Co 3 + ,Mn 4 + ] 2 O 4 . A low lithium level is found to induce primary particle growth, as well as Co and Ni segregation within the secondary particles. These results are expected to contribute to material optimization and commercialization of lithium-rich oxide cathodes.

The effect of bi-functional coatings consisting of Zr and phosphate (P) on the electrochemical performance of Li1.0Ni0.8Co0.15Mn0.05O2 (NCM) has been investigated. The presence of various types of Zr and P compounds such as oxides (ZrO2 and Li2ZrO3) and phosphates (Zr2P2O9, ZrP2O7 and LiZr2(PO4)3) in the coating was confirmed by experiments as well as density functional theory (DFT) calculations. When the NCM samples were coated with the Zr/P hybrid material, the cycle retention and the amount of removed Li residuals (LiOH, Li2CO3) were enhanced by the synergistic effect from Zr and P. The NCM sample coated with a Zr/P layer with a Zr/P ratio of 1 : 1 exhibited an increase in the initial capacity (209.3 mA h g−1) compared to the pristine sample (207.4 mA h g−1) at 0.1C, owing to the formation of the coating layer. The capacity retention of the Zr/P coated sample (92.4% at the 50th cycle) was also improved compared to that of the pristine NCM sample (90.6% at the 50th cycle). Moreover, the amount of Li residuals in the Zr/P coated NCM sample was greatly reduced from 3693 ppm (pristine NCM) to 2525 ppm (Zr/P = 5 : 5).

Ni-rich layered oxides are promising cathode materials due to their high capacities. However, their synthesis process retains a large amount of Li residue on the surface, which is a main source of gas generation during operation of the battery. In this study, combined with simulation and experiment, we propose the optimal metal phosphate coating materials for removing residual Li from the surface of the Ni-rich layered oxide cathode material LiNi0.91Co0.06Mn0.03O2. First-principles-based screening process for 16 metal phosphates is performed to identify an ideal coating material that is highly reactive to Li2O. By constructing the phase diagram, we obtain the equilibrium phases from the reaction of coating materials and Li2O, based on a database using a DFT hybrid functional. Experimental verification for this approach is accomplished with Mn3(PO4)2, Co3(PO4)2, Fe3(PO4)2, and TiPO4. The Li-removing capabilities of these materials are comparable to the calculated results. In addition, electrochemical performances up to 50 charge/discharge cycles show that Mn-, Co-, Fe-phosphate materials are superior to an uncoated sample in terms of preventing capacity fading behavior, while TiPO4 shows poor initial capacity and rapid reduction of capacity during cycling. Finally, Li-containing equilibrium phases examined from XRD analysis are in agreement with the simulation results.

The solvent evaporation method on the structural changes and surface chemistry of the cathode and the effect of electrochemical performance of Li1.0Ni0.8Co0.15Mn0.05O2 (NCM) has been investigated. After dissolving of Li residuals using minimum content of solvent in order to minimize the damage of pristine material and the evaporation time, the solvent was evaporated without filtering and remaining powder was re-heated at 700 °C in oxygen environment. Two kinds of solvent, de-ionized water and diluted nitric acid, were used as a solvent. The almost 40% of Li residuals were removed using solvent evaporation method. The NCM sample after solvent evaporation process exhibited an increase in the initial capacity (214.3 mAh/g) compared to the pristine sample (207.4 mAh/g) at 0.1C because of enhancement of electric conductivity caused by decline of Li residuals. The capacity retention of NCM sample after solvent evaporation process (96.0% at the 50th cycle) was also improved compared to that of the pristine NCM sample (90.6% at the 50th cycle). The uniform Li residual layer after solvent treated and heat treatment acted like a coating layer, leading to enhance the cycle performance. The NCM sample using diluted nitric acid showed better performance than that using de-ionized water.

A chelating-agent-assisted Na2/3Fe1/2Mn1/2O2 material is studied as a cathode for sodium-ion batteries. The addition of NH4OH results in the formation of a homogeneous powder with a lower resistance and larger carbon content on the cathode surface. The formation of a thin and stable solid-electrolyte interface layer leads to its enhanced electrochemical performance.

Mudholkar and Tian (2002) proposed an entropy-based test of fit for the inverse Gaussian distribution; however, the test can be applied to only the composite hypothesis of the inverse Gaussian distribution with an unknown location parameter. In this paper, we propose an entropy-based goodness-of-fit test for an inverse Gaussian distribution that can be applied to the composite hypothesis of the inverse Gaussian distribution as well as the simple hypothesis of the inverse Gaussian distribution with a specified location parameter. The proposed test is based on the probability integration transformation. The critical values of the test statistic estimated by simulations are presented in a tabular form. A simulation study is performed to compare the proposed test under some selected alternatives with Mudholkar and Tian (2002)`s test in terms of power. The results show that the proposed test has better power than the previous entropy-based test.

A Q-Q plot is an effective and convenient graphical method to assess a distributional assumption of data. The primary step in the construction of a Q-Q plot is to obtain a closed-form expression to represent the relation between observed quantiles and theoretical quantiles to be plotted in order that the points fall near the line y

The solvent evaporation method on the structural changes and surface chemistry of the cathode and the effect of electrochemical performance of Li1.0Ni0.8Co0.15Mn0.05O2 (NCM) has been investigated. After dissolving of Li residuals using minimum content of solvent in order to minimize the damage of pristine material and the evaporation time, the solvent was evaporated without filtering and remaining powder was re-heated at 700 °C in oxygen environment. Two kinds of solvent, de-ionized water and diluted nitric acid, were used as a solvent. The almost 40% of Li residuals were removed using solvent evaporation method. The NCM sample after solvent evaporation process exhibited an increase in the initial capacity (214.3 mAh/g) compared to the pristine sample (207.4 mAh/g) at 0.1C because of enhancement of electric conductivity caused by decline of Li residuals. The capacity retention of NCM sample after solvent evaporation process (96.0% at the 50th cycle) was also improved compared to that of the pristine NCM sample (90.6% at the 50th cycle). The uniform Li residual layer after solvent treated and heat treatment acted like a coating layer, leading to enhance the cycle performance. The NCM sample using diluted nitric acid showed better performance than that using de-ionized water.