Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\n
Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\n
Thank you all for being part of the journey. 5,000 times thank you!
\\n\\n
Now with 5,000 titles available Open Access, which one will you read next?
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n
"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\n
Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\n
Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\n\n
Thank you all for being part of the journey. 5,000 times thank you!
\n\n
Now with 5,000 titles available Open Access, which one will you read next?
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"2060",leadTitle:null,fullTitle:"Liver Transplantation - Technical Issues and Complications",title:"Liver Transplantation",subtitle:"Technical Issues and Complications",reviewType:"peer-reviewed",abstract:"This book covers a wide spectrum of topics including, but not limited to, the technical issues in living and deceased donor liver transplant procedures, cell and experimental liver transplantation, and the complications of liver transplantation. 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\n\t\t\t
1. Introduction
\n\t\t\t
Type 1 diabetes mellitus is by far the most common metabolic and endocrinal disease in children (Peters & Schriger, 1997). The major dietary component responsible for fluctuations in blood glucose levels is carbohydrate. The amount, source (Jenkins et al., 1981; Gannon et al., 1989) and type (Brand et al., 1985) of carbohydrate appear to have profound influence on postprandial glucose levels. The chronic hyperglycemia of diabetes is associated with long-term damage, dysfunction and failure of various organs especially the eyes, kidneys, nerves, heart and blood vessels (American Diabetes Association, 2001).
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The glycemic effect of any foodstuff is defined as its effect on blood glucose level postprandially. Both the glycemic index (GI) and the peak incremental index (PII) are used to assess the glycemic effect of different food stuffs (Jenkins et al., 1981). Jennie et al (2003) who studied the use of low glycemic index diets in the management of diabetes found that diets with low glycemic indices (GI), compared with conventional or high-GI diets, improved overall glycemic control in individuals with diabetes, as assessed by glycemic index, peak incremental index, reduced HbA1c and fructosamine. They concluded that using low-GI foods in place of conventional or high-GI foods has a clinically useful effect on postprandial hyperglycemia similar to that offered by pharmacological agents that target postprandial hyperglycemia. Similarly, the American Diabetes Association (2002) stated that the use of low-GI foods may reduce postprandial hyperglycemia.
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Honey is the substance made when the nectar and sweet deposits from plants are gathered, modified and stored in the honeycomb by honey bees. It is composed primarily of the sugars glucose and fructose; its third greatest component is water. Honey also contains numerous other types of sugars, as well as acids, proteins and minerals (White et al., 1962; White, 1980; White, 1975). The water content of honey ranges between 15 to 20% (average 17.2%). Glucose and fructose, the major constituents of honey, account for about 85% of the honey solids. Besides, about 25 different sugars have been detected. The principal oligosaccharides in blossom honeys are disaccharides: sucrose, maltose, turanose, erlose. Trace amounts of tetra and pentasaccharides have also been isolated (Bogdanov, 2010). The protein and amino acid content of honey varies from 0.05 to 0.3 %. The honey proteins are mainly enzymes (White, 1975). Honey also contains varying amounts of mineral substances ranging from 0.02 to 1.03 g/100 g (White, 1975). Among honey benefits are its anti-inflammatory (Al Waili & Boni, 2003), anti- oxidant (Frankel et al., 1998; Gheldof & Engeseth, 2002; Gross et al., 2004) and anti-microbial effects (Molan, 1992; Steinberg et al., 1996; Molan, 1997; Theunissen et al., 2001). Further-more, several studies have shown that honey produced an attenuated postprandial glycemic response when compared with sucrose in both patients with diabetes and normal subjects (Ionescu-Tirgoviste et al., 1983; Shambaugh et al., 1990; Samanta et al., 1985; Al Waili, 2004; Agrawal et al., 2007).
\n\t\t\t
C-peptide is considered to be a good marker of insulin secretion and has no biologic activity of its own (Ido et al., 1997). Measurement of C-peptide, however, provides a fully validated means of quantifying endogenous insulin secretion. C-peptide is co-secreted with insulin by the pancreatic cells as a by-product of the enzymatic cleavage of proinsulin to insulin. Consequently, serum C-peptide level can be used as a true indicator of any change in the insulin level, which is the main determinant of plasma glucose level.
The aim of this work was to compare the effects of honey, sucrose and glucose on plasma glucose and C-peptide levels in children and adolescents with type 1 diabetes mellitus.
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\n\t\t
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3. Subjects and methods
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\n\t\t\t\t
3.1. Subjects
\n\t\t\t\t
Twenty patients with type 1 diabetes mellitus, aged 3–18 (mean 10.95 years) and ten healthy non-diabetic children and adolescents, aged 1–17 (mean 8.5 years) were studied. All subjects were within 68–118% and 77–125% of their ideal body weight and height, respectively. The mean BMI of patients and controls were 22.60 and 23.15, respectively. All patients with diabetes had a mean glycosylated hemoglobin of 9.9% (range = 7–15%). The sex ratio in patients and controls was 1:1. The patients were recruited from the regular attendants of the children clinic of the National Institute of Diabetes in Cairo, Egypt. The study was approved by the local ethical committee, and an informed written consent was obtained from at least one parent of each subject before the study. All patients were receiving three insulin injections per day, each consisting of a mixture of a medium-acting insulin (isophane NPH) and a short-acting soluble insulin (human Actrapid).
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3.2. Methods
\n\t\t\t\t
All patients were primarily diagnosed with type 1 insulin-dependent diabetes mellitus by measuring the serum level of C-peptide on presentation [the patient was considered suffering from insulin-dependent diabetes mellitus type 1 if the C-peptide level was below 0.4 ng/dl (Connors, 2000)]. All subjects were subjected to the following:
\n\t\t\t\t
Anthropometric measures including weight in kg and height in cm which were plotted against percentiles for age and sex.
Oral sugar tolerance tests using glucose, sucrose and honey in three separate sittings: After an overnight fast (8 h) and omission of the morning insulin dose, a calculated amount of each sugar (amount = weight of subject in kg X 1.75 with a maximum of 75 g) (William & Ruchi, 2005) was diluted in 200 ml water and then ingested over 5 min in a random order, on separate mornings 1 week apart. The honey dose for each patient was calculated based on the fact that each 100 gm of the honey used in this study contained 77.3 gm sugars. So if a patient weighs for example 20 kg, he/she should receive 20 x 1.75 = 35 gm sugar which will be present in (35 x 100) ÷ 77.3 = 45.3 gm honey. Venous blood was sampled just before ingestion and then every 30 min postprandial for 2 h thereafter. Samples were left to clot, centrifuged and glucose assay was performed chemically on the Synchron CX5 autoanalyzer (Beckman instruments Inc.) [1] -.
Measurement of fasting and postprandial serum C- peptide level: Venous blood samples were withdrawn from each subject at 0 (fasting) and 2 h postprandial after ingestion of each individual sugar. The samples were then centrifuged and serum was stored in aliquots at – 20 C. At the end of the study, samples were calibrated for C-peptide using the biosource c-pep-easia [1] -, which is a solid phase enzyme amplified sensitivity immunoassay performed on a microtiter plate. A fixed amount of C-peptide labeled with horseradish peroxidase (HRP) competes with unlabeled C-peptide present in the calibrators controls and samples for a limited number of binding sites on a specific antibody. After 2 h incubation at room temperature, the microtiter plates were washed to stop the competition reaction. The chromogenic solution (TMB-H2O2) was added and incubated for 30 min. The reaction was stopped with the addition of stop solution, and the microtiter plate was then read at the appropriate wave length. The amount of substrate turnover was determined colorimetrically by measuring the absorbance which was inversely proportionate to the C-peptide concentration. A calibration curve was plotted and C-peptide concentration in samples was determined by interpolation from the calibration curve.
Calculation of glycemic and peak incremental indices (see example figure 1):
Area under curve (AUC) refers to the area included between the baseline and incremental blood glucose points when connected by straight lines. The area under each incremental glucose curve is calculated using the trapezoid rule (note: only areas above the baseline are used).
Peak incremental index (PII) (Samanta et al., 1985) is defined as the ratio of the maximal increment of plasma glucose produced by sugar to that produced by glucose
\n\t\t\t\t\n\t\t\t\t\t
\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tPeak incremental index \n\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tMaximal increment produced by the sugar tested\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tMaximal increment produced by glucose\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\tE2
\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t
Maximal increment is the difference between the peak point and the fasting point.
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\n\t\t\t
\n\t\t\t\t
3.3. Statistical analysis
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Standard computer program SPSS for Windows, release 13.0 (SPSS Inc., USA) was used for data entry and analysis. All numeric variables were expressed as mean ± standard deviation (SD). Comparison of different variables in various groups was done using student t-test and Mann–Whitney test for normal and non-parametric variables, respectively. Wilcoxon signed
\n\t\t\t\t
rank tests were used to compare multiple readings of the same variables. Chi-square (χ2) test was used to compare frequency of qualitative variables among the different groups (Daniel, 1995).
\n\t\t\t\t
Figure 1.
Oral glucose tolerance curve of one of our patients.
\n\t\t\t\t
For calculation of the area under honey curve (AUC) =A1+A2+A3+A4\n\t\t\t\t
\n\t\t\t\t
A1 is a triangle = 1/2 base x height = 1/2(X2 - X1) x (Y1 - X2) = ½(30) x (144 – 89) = 15 x 55 = 825
\n\t\t\t\t
A2 is a trapezoid = 1/2 sum of the parallel sides (heights) x base
\n\t\t\t\t
= 1/2[(Y1 - X2) + (Y2- X3)] ×(X4- X5) = 1/2[(144 – 89) + (225 – 89)] x 30 = 1/2(55 + 136) x 30 = 1/2(191) x 30 = 95.5 x 30 = 2865
\n\t\t\t\t
A3 is a trapezoid = 1/2 sum of the parallel sides (heights) x base
\n\t\t\t\t
= 1/2[(Y2 – X3) + (Y3- X4)] ×(X4- X3) = 1/2[(225 – 89) + (245 – 89)] x 30 = 1/2(136 + 156) x 30 = 1/2(292) x 30 = 146 x 30 = 4380
\n\t\t\t\t
A4 is a trapezoid = 1/2 sum of the parallel sides (heights) x base
\n\t\t\t\t
= 1/2[(Y3 – X4) + (Y4- X5)] ×(X3- X2) = 1/2[(245 – 89) + (128 – 89)] x 30 = 1/2(156 + 39) x 30 = 1/2(195) x 30 = 97.5 x 30 = 2925
No significant difference was found between patients (diabetics) and controls (non-diabetics) as regards the age and anthropometric measures (table 1). The mean age of subjects in the diabetic and non- diabetic groups was 11.3 and 8.5 years, respectively, with no statistically significant difference between groups (P > 0.05). The mean weight %, height % and body mass index did not also differ significantly between diabetics and non- diabetics (93.6%, 99.2%, 22.6 versus 94%, 98.2%, 23.1, respectively; P > 0.05). The mean plasma glucose level at 0 (fasting) and 30 min postprandial (i.e. 30 min after intake of glucose, sucrose or honey) did not differ significantly between subjects in both groups (diabetics and non-diabetics) (Tables 2 - 5) (P > 0.05). In non-diabetics (control), as shown in tables 2 and 3, the mean plasma glucose level 60, 90 and 120 min after intake of honey became significantly lower than after either glucose or sucrose (P< 0.05). Similarly, as shown in tables 4 and 5, there was a statistically significant decrease of plasma glucose in diabetics at 60, 90 and 120 min after honey intake, when compared with either glucose or sucrose (P< 0.05). The glycemic index (GI) and the peak incremental index (PII) of either sucrose or honey did not differ significantly between patients and controls (P > 0.05). On the other hand, both the GI and PII of honey were significantly lower when compared with sucrose in patients and controls. In non-diabetics, the glycemic index (GI) of honey was 0.69 compared to 1.32 for sucrose, with statistically significant difference (P< 0.05). In diabetics, the GI of honey was also significantly lower than that of sucrose (o.61 versus 1.19, respectively; P< 0.001) (table 6; figure 2). The PII of honey in non-diabetics was 0.61, compared to 1.25 for sucrose (P< 0.05). In diabetics, the PII of honey was also significantly lower than that of sucrose (0.60 versus 1.10, respectively; P< 0.001) (table 7; figure 3).
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The mean (±SD) fasting C-peptide of patients and controls were 0.15 (±0.13) and 1.91 (±0.77) ng/ml, respectively (P< 0.001). All diabetic patients had a basal C-peptide level less than 0.7 ng/ml. In diabetics, although honey intake resulted in increase in the mean level of C-peptide, yet this increase was not statistically significant when compared with either glucose or sucrose (P> 0.05) (Table 8; figure 4). On the other hand, in non-diabetics, honey produced a statistically significant higher C-peptide level, when compared with either glucose or sucrose (P< 0.05) (Table 8; figure 5).
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\n\t\t\t\t\t
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\n\t\t\t\t\t
\n\t\t\t\t\t
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\n\t\t\t\t\t\t
Variable
\n\t\t\t\t\t\t
Diabetics
\n\t\t\t\t\t\t
Non-diabetics
\n\t\t\t\t\t\t
P
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Age (yr)
\n\t\t\t\t\t\t
11.30 ± 4.80
\n\t\t\t\t\t\t
8.50 ± 5.38
\n\t\t\t\t\t\t
"/0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Weight %
\n\t\t\t\t\t\t
93.60 ± 13.82
\n\t\t\t\t\t\t
94.00 ± 14.28
\n\t\t\t\t\t\t
"/0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Height %
\n\t\t\t\t\t\t
99.20 ± 13.01
\n\t\t\t\t\t\t
98.20 ± 11.14
\n\t\t\t\t\t\t
"/0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
BMI
\n\t\t\t\t\t\t
22.59 ± 5.50
\n\t\t\t\t\t\t
23.14 ± 2.90
\n\t\t\t\t\t\t
"/0.05
\n\t\t\t\t\t
\n\t\t\t\t
Table 1.
Age and anthropometric measures in diabetics and non-diabetic controls (mean ± SD).
P > 0.05 is non significant
BMI: Body Mass Index
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Time (min)
\n\t\t\t\t\t\t
Glucose
\n\t\t\t\t\t\t
Honey
\n\t\t\t\t\t\t
P
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
0
\n\t\t\t\t\t\t
75.20 ± 17.45
\n\t\t\t\t\t\t
72.30 ± 9.09
\n\t\t\t\t\t\t
"/ 0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
30
\n\t\t\t\t\t\t
86.00 ± 19.88
\n\t\t\t\t\t\t
83.30 ± 9.52
\n\t\t\t\t\t\t
"/ 0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
60
\n\t\t\t\t\t\t
102.90 ± 24.47
\n\t\t\t\t\t\t
88.80 ± 10.04
\n\t\t\t\t\t\t
< 0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
90
\n\t\t\t\t\t\t
103.60 ± 21.24
\n\t\t\t\t\t\t
88.50 ± 8.64
\n\t\t\t\t\t\t
< 0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
120
\n\t\t\t\t\t\t
91.10 ± 20.74
\n\t\t\t\t\t\t
81.00 ± 8.30
\n\t\t\t\t\t\t
< 0.05
\n\t\t\t\t\t
\n\t\t\t\t
Table 2.
Mean plasma glucose (±SD) (mg/dl) in non-diabetics (control) following equivalent amount of glucose or honey (P < 0.05 is significant).
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Time (min)
\n\t\t\t\t\t\t
Sucrose
\n\t\t\t\t\t\t
Honey
\n\t\t\t\t\t\t
P
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
0
\n\t\t\t\t\t\t
68.50 ± 12.59
\n\t\t\t\t\t\t
72.30 ± 9.09
\n\t\t\t\t\t\t
"/ 0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
30
\n\t\t\t\t\t\t
83.80 ± 13.56
\n\t\t\t\t\t\t
83.30 ± 9.52
\n\t\t\t\t\t\t
"/ 0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
60
\n\t\t\t\t\t\t
101.60 ± 11.45
\n\t\t\t\t\t\t
88.80 ± 10.04
\n\t\t\t\t\t\t
< 0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
90
\n\t\t\t\t\t\t
105.40 ± 18.03
\n\t\t\t\t\t\t
88.50 ± 8.64
\n\t\t\t\t\t\t
< 0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
120
\n\t\t\t\t\t\t
93.60 ± 17.25
\n\t\t\t\t\t\t
81.00 ± 8.30
\n\t\t\t\t\t\t
< 0.05
\n\t\t\t\t\t
\n\t\t\t\t
Table 3.
Mean plasma glucose (±SD) (mg/dl) in non-diabetics (control) following equivalent amount of sucrose or honey (P < 0.05 is significant).
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Time (min)
\n\t\t\t\t\t\t
Glucose
\n\t\t\t\t\t\t
Honey
\n\t\t\t\t\t\t
P
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
0
\n\t\t\t\t\t\t
206.05 ± 95.79
\n\t\t\t\t\t\t
208.10 ± 92.76
\n\t\t\t\t\t\t
"/ 0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
30
\n\t\t\t\t\t\t
257.55 ± 92.79
\n\t\t\t\t\t\t
247.75 ± 99.44
\n\t\t\t\t\t\t
"/ 0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
60
\n\t\t\t\t\t\t
339.80 ± 96.86
\n\t\t\t\t\t\t
285.50 ± 86.29
\n\t\t\t\t\t\t
< 0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
90
\n\t\t\t\t\t\t
328.05 ± 99.75
\n\t\t\t\t\t\t
272.25 ± 85.33
\n\t\t\t\t\t\t
< 0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
120
\n\t\t\t\t\t\t
297.90 ± 106.86
\n\t\t\t\t\t\t
236.75 ± 76.80
\n\t\t\t\t\t\t
< 0.05
\n\t\t\t\t\t
\n\t\t\t\t
Table 4.
Mean plasma glucose (±SD) (mg/dl) in diabetics following equivalent amount of glucose or honey (P < 0.05 is significant).
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Time (min)
\n\t\t\t\t\t\t
Sucrose
\n\t\t\t\t\t\t
Honey
\n\t\t\t\t\t\t
P
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
0
\n\t\t\t\t\t\t
198.30 ± 77.762
\n\t\t\t\t\t\t
208.10 ± 92.76
\n\t\t\t\t\t\t
"/ 0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
30
\n\t\t\t\t\t\t
268.25 ± 78.78
\n\t\t\t\t\t\t
247.75 ± 99.44
\n\t\t\t\t\t\t
"/ 0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
60
\n\t\t\t\t\t\t
320.35 ± 67.17
\n\t\t\t\t\t\t
285.50 ± 86.29
\n\t\t\t\t\t\t
< 0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
90
\n\t\t\t\t\t\t
323.65 ± 71.27
\n\t\t\t\t\t\t
272.25 ± 85.33
\n\t\t\t\t\t\t
< 0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
120
\n\t\t\t\t\t\t
310.15 ± 92.63
\n\t\t\t\t\t\t
236.75 ± 76.80
\n\t\t\t\t\t\t
< 0.05
\n\t\t\t\t\t
\n\t\t\t\t
Table 5.
Mean plasma glucose (±SD) (mg/dl) in diabetics following equivalent amount of sucrose or honey (P < 0.05 is significant).
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
Sucrose
\n\t\t\t\t\t\t
Honey
\n\t\t\t\t\t\t
P
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
GI
\n\t\t\t\t\t\t
GI
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Non- diabetics
\n\t\t\t\t\t\t
1.32 (0.85–1.92)
\n\t\t\t\t\t\t
0.69 (0.43–1.43)
\n\t\t\t\t\t\t
< 0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Diabetics
\n\t\t\t\t\t\t
1.19 (0.31–3.08)
\n\t\t\t\t\t\t
0.61 (0.15–1.92)
\n\t\t\t\t\t\t
< 0.001
\n\t\t\t\t\t
\n\t\t\t\t
Table 6.
Glycemic index (GI) mean (range) of sucrose and honey (glycemic index of glucose = 1) (P < 0.05 is significant; P < 0.001 is highly significant).
\n\t\t\t
Figure 2.
Glycemic index of sucrose and honey.
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
Sucrose
\n\t\t\t\t\t\t
Honey
\n\t\t\t\t\t\t
P
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
PII
\n\t\t\t\t\t\t
PII
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Non- diabetics
\n\t\t\t\t\t\t
1.25 (0.50–1.82)
\n\t\t\t\t\t\t
0.61 (0.30–1.10)
\n\t\t\t\t\t\t
< 0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Diabetics
\n\t\t\t\t\t\t
1.10 (0.65–2.98)
\n\t\t\t\t\t\t
0.60 (0.20–1.60)
\n\t\t\t\t\t\t
< 0.001
\n\t\t\t\t\t
\n\t\t\t\t
Table 7.
Peak incremental index (PII) mean (range) of sucrose and honey (peak incremental index of glucose = 1) (P < 0.05 is significant; P < 0.001 is highly significant).
\n\t\t\t
Figure 3.
Peak incremental index of sucrose and honey.
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Group
\n\t\t\t\t\t\t
C-peptide (ng/ml)
\n\t\t\t\t\t\t
P
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
After glucose
\n\t\t\t\t\t\t
After sucrose
\n\t\t\t\t\t\t
After honey
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Non-diabetics
\n\t\t\t\t\t\t
3.96 ± 0.84
\n\t\t\t\t\t\t
3.99 ± 1.10
\n\t\t\t\t\t\t
5.50 ± 1.15
\n\t\t\t\t\t\t
P < 0.05
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Diabetics
\n\t\t\t\t\t\t
0.29 ± 0.53
\n\t\t\t\t\t\t
0.32 ± 0.53
\n\t\t\t\t\t\t
0.47 ± 1.09
\n\t\t\t\t\t\t
P "/ 0.05
\n\t\t\t\t\t
\n\t\t\t\t
Table 8.
Mean C-peptide (±SD) (ng/ml) following equivalent amount of glucose, sucrose or honey in non-diabetics and diabetics (P < 0.05 is significant).
\n\t\t\t
Figure 4.
C-peptide following equivalent amount of glucose, sucrose or honey in diabetics.
\n\t\t\t
Figure 5.
C-peptide following equivalent amount of glucose, sucrose or honey in non-diabetics.
\n\t\t\t\tJenkins (1987) defined the glycemic index as the ratio between the blood glucose areas produced after ingestion of a studied sugar compared to the blood glucose area produced after glucose ingestion itself. He stated that the glycemic response to food affects the insulin response which in turn is also potentiated by other non-glucose dependent factors in this food (Ostman et al., 2001). On the other hand, FAO/WHO (1998) defined the glycemic index as the incremental blood glucose area (0–2 h) following ingestion of 50 g of available carbohydrates (no fibers or resistant starch included), expressed as a percentage of the corre-sponding area following an equivalent amount of carbohydrate from a standard reference product. Samnata et al (1985) defined the peak incremental index of a certain sugar as the ratio between the maximal increments of the glucose level after ingestion of the sugar compared to the maximal increment produced after ingestion of glucose. He also mentioned that both the glycemic and the peak incremental indices are closely related, highly dependent and positively correlated to the plasma glucose produced after ingestion of any given sugar. Therefore, any change in the plasma glucose level after ingestion of a certain sugar will markedly affect both the glycemic index and the peak incremental index. Hence, the glycemic and the peak incremental indices measure how fast and how much a food raises blood glucose levels. Foods with higher index values raise blood sugar more rapidly than foods with lower index values do in case of the glycemic index and much more in case of peak incremental index.
\n\t\t\t
In our study, no statistically significant differences were found between diabetic patients and non-diabetic controls regarding the glycemic and the peak incremental indices of the studied sugars. Similarly, Samnata et al (1985), who studied the glycemic effect of glucose, sucrose and honey in 12 normal volunteers, eight patients with insulin-dependent diabetes mellitus (IDDM) and six patients with non-insulin-dependent diabetes mellitus (NIDDM), found no significant differences between the normal volunteers and diabetic patients regarding the glycemic and peak incremental indices of both sugars. Since the glycemic index (GI) is the ratio between the area under curve (AUC) of the studied sugar and the AUC of glucose, and the peak incremental index (PII) is the ratio between the maximal blood glucose increment of the studied sugar and that of glucose; it may be expected that both GI and PII will be the same in both diabetics and non-diabetics. Our study showed that honey has statistically significant lower glycemic and peak incremental indices than sucrose and glucose in both patients and controls (< 1 with honey, 1 with glucose being the reference sugar and >1 with sucrose). In agreement, Kaye et al (2002), who published the international table of glycemic index and glycemic load values, found that the GI of honey (0.55 ± 0.05) was lower than that of sucrose (1.10 ± 0.21). Also, Shambaugh et al (1990) found that sucrose caused higher blood sugar readings than honey in normal volunteers. In the study of Samnata et al (1985), honey ingestion in both diabetics (IDDM) and non-diabetics also resulted in a significantly lower PII compared to the glucose and sucrose. In the study done by Al-Waili (2004), honey compared with dextrose and sucrose caused a lower elevation of
\n\t\t\t
plasma glucose levels (PGL) in both diabetics (IDDM) and normal subjects. In an attempt to explain his results, he stated that the mild reduction of plasma glucose levels obtained by honey might be a result of the fructose content of honey which requires metabolic transformation in the liver, a slow process conferring relatively low-GI on these sugars (Jenkins et al., 1981; Wolever et al., 1991). Also, Watford (2002) demonstrated that very small amounts of fructose, which is the main component of honey, could increase hepatic glucose uptake and glycogen storage, as well as reduce peripheral glycemia which could be beneficial in diabetic patients. In the study performed by Agrawal et al (2007), honey was found to produce an attenuated postprandial glycemic response especially in subjects with glucose intolerance. They referred these results to the possibility that the glucose component of honey might be poorly absorbed from the gut epithelium. Also, Tirgoviste et al (1983) studied blood glucose and plasma insulin responses to various carbohydrates in type 2 diabetes, and they found that the increase in plasma glucose was significantly higher after administration of more refined carbohydrates such as glucose than after the complex ones such as honey. Meanwhile, Oizumi et al (2007) and Arai et al (2004) found that consumption of a palatinose (a disaccharide found in honey)-based balanced formula suppressed postprandial hyperglycemia, glycemic and peak incremental indices and produced beneficial effects on the metabolic syndrome–related parameters (namely, the lipid profile and visceral fat accumulation) in diabetic patients. They stated the reason of this observation to be due to the fact that although palatinose is completely absorbed, yet it has the specific characteristics of delayed digestion and absorption as reported by Dahlquist et al (1963) and Lina et al (2002).
\n\t\t\t
Our results showed that honey, compared to glucose and sucrose, caused a significant elevation in the C-peptide levels in non-diabetic subjects. Meanwhile, in diabetic patients, the plasma C-peptide levels did not differ significantly between the three types of sugars. To our knowledge, no similar work was done to study the effects of honey on C-peptide levels in type 1 diabetes mellitus. However, several studies were performed in healthy and in type 2 diabetic patients to evaluate the effects of honey on the insulin and C-peptide levels, and the results were controversial. In the study of Al Waili (2003), inhalation of honey solution, when compared with hyperosmolar dextrose and hypoosmolar distilled water, resulted in a significant elevation of plasma insulin and C-peptide in both normal individuals and in patients with type 2 diabetes mellitus. However, in 2004, the same author found that honey ingestion, when compared with sucrose, caused a greater elevation of insulin and C-peptide in type 2 diabetic patients, while in healthy subjects dextrose ingestion caused a significant elevation of plasma insulin and C-peptide when compared with honey. The author hypothesized that honey may have the ability to stimulate insulin production and secretion from the pancreas than do sucrose in type 2 diabetes mellitus. On the other hand, Bornet et al (1985) reported no significant changes in plasma insulin levels after honey ingestion compared to sucrose in type 2 diabetics. Liljeberg et al (1999) found that high-GI foods induced a greater elevation of blood insulin than did low glycemic index meals (like honey). Elliott et al (2002) tried to explore whether fructose consumption might be a contributing factor to the development of obesity and the accompanying metabolic abnormalities observed in the insulin resistance syndrome and they found that honey intake caused a significant lowering of plasma insulin and C-peptide in normal subjects when compared to sucrose and dextrose. They related their findings to the fructose content of honey which does not stimulate insulin secretion from pancreatic beta cells and that consumption of foods and beverages containing fructose produced a smaller postprandial insulin excursions than did consumption of glucose-containing carbohydrates (Glinsmann & Bowman, 1993). Also, Watford et al (2002) stated that very small amounts of fructose, which is the main component of honey, could increase hepatic glucose uptake and glycogen storage, as well as reduce peripheral glycemia and thus insulin levels. Ionescu-Tirgoviste et al (1983) studied the blood glucose and plasma insulin responses to some simple carbohydrates (glucose, fructose, lactose) and some complex ones (apples, potatoes, bread, rice, carrots and honey) in 32 type 2 (non-insulin-dependent) diabetic patients, and they found that increases in plasma insulin were significantly higher after the more refined carbohydrates (glucose, fructose and lactose) than after the more complex ones (apples, potatoes, rice, carrots and honey, P less than 0.01).
\n\t\t\t
We hypothesize that honey might have a direct stimulatory effect on the healthy beta cells of pancreas; an effect which may be related to the non-sugar part of honey. This hypothesis is based on the finding that honey caused significant postprandial increase in the C-peptide level despite its lower glycemic and peak incremental indices when compared to either glucose or sucrose. On the other hand, the lack of significant increase in C-peptide levels among diabetic patients might be due to the minimal residual function of the patient’s pancreatic beta cells, which is beyond their capacity of further postprandial response. This proposal is backed up by the findings of Pozzan et al (1997) who investigated the relation between the fasting C-peptide level and the ability to respond to a particular stimulus, and they reported that there is a positive significant correlation between the basal value (BV) and the peak value (PV) of C-peptide in insulin dependent diabetic patients and that positive responses need a minimal basal level of 0.74 ng/ml. In all our studied patients, the basal C-peptide level was less than 0.7 ng/ml. Also other authors found significant correlations between the basal and the maximum C-peptide values after a stimulus. However, they reported different basal values which can respond to stimulation. Such values were 0.09 (Clarson et al., 1987), 0.18 (Eff et al., 1989) and 0.39 ng/ml (Faber & Binder, 1977). The variation in these levels was probably due to the different ages and different diabetes duration of the studied populations (Pozzan et al., 1997).
\n\t\t
\n\t\t
\n\t\t\t
6. Conclusions and recommendations
\n\t\t\t
Honey has a lower glycemic and peak incremental indices compared to glucose and sucrose in both type 1 diabetic patients and non-diabetics. Therefore, we recommend using honey as a sugar substitute in type 1 diabetic patients.
In spite of its significantly lower glycemic and peak incremental indices, honey caused significant post- prandial rise of plasma C-peptide levels when compared to glucose and sucrose in non-diabetics; indicating that honey may have a direct stimulatory effect on the healthy beta cells of pancreas. On the other hand, C-peptide levels were not significantly elevated after honey ingestion when compared with either glucose or sucrose in type 1 diabetic patients. Whether or not ingestion of honey in larger doses or/and for an extended period of time would have a significant positive effect on the diseased beta cells, needs further studies.
\n\t\t
\n\t\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/24101.pdf",chapterXML:"https://mts.intechopen.com/source/xml/24101.xml",downloadPdfUrl:"/chapter/pdf-download/24101",previewPdfUrl:"/chapter/pdf-preview/24101",totalDownloads:3389,totalViews:519,totalCrossrefCites:0,totalDimensionsCites:3,hasAltmetrics:0,dateSubmitted:"November 17th 2010",dateReviewed:"March 2nd 2011",datePrePublished:null,datePublished:"November 21st 2011",dateFinished:null,readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/24101",risUrl:"/chapter/ris/24101",book:{slug:"type-1-diabetes-complications-pathogenesis-and-alternative-treatments"},signatures:"Mamdouh Abdulrhman, Mohamed El Hefnawy, Rasha Ali and Ahmad Abou El-Goud",authors:[{id:"46730",title:"Prof.",name:"Mamdouh",middleName:null,surname:"Abdulrhman",fullName:"Mamdouh Abdulrhman",slug:"mamdouh-abdulrhman",email:"mamdouh565@hotmail.com",position:null,institution:null},{id:"47621",title:"Prof.",name:"Mohamed",middleName:null,surname:"El-Hefnawy",fullName:"Mohamed El-Hefnawy",slug:"mohamed-el-hefnawy",email:"drhefnawy@yahoo.com",position:null,institution:{name:"National Cancer Institute",institutionURL:null,country:{name:"United States of America"}}},{id:"47623",title:"Dr.",name:"Rasha",middleName:null,surname:"Hussein",fullName:"Rasha Hussein",slug:"rasha-hussein",email:"rashahusseinaly@yahoo.com",position:null,institution:null},{id:"47624",title:"Dr.",name:"Ahmad",middleName:null,surname:"Abou El-Goud",fullName:"Ahmad Abou El-Goud",slug:"ahmad-abou-el-goud",email:"ahmedaboelgoad@yahoo.com",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction ",level:"1"},{id:"sec_2",title:"2. Aim of the study",level:"1"},{id:"sec_3",title:"3. Subjects and methods",level:"1"},{id:"sec_3_2",title:"3.1. Subjects",level:"2"},{id:"sec_4_2",title:"3.2. Methods",level:"2"},{id:"sec_5_2",title:"3.3. Statistical analysis",level:"2"},{id:"sec_7",title:"4. Results",level:"1"},{id:"sec_8",title:"5. Discussion ",level:"1"},{id:"sec_9",title:"6. 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Apidologie 32\n\t\t\t\t\t4\n\t\t\t\t\t371\n\t\t\t\t\t379\n\t\t\t\t\n\t\t\t'},{id:"B48",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tTominaga\n\t\t\t\t\t\t\tM.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tEguchi\n\t\t\t\t\t\t\tH.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tManaka\n\t\t\t\t\t\t\tH.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tIgarashi\n\t\t\t\t\t\t\tK.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKato\n\t\t\t\t\t\t\tT.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSekikawa\n\t\t\t\t\t\t\tA.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1999 Impaired glucose tolerance is a risk factor for cardiovascular disease, but not impaired fasting glucose. The Funagata Diabetes Study. Diabetes Care 22\n\t\t\t\t\t920\n\t\t\t\t\t924\n\t\t\t\t\n\t\t\t'},{id:"B49",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tWatford\n\t\t\t\t\t\t\tM.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2002 Small amounts of dietary fructose dramatically increase hepatic glucose uptake. 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Pediatric Department, Faculty of Medicine, Ain Shams University, Abbasia - Cairo
'},{corresp:null,contributorFullName:"Mohamed El Hefnawy",address:null,affiliation:'
Pediatric Department, Faculty of Medicine, Ain Shams University, Abbasia - Cairo
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1. Introduction
Stability constant of the formation of metal complexes is used to measure interaction strength of reagents. From this process, metal ion and ligand interaction formed the two types of metal complexes; one is supramolecular complexes known as host-guest complexes [1] and the other is anion-containing complexes. In the solution it provides and calculates the required information about the concentration of metal complexes.
Solubility, light, absorption conductance, partitioning behavior, conductance, and chemical reactivity are the complex characteristics which are different from their components. It is determined by various numerical and graphical methods which calculate the equilibrium constants. This is based on or related to a quantity, and this is called the complex formation function.
During the displacement process at the time of metal complex formation, some ions disappear and form a bonding between metal ions and ligands. It may be considered due to displacement of a proton from a ligand species or ions or molecules causing a drop in the pH values of the solution [2]. Irving and Rossotti developed a technique for the calculation of stability constant, and it is called potentiometric technique.
To determine the stability constant, Bjerrum has used a very simple method, and that is metal salt solubility method. For the studies of a larger different variety of polycarboxylic acid-, oxime-, phenol-containing metal complexes, Martel and Calvin used the potentiometric technique for calculating the stability constant. Those ligands [3, 4] which are uncharged are also examined, and their stability constant calculations are determined by the limitations inherent in the ligand solubility method. The limitations of the metal salt solubility method and the result of solubility methods are compared with this. M-L, MLM, and (M3) L are some types of examples of metal-ligand bonding. One thing is common, and that is these entire types metal complexes all have one ligand.
The solubility method can only usefully be applied to studies of such complexes, and it is best applied for ML; in such types of system, only ML is formed. Jacqueline Gonzalez and his co-worker propose to explore the coordination chemistry of calcium complexes. Jacqueline and et al. followed this technique for evaluate the as partial model of the manganese-calcium cluster and spectrophotometric studies of metal complexes, i.e., they were carried calcium(II)-1,4-butanediamine in acetonitrile and calcium(II)-1,2-ethylendiamine, calcium(II)-1,3-propanediamine by them.
Spectrophotometric programming of HypSpec and received data allows the determination of the formation of solubility constants. The logarithmic values, log β110 = 5.25 for calcium(II)-1,3-propanediamine, log β110 = 4.072 for calcium(II)-1,4-butanediamine, and log β110 = 4.69 for calcium(II)-1,2-ethylendiamine, are obtained for the formation constants [5]. The structure of Cimetidine and histamine H2-receptor is a chelating agent. Syed Ahmad Tirmizi has examined Ni(II) cimetidine complex spectrophotometrically and found an absorption peak maximum of 622 nm with respect to different temperatures.
Syed Ahmad Tirmizi have been used to taken 1:2 ratio of metal and cimetidine compound for the formation of metal complex and this satisfied by molar ratio data. The data, 1.40–2.4 × 108, was calculated using the continuous variation method and stability constant at room temperature, and by using the mole ratio method, this value at 40°C was 1.24–2.4 × 108. In the formation of lead(II) metal complexes with 1-(aminomethyl) cyclohexene, Thanavelan et al. found the formation of their binary and ternary complexes. Glycine, l-proline, l-alanine, l-isoleucine, l-valine, and l-leucine are α-amino acids, and these are important biologically [6]. These α-amino acids are also investigated by potentiometric technique at 32°C. The mixed ligands were also studied using these methods. 50% (v/v) DMSO-water medium used for the determination of acidity constants and their stability constants these type ligands. In a stepwise manner, the ternary complexes were synthesized.
Using the stability constant method, these ternary complexes were found out, and using the parameters such as Δ log K and log X, these ternary complex data were compared with binary complex. The potentiometric technique at room temperature (25°C) was used in the investigation of some binary complex formations by Abdelatty Mohamed Radalla. These binary complexes are formed with 3D transition metal ions like Cu2+, Ni2+, Co2+, and Zn2+ and gallic acid’s importance as a ligand and 0.10 mol dm−3 of NaNO3. Such types of aliphatic dicarboxylic acids are very important biologically. Many acid-base characters and the nature of using metal complexes have been investigated and discussed time to time by researchers [7].
The above acids (gallic and aliphatic dicarboxylic acid) were taken to determine the acidity constants. For the purpose of determining the stability constant, binary and ternary complexes were carried in the aqueous medium using the experimental conditions as stated above. The potentiometric pH-metric titration curves are inferred for the binary complexes and ternary complexes at different ratios, and formation of ternary metal complex formation was in a stepwise manner that provided an easy way to calculate stability constants for the formation of metal complexes.
The values of Δ log K, percentage of relative stabilization (% R. S.), and log X were evaluated and discussed. Now it provides the outline about the various complex species for the formation of different solvents, and using the concentration distribution, these complexes were evaluated and discussed. The conductivity measurements have ascertained for the mode of ternary chelating complexes.
A study by Kathrina and Pekar suggests that pH plays an important role in the formation of metal complexes. When epigallocatechin gallate and gallic acid combine with copper(II) to form metal complexes, the pH changes its speculation. We have been able to determine its pH in frozen and fluid state with the help of multifrequency EPR spectroscopy [8]. With the help of this spectroscopy, it is able to detect that each polyphenol exhibits the formation of three different mononuclear species. If the pH ranges 4–8 for di- or polymeric complex of Cu(II), then it conjectures such metal complexes. It is only at alkaline pH values.
The line width in fluid solutions by molecular motion exhibits an incomplete average of the parameters of anisotropy spin Hamilton. If the complexes are different, then their rotational correlation times for this also vary. The analysis of the LyCEP anisotropy of the fluid solution spectra is performed using the parameters determined by the simulation of the rigid boundary spectra. Its result suggests that pH increases its value by affecting its molecular mass. It is a polyphenol ligand complex with copper, showing the coordination of an increasing number of its molecules or increasing participation of polyphenol dimers used as ligands in the copper coordination region.
The study by Vishenkova and his co-worker [8] provides the investigation of electrochemical properties of triphenylmethane dyes using a voltammetric method with constant-current potential sweep. Malachite green (MG) and basic fuchsin (BF) have been chosen as representatives of the triphenylmethane dyes [9]. The electrochemical behavior of MG and BF on the surface of a mercury film electrode depending on pH, the nature of background electrolyte, and scan rate of potential sweep has been investigated.
Using a voltammetric method with a constant-current potential sweep examines the electrical properties of triphenylmethane dye. In order to find out the solution of MG and BF, certain registration conditions have been prescribed for it, which have proved to be quite useful. The reduction peak for the currents of MG and BF has demonstrated that it increases linearly with respect to their concentration as 9.0 × 10−5–7.0 × 10−3 mol/dm3 for MG and 6.0 × 10−5–8.0 × 10−3 mol/dm3 for BF and correlation coefficients of these values are 0.9987 for MG and 0.9961 for BF [10].
5.0 × 10−5 and 2.0 × 10−5 mol/dm3 are the values used as the detection limit of MG and BF, respectively. Stability constants are a very useful technique whose size is huge. Due to its usefulness, it has acquired an umbrella right in the fields of chemistry, biology, and medicine. No science subject is untouched by this. Stability constants of metal complexes are widely used in the various areas like pharmaceuticals as well as biological processes, separation techniques, analytical processes, etc. In the presented chapter, we have tried to explain this in detail by focusing our attention on the applications and solutions of stability of metal complexes in solution.
2. Stability constant of metal complexes
Stability or formation or binding constant is the type of equilibrium constant used for the formation of metal complexes in the solution. Acutely, stability constant is applicable to measure the strength of interactions between the ligands and metal ions that are involved in complex formation in the solution [11]. A generally these 1-4 equations are expressed as the following ways:
Metal+Ligand⇆Metal−LigandK1=MLMLE1
Metal+Ligands⇆Metal+Ligand2K2=ML2MLLE2
Metal+Ligand3⇆Metal+Ligand3K3=ML3ML2LE3
Thus
Metal+Ligandn−1+L⇆Metal+Ligand−nKn=MLnMLn−1LE4
K1, K2, K3, … Kn are the equilibrium constants and these are also called stepwise stability constants. The formation of the metal-ligand-n complex may also be expressed as equilibrium constants by the following steps:
Metal+Ligand→B1Metal−Ligand,β=MLMLE5
Metal+2Ligand→B2Metal−Ligand2,β2=ML2ML2E6
Thus Metal+nLigand→BnMetal−ligandLn,βn=MLnMLnE7
β1, β2, β3, … βn are the equilibrium constants, and these equilibrium constants are known as overall stability constants or overall formation. βn is called as the nth cumulative or overall formation constant [12]. Any metal complexes will be of greater stability if its stability constant has the higher value. Sometimes the 1/k values are alternative values of stability constant, and now this is called as instability constant. Log10K1, log10K2 … log10Kn, and log10βn are the ways that expressed the stepwise and cumulative stability constants.
3. Relationship or interaction between βn and K1, K2, K3, … Kn
The parameters K and β are related together, and these are expressed in the following example:
β3=ML3ML3E8
Now the numerator and denominator are multiplied together with the use of [metal-ligand] [metal-ligand2], and after the rearranging we get the following equation:
From the above relation, it is clear that the overall stability constant βn is equal to the product of the successive (i.e., stepwise) stability constants, K1, K2, K3,…Kn. This in other words means that the value of stability constants for a given complex is actually made up of a number of stepwise stability constants. The term stability is used without qualification to mean that the complex exists under a suitable condition and that it is possible to store the complex for an appreciable amount of time. The term stability is commonly used because coordination compounds are stable in one reagent but dissociate or dissolve in the presence of another regent. It is also possible that the term stability can be referred as an action of heat or light or compound. The stability of complex [13] is expressed qualitatively in terms of thermodynamic stability and kinetic stability.
3.1 Thermodynamic stability
In a chemical reaction, chemical equilibrium is a state in which the concentration of reactants and products does not change over time. Often this condition occurs when the speed of forward reaction becomes the same as the speed of reverse reaction. It is worth noting that the velocities of the forward and backward reaction are not zero at this stage but are equal.
If hydrogen and iodine are kept together in molecular proportions in a closed process vessel at high temperature (500°C), the following action begins:
H2+I2→2HIE12
In this activity, hydrogen iodide is formed by combining hydrogen and iodine, and the amount of hydrogen iodide increases with time. In contrast to this action, if the pure hydrogen iodide gas is heated to 500°C in the reaction, the compound is dissolved by reverse action, which causes hydrogen iodide to dissolve into hydrogen and iodine, and the ratio of these products increases over time. This is expressed in the following reaction:
2HI→H2+I2E13
For the formation of metal chelates, the thermodynamic technique provides a very significant information. Thermodynamics is a very useful technique in distinguishing between enthalpic effects and entropic effects. The bond strengths are totally effected by enthalpic effect, and this does not make any difference in the whole solution in order/disorder. Based on thermodynamics the chelate effect below can be best explained. The change of standard Gibbs free energy for equilibrium constant is response:
ΔG=−2.303RTlog10β.E14
Where:
R = gas constant
T = absolute temperature
At 25°C,
ΔG = (− 5.708 kJ mol−1) · log β.
The enthalpy term creates free energy, i.e.,
ΔG=ΔH–TΔSE15
For metal complexes, thermodynamic stability and kinetic stability are two interpretations of the stability constant in the solution. If reaction moves from reactants to products, it refers to a change in its energy as shown in the above equation. But for the reactivity, kinetic stability is responsible for this system, and this refers to ligand species [14].
Stable and unstable are thermodynamic terms, while labile and inert are kinetic terms. As a rule of thumb, those complexes which react completely within about 1 minute at 25°C are considered labile, and those complexes which take longer time than this to react are considered inert. [Ni(CN)4]2− is thermodynamically stable but kinetically inert because it rapidly exchanges ligands.
The metal complexes [Co(NH3)6]3+ and such types of other complexes are kinetically inert, but these are thermodynamically unstable. We may expect the complex to decompose in the presence of acid immediately because the complex is thermodynamically unstable. The rate is of the order of 1025 for the decomposition in acidic solution. Hence, it is thermodynamically unstable. However, nothing happens to the complex when it is kept in acidic solution for several days. While considering the stability of a complex, always the condition must be specified. Under what condition, the complex which is stable or unstable must be specified such as acidic and also basic condition, temperature, reactant, etc.
A complex may be stable with respect to a particular condition but with respect to another. In brief, a stable complex need not be inert and similarly, and an unstable complex need not be labile. It is the measure of extent of formation or transformation of complex under a given set of conditions at equilibrium [15].
Thermodynamic stability has an important role in determining the bond strength between metal ligands. Some complexes are stable, but as soon as they are introduced into aqueous solution, it is seen that these complexes have an effect on stability and fall apart. For an example, we take the [Co (SCN)4]2+ complex. The ion bond of this complex is very weak and breaks down quickly to form other compounds. But when [Fe(CN)6]3− is dissolved in water, it does not test Fe3+ by any sensitive reagent, which shows that this complex is more stable in aqueous solution. So it is indicated that thermodynamic stability deals with metal-ligand bond energy, stability constant, and other thermodynamic parameters.
This example also suggests that thermodynamic stability refers to the stability and instability of complexes. The measurement of the extent to which one type of species is converted to another species can be determined by thermodynamic stability until equilibrium is achieved. For example, tetracyanonickelate is a thermodynamically stable and kinetic labile complex. But the example of hexa-amine cobalt(III) cation is just the opposite:
CoNH363++6H3O+→CoH2O63++6NH4+E16
Thermodynamics is used to express the difference between stability and inertia. For the stable complex, large positive free energies have been obtained from ΔG0 reaction. The ΔH0, standard enthalpy change for this reaction, is related to the equilibrium constant, βn, by the well thermodynamic equation:
ΔG0=−RTlnβE17
ΔG0=ΔH0−TΔS0E18
For similar complexes of various ions of the same charge of a particular transition series and particular ligand, ΔS0 values would not differ substantially, and hence a change in ΔH0 value would be related to change in βn values. So the order of values of ΔH0 is also the order of the βn value.
3.2 Kinetic stability
Kinetic stability is referred to the rate of reaction between the metal ions and ligand proceeds at equilibrium or used for the formation of metal complexes. To take a decision for kinetic stability of any complexes, time is a factor which plays an important role for this. It deals between the rate of reaction and what is the mechanism of this metal complex reaction.
As we discuss above in thermodynamic stability, kinetic stability is referred for the complexes at which complex is inert or labile. The term “inert” was used by Tube for the thermally stable complex and for reactive complexes the term ‘labile’ used [16]. The naturally occurring chlorophyll is the example of polydentate ligand. This complex is extremely inert due to exchange of Mg2+ ion in the aqueous media.
4. Factors affecting the stability of complexes
The nature of central atom of metal complexes, dimension, its degree of oxidation, electronic structure of these complexes, and so many other properties of complexes are affected by the stability constant. Some of the following factors described are as follows.
4.1 Nature of central metal ion
In the coordination chemistry, metal complexes are formed by the interaction between metal ions and ligands. For these type of compounds, metal ions are the coordination center, and the ligand or complexing agents are oriented surrounding it. These metal ions mostly are the transition elements. For the determination of stability constant, some important characteristics of these metal complexes may be as given below.
4.2 Ionic size
Ligands are oriented around the central metal ions in the metal complexes. The sizes of these metal ions determine the number of ligand species that will be attached or ordinated (dative covalent) in the bond formation. If the sizes of these metal ions are increased, the stability of coordination compound defiantly decreased. Zn(II) metal ions are the central atoms in their complexes, and due to their lower size (0.74A°) as compared to Cd(II) size (0.97A°), metal ions are formed more stable.
Hence, Al3+ ion has the greatest nuclear charge, but its size is the smallest, and the ion N3− has the smallest nuclear charge, and its size is the largest [17]. Inert atoms like neon do not participate in the formation of the covalent or ionic compound, and these atoms are not included in isoelectronic series; hence, it is not easy to measure the radius of this type of atoms.
4.3 Ionic charge
The properties of stability depend on the size of the metal ion used in the complexes and the total charge thereon. If the size of these metal ions is small and the total charge is high, then their complexes will be more stable. That is, their ratio will depend on the charge/radius. This can be demonstrated through the following reaction:
Fe3++6CN−⇆FeCN63−logβ=31More StableE19
Fe2++6CN−⇆FeCN64−logβ=8.3Less StableE20
An ionic charge is the electric charge of an ion which is formed by the gain (negative charge) or loss (positive charge) of one or more electrons from an atom or group of atoms. If we talk about the stability of the coordination compounds, we find that the total charge of their central metal ions affects their stability, so when we change their charge, their stability in a range of constant can be determined by propagating of error [18]. If the charge of the central metal ion is high and the size is small, the stability of the compound is high:
Li+>Na+>K+>Rb+>Cs+E21
Th4+>Y3+>Ca2+>Na+andLa3+>Sr2+>K+E22
In general, the most stable coordination bonds can cause smaller and highly charged rations to form more stable coordination compounds.
4.4 Electronegativity
When an electron pair attracts a central ion toward itself, a strong stability complex is formed, and this is due to electron donation from ligand → metal ion. This donation process is increasing the bond stability of metal complexes exerted the polarizing effect on certain metal ions. Li+, Na+, Mg2+, Ca2+, Al3+, etc. are such type of metal cation which is not able to attract so strongly from a highly electronegative containing stable complexes, and these atoms are O, N, F, Au, Hg, Ag, Pd, Pt, and Pb. Such type of ligands that contains P, S, As, Br and I atom are formed stable complex because these accepts electron from M → π-bonding. Hg2+, Pb2+, Cd2+, and Bi3+ metal ions are also electronegative ions which form insoluble salts of metal sulfide which are insoluble in aqueous medium.
4.5 Temperature and pressure
Volatile ligands may be lost at higher temperature. This is exemplified by the loss of water by hydrates and ammonia:
CoNH36Cl3∆175−180°C→CoNH35ClCl2+NH3E23
The transformation of certain coordination compounds from one to another is shown as follows:
AgHgAgI4red45°C⇆Ag2HgI4yellowE24
4.6 Ligand nature
A ligand is an ion or small molecule that binds to a metal atom (in chemistry) or to a biomolecule (in biochemistry) to form a complex, such as the iron-cyanide coordination complex Prussian blue or the iron-containing blood-protein hemoglobin. The ligands are arranged in spectrochemical series which are based on the order of their field strength. It is not possible to form the entire series by studying complexes with a single metal ion; the series has been developed by overlapping different sequences obtained from spectroscopic studies [19]. The order of common ligands according to their increasing ligand field strength is
The above spectrochemical series help us to for determination of strength of ligands. The left last ligand is as weaker ligand. These weaker ligand cannot forcible binding the 3d electron and resultant outer octahedral complexes formed. It is as-Mn2+<Ni2+<Co2+<Fe2+<V2+<Fe3+<Cr3+<V3+<Co3+. For the given ligand, it is not possible to say about the exerted strong or weaker field on the central metal ion. The values of Δ are observed as:
Increasing the oxidation number the value of Δ increased.
Δ increases from top to bottom.
However, when we consider the metal ion, the following two useful trends are observed:
Δ increases with increasing oxidation number.
Δ increases down a group. For the determination of stability constant, the nature of the ligand plays an important role.
The following factors described the nature of ligands.
4.7 Size and charge
The size and charge are two factors that affect the production of metal complexes. The less charges and small sizes of ligands are more favorable for less stable bond formation with metal and ligand. But if this condition just opposite the product of metal and ligand will be a more stable compound. So, less nuclear charge and more size= less stable complex whereas if more nuclear charge and small in size= less stable complex. We take fluoride as an example because due to their smaller size than other halide and their highest electro negativity than the other halides formed more stable complexes. So, fluoride ion complexes are more stable than the other halides:
FeF2+logβ=6.0E26
FeCl2+logβ=1.3E27
As compared to S2− ion, O22− ions formed more stable complexes.
4.8 Basic character
It is suggested by Calvin and Wilson that the metal complexes will be more stable if the basic character or strength of ligands is higher. It means that the donating power of ligands to central metal ions is high [20].
It means that the donating power of ligands to central metal ions is high. In the case of complex formation of aliphatic diamines and aromatic diamines, the stable complex is formed by aliphatic diamines, while an unstable coordination complex is formed with aromatic diamines. So, from the above discussion, we find that the stability will be grater if the e-donation power is greater.
Thus it is clear that greater basic power of electron-donating species will form always a stable complex. NH3, CN−, and F− behaved as ligands and formed stable complexes; on the other hand, these are more basic in nature.
4.9 Ligand concentration
We know that if the concentration of coordination group is higher, these coordination compounds will exist in the water as solution. It is noted that greater coordinating tendency show the water molecules than the coordinating group which is originally present. SCN− (thiocynate) ions are present in higher concentration; with the Co2+ metal ion, it formed a blue-colored complex which is stable in state, but on dilution of water medium, a pink color is generated in place of blue, or blue color complex is destroyed by [Co(H2O)6]2+, and now if we added further SCN−, the pink color will not appear:
CoSCN42−+H2O⇆CoH2O62++4SCN−BluePinkE28
Now it is clear that H2O and SCN− are in competition for the formation of Co(II) metal-containing complex compound. In the case of tetra-amine cupric sulfate metal complex, ammonia acts as a donor atom or ligand. If the concentration of NH3 is lower in the reaction, copper hydroxide is formed but at higher concentration formed tetra-amine cupric sulfate as in the following reaction:
CuSO4+NH4OH→CuOH2Small quantity of ligandE29
CuSO4+NH4OH→CuOH2CuNH42SO4.H2OHigh concentration of ligandE30
4.10 Chelating effect
For a metal ion, chelating ligand is enhanced and affinity it and this is known as chelate effect and compared it with non-chelating and monodentate ligand or the multidentate ligand is acts as chelating agent. Ethylenediamine is a simple chelating agent (Figure 1).
Figure 1.
Structure of ethylenediamine.
Due to the bidentate nature of ethylenediamine, it forms two bonds with metal ion or central atom. Water forms a complex with Ni(II) metal ion, but due to its monodentate nature, it is not a chelating ligand (Figures 2 and 3).
Figure 2.
Structure of chelating configuration of ethylenediamine ligand.
Figure 3.
Structure of chelate with three ethylenediamine ligands.
The dentate cheater of ligand provides bonding strength to the metal ion or central atom, and as the number of dentate increased, the tightness also increased. This phenomenon is known as chelating effect, whereas the formation of metal complexes with these chelating ligands is called chelation:
Metal+2Ligand↔MetalLigand2K=ML2ML2E31
Metal+Ligand–Ligand↔MetalLigand−LigandE32
or
E33
Some factors are of much importance for chelation as follows.
4.11 Ring size
The sizes of the chelating ring are increased as well as the stability of metal complex decreased. According to Schwarzenbach, connecting bridges form the chelating rings. The elongated ring predominates when long bridges connect to the ligand to form a long ring. It is usually observed that an increased a chelate ring size leads to a decrease in complex stability.
He interpreted this statement. The entropy of complex will be change if the size of chelating ring is increased, i.e., second donor atom is allowed by the chelating ring. As the size of chelating ring increased, the stability should be increased with entropy effect. Four-membered ring compounds are unstable, whereas five-membered are more stable. So the chelating ring increased its size and the stability of the formed metal complexes.
4.12 Number of rings
The number of chelating rings also decides the stability of complexes. Non-chelating metal compounds are less stable than chelating compounds. These numbers increase the thermodynamic volume, and this is also known as an entropy term. In recent years ligands capable of occupying as many as six coordination positions on a single metal ion have been described. The studies on the formation constants of coordination compounds with these ligands have been reported. The numbers of ligand or chelating agents are affecting the stability of metal complexes so as these numbers go up and down, the stability will also vary with it.
For the Ni(II) complexes with ethylenediamine as chelating agent, its log K1 value is 7.9 and if chelating agents are trine and penten, then the log K1 values are 7.9 and 19.3, respectively. If the metal ion change Zn is used in place of Ni (II), then the values of log K1 for ethylenediamine, trine, and penten are 6.0, 12.1, and 16.2, respectively. The log βMY values of metal ions are given in Table 1.
Metal ion
log βMY (25°C, I = 0.1 M)
Ca2+
11.2
Cu2+
19.8
Fe3+
24.9
Table 1.
Metal ion vs. log βMY values.
Ni(NH3)62+ is an octahedral metal complex, and at 25 °C its log β6 value is 8.3, but Ni(ethylenediamine)32+ complex is also octahedral in geometry, with 18.4 as the value of log β6. The calculated stability value of Ni(ethylenediamine)32+ 1010 times is more stable because three rings are formed as chelating rings by ethylenediamine as compared to no such ring is formed. Ethylenediaminetetraacetate (EDTA) is a hexadentate ligand that usually formed stable metal complexes due to its chelating power.
4.13 Steric effect
A special effect in molecules is when the atoms occupy space. This is called steric effect. Energy is needed to bring these atoms closer to each other. These electrons run away from near atoms. There can be many ways of generating it. We know the repulsion between valence electrons as the steric effect which increases the energy of the current system [21]. Favorable or unfavorable any response is created.
For example, if the static effect is greater than that of a product in a metal complex formation process, then the static increase would favor this reaction. But if the case is opposite, the skepticism will be toward retardation.
This effect will mainly depend on the conformational states, and the minimum steric interaction theory can also be considered. The effect of secondary steric is seen on receptor binding produced by an alternative such as:
Reduced access to a critical group.
Stick barrier.
Electronic resonance substitution bond by repulsion.
Population of a conformer changes due to active shielding effect.
4.14 Macrocyclic effect
The macrocyclic effect is exactly like the image of the chelate effect. It means the principle of both is the same. But the macrocyclic effect suggests cyclic deformation of the ligand. Macrocyclic ligands are more tainted than chelating agents. Rather, their compounds are more stable due to their cyclically constrained constriction. It requires some entropy in the body to react with the metal ion. For example, for a tetradentate cyclic ligand, we can use heme-B which forms a metal complex using Fe+2 ions in biological systems (Figure 4).
Figure 4.
Structure of hemoglobin is the biological complex compound which contains Fe(II) metal ion.
The n-dentate chelating agents play an important role for the formation of more stable metal complexes as compared to n-unidentate ligands. But the n-dentate macrocyclic ligand gives more stable environment in the metal complexes as compared to open-chain ligands. This change is very favorable for entropy (ΔS) and enthalpy (ΔH) change.
5. Determination of stability constants of complexes in solution
There are so many parameters to determination of formation constants or stability constant in solution for all types of chelating agents. These numerous parameters or techniques are refractive index, conductance, temperature, distribution coefficients, refractive index, nuclear magnetic resonance volume changes, and optical activity.
5.1 Methods based on study of heterogeneous equilibrium
5.1.1 Solubility methods
Solubility products are helpful and used for the insoluble salt that metal ions formed and complexes which are also formed by metal ions and are more soluble. The formation constant is observed in presence of donor atoms by measuring increased solubility.
5.1.2 Distribution method
To determine the solubility constant, it involves the distribution of the ligands or any complex species; metal ions are present in two immiscible solvents like water and carbon tetrachloride, benzene, etc.
5.1.3 Ion exchange method
In this method metal ions or ligands are present in solution and on exchanger. A solid polymers containing with positive and negative ions are ion exchange resins. These are insoluble in nature. This technique is helpful to determine the metal ions in resin phase, liquid phase, or even in radioactive metal. This method is also helpful to determine the polarizing effect of metal ions on the stability of ligands like Cu(II) and Zn(II) with amino acid complex formation.
5.1.4 Electrometric techniques
At the equilibrium free metal and ions are present in the solution, and using the different electrometric techniques as described determines its stability constant.
5.1.5 Potentiometric methods
This method is based upon the titration method or follows its principle. A stranded acid-base solution used as titrate and which is titrated, it may be strong base or strong acid follows as potentiometrically. The concentration of solution using 103− M does not decomposed during the reaction process, and this method is useful for protonated and nonprotonated ligands.
5.1.6 Polarographic method
This is the graphic method used to determine the stability constant in producing metal complex formation by plotting a polarograph between the absences of substances and the presence of substances. During the complex formation, the presence of metal ions produced a shift in the half-wave potential in the solution.
5.2 Other methods
5.2.1 Rate method
If a complex is relatively slow to form and also decomposes at measurable rate, it is possible, in favorable situations, to determine the equilibrium constant.
5.2.2 Freezing technique
This involves the study of the equilibrium constant of slow complex formation reactions. The use of tracer technique is extremely useful for determining the concentrations of dissociation products of the coordination compound.
5.2.3 Biological method
This method is based on the study of the effect of an equilibrium concentration of some ions on the function at a definite organ of a living organism. The equilibrium concentration of the ion studied may be determined by the action of this organ in systems with complex formation.
5.2.4 Spectrophotometric method
The solution of 25 ml is adopted by preparing at the 1.0 × 10−5 M ligand or 1.0 × 10−5 M concentration and 1.0 × 10−5 M for the metal ion:
The solutions containing the metal ions were considered both at a pH sufficiently high to give almost complete complexation and at a pH value selected in order to obtain an equilibrium system of ligand and complexes.
In order to avoid modification of the spectral behavior of the ligand due to pH variations, it has been verified that the range of pH considered in all cases does not affect absorbance values. Use the collected pH values adopted for the determinations as well as selected wavelengths. The ionic strengths calculated from the composition of solutions allowed activity coefficient corrections. Absorbance values were determined at wavelengths in the range 430–700 nm, every 2 nm.
5.2.5 Bjerrum’s method
For a successive metal complex formation, use this method. If ligand is protonate and the produced complex has maximum number of donate atoms of ligands, a selective light is absorbed by this complex, while for determination of stability constant, it is just known about the composition of formed species.
Bjerrum (1941) used the method stepwise addition of the ligands to coordination sphere for the formation of complex. So, complex metal–ligand-n forms as the following steps [22]. The equilibrium constants, K1, K2, K3, … Kn are called stepwise stability constants. The formation of the complex metal-ligandn may also be expressed by the following steps and equilibrium constants.
Where:
M = central metal cation
L = monodentate ligand
N = maximum coordination number for the metal ion M for the ligand
If a complex ion is slow to reach equilibrium, it is often possible to apply the method of isotopic dilution to determine the equilibrium concentration of one or more of the species. Most often radioactive isotopes are used.
5.2.7 Conductance measurement method
This method was extensively used by Werner and others to study metal complexes. In the case of a series of complexes of Co(III) and Pt(IV), Werner assigned the correct formulae on the basis of their molar conductance values measured in freshly prepared dilute solutions. In some cases, the conductance of the solution increased with time due to a chemical change, e.g.,
CoNH34Br2++2H2OCoNH34H2O23++2Br−E38
6. Conclusion
It is concluded that the information presented is very important to determine the stability constant of the ligand metal complexes. Some methods like spectrophotometric method, Bjerrum’s method, distribution method, ion exchange method, electrometric techniques, and potentiometric method have a huge contribution in quantitative analysis by easily finding the stability constants of metal complexes in aqueous solutions.
Acknowledgments
All the authors thank the Library of University of Delhi for reference books, journals, etc. which helped us a lot in reviewing the chapter.
\n',keywords:"thermodynamic stability, kinetic stability, chelate effect, distribution method, ion exchange method, Bjerrum’s method",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/70192.pdf",chapterXML:"https://mts.intechopen.com/source/xml/70192.xml",downloadPdfUrl:"/chapter/pdf-download/70192",previewPdfUrl:"/chapter/pdf-preview/70192",totalDownloads:1325,totalViews:0,totalCrossrefCites:1,dateSubmitted:"April 10th 2019",dateReviewed:"October 16th 2019",datePrePublished:"November 25th 2019",datePublished:null,dateFinished:null,readingETA:"0",abstract:"In the formation of metal complexes in an aqueous medium, equilibrium constant or stability constant is used to determine the strength of interaction between reagents that make the final product after the formation of bonds. In general stability means that a complex may be stored for a long time under suitable conditions or this compound may be existing under suitable conditions. Regarding how much is the concentration of complexes in solution, stability constant provides this information via calculations. These calculations are very much important in many areas of science like chemistry, biology, and medicine. During the complex formation in aqueous medium, two types of stabilities are considered: one is the thermodynamic stability, and the other is kinetic stability. Stability of metal complexes may be affected by various factors like nature of central metal ion and ligand, chelating effect, etc., and some parameters like distribution coefficients, conductance, refractive index, etc. are useful for the determination of stability constants. Various modern techniques are used to determine the stability constant of simple as well as mixed ligand compounds.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/70192",risUrl:"/chapter/ris/70192",signatures:"Jagvir Singh, Abhay Nanda Srivastav, Netrapal Singh and Anuradha Singh",book:{id:"9190",title:"Stability and Applications of Coordination Compounds",subtitle:null,fullTitle:"Stability and Applications of Coordination Compounds",slug:"stability-and-applications-of-coordination-compounds",publishedDate:"July 8th 2020",bookSignature:"Abhay Nanda Srivastva",coverURL:"https://cdn.intechopen.com/books/images_new/9190.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"293623",title:"Dr.",name:"Abhay Nanda",middleName:"Nanda",surname:"Srivastva",slug:"abhay-nanda-srivastva",fullName:"Abhay Nanda Srivastva"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Stability constant of metal complexes",level:"1"},{id:"sec_3",title:"3. Relationship or interaction between βn and K1, K2, K3, … Kn",level:"1"},{id:"sec_3_2",title:"3.1 Thermodynamic stability",level:"2"},{id:"sec_4_2",title:"3.2 Kinetic stability",level:"2"},{id:"sec_6",title:"4. Factors affecting the stability of complexes",level:"1"},{id:"sec_6_2",title:"4.1 Nature of central metal ion",level:"2"},{id:"sec_7_2",title:"4.2 Ionic size",level:"2"},{id:"sec_8_2",title:"4.3 Ionic charge",level:"2"},{id:"sec_9_2",title:"4.4 Electronegativity",level:"2"},{id:"sec_10_2",title:"4.5 Temperature and pressure",level:"2"},{id:"sec_11_2",title:"4.6 Ligand nature",level:"2"},{id:"sec_12_2",title:"4.7 Size and charge",level:"2"},{id:"sec_13_2",title:"4.8 Basic character",level:"2"},{id:"sec_14_2",title:"4.9 Ligand concentration",level:"2"},{id:"sec_15_2",title:"4.10 Chelating effect",level:"2"},{id:"sec_16_2",title:"4.11 Ring size",level:"2"},{id:"sec_17_2",title:"4.12 Number of rings",level:"2"},{id:"sec_18_2",title:"4.13 Steric effect",level:"2"},{id:"sec_19_2",title:"4.14 Macrocyclic effect",level:"2"},{id:"sec_21",title:"5. Determination of stability constants of complexes in solution",level:"1"},{id:"sec_21_2",title:"5.1 Methods based on study of heterogeneous equilibrium",level:"2"},{id:"sec_21_3",title:"5.1.1 Solubility methods",level:"3"},{id:"sec_22_3",title:"5.1.2 Distribution method",level:"3"},{id:"sec_23_3",title:"5.1.3 Ion exchange method",level:"3"},{id:"sec_24_3",title:"5.1.4 Electrometric techniques",level:"3"},{id:"sec_25_3",title:"5.1.5 Potentiometric methods",level:"3"},{id:"sec_26_3",title:"5.1.6 Polarographic method",level:"3"},{id:"sec_28_2",title:"5.2 Other methods",level:"2"},{id:"sec_28_3",title:"5.2.1 Rate method",level:"3"},{id:"sec_29_3",title:"5.2.2 Freezing technique",level:"3"},{id:"sec_30_3",title:"5.2.3 Biological method",level:"3"},{id:"sec_31_3",title:"5.2.4 Spectrophotometric method",level:"3"},{id:"sec_32_3",title:"5.2.5 Bjerrum’s method",level:"3"},{id:"sec_33_3",title:"5.2.6 Isotopic dilution method",level:"3"},{id:"sec_34_3",title:"5.2.7 Conductance measurement method",level:"3"},{id:"sec_37",title:"6. Conclusion",level:"1"},{id:"sec_38",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Rossotti HS. Limitations of the ligand solubility method for studying complex formation. Journal of Inorganic and Nuclear Chemistry. Apr 1960;13(1-2):18-21'},{id:"B2",body:'Jacqueline GG, Monica NL, Varinia LR, Juan ARV, Jose JN, Segoviano G. Spectrophotometric determination of the formation constants of Calcium(II) complexes with 1,2-ethylenediamine, 1,3-propanediamine and 1,4-butanediamine in acetonitrile. Journal of Green Energy & Environment (KeAi). 2017;1:51-57'},{id:"B3",body:'Syed AT, Feroza HW, Muhammad HSW, Saadia S, Allah NM, Allah BG. Spectrophotometric study of stability constants of cimetidine–Ni(II) complex at different temperatures. Arabian Journal of Chemistry. 2012;2:309-314'},{id:"B4",body:'Thanavelan R, Ramalingam G, Manikandan G, Thanikachalam V. Stability constants of mixed ligand complexes of lead(II) with 1-(aminomethyl) cyclohexane acetic acid and α-amino acids. Journal of Saudi Chemical Society. 2014;18(3):227-233'},{id:"B5",body:'Abdelatty MR. Potentiometric studies on ternary complexes involving some divalent transition metal ions, gallic acid and biologically abundant aliphatic dicarboxylic acids in aqueous solutions, Beni-Suef University. Journal of Basic and Applied Sciences. 2015;4(2):174-182'},{id:"B6",body:'Katharina FP, Maria CB, Riccardo B, Bernard AG. Influence of pH on the speciation of copper(II) in reactions with the green tea polyphenols, epigallocatechin gallate and gallic acid. Journal of Inorganic Chemistry. 2012;112:10-16'},{id:"B7",body:'Vishenkova DA, Korotkova EI, Sokolova VA, Ratochvil BK. Electrochemical determination of some triphenylmethane dyes by means of voltammetry. Procedia Chemistry. 2015;15:109-114'},{id:"B8",body:'Lorenzo T, Zsolt B, Luca G, Attila F, Adrienn VMB. Thermodynamic stability, kinetic inertness and relaxometric properties of monoamide derivatives of lanthanide(III) DOTA complexes. Dalton Transactions. 2015;44:5467-5478'},{id:"B9",body:'Nagypal I. Chemistry of complex equilibria. Horwood. 1990;85312:143-145'},{id:"B10",body:'Dyrssen D, Ingri N, Sillen LG. Pit-mapping—A general approach to computer refinement of stability constants. Acta Chemica Scandinavica. 1961;15:694-696'},{id:"B11",body:'Ingri N, Sillen LG. High-speed computers as a supplement to graphical methods. Arkivor Kemi. 1964;23:97-121'},{id:"B12",body:'Sayce IG. Computer calculations of equilibrium constants of species present in mixtures of metal ions and complexing reagents. Talanta. 1968;15(12):1397-1421'},{id:"B13",body:'Sabatini A, Vacca A, Gans P. MINIQUAD—A general computer program for the computation of stability constants. Talanta. 1974;21(1):53-77'},{id:"B14",body:'Pearson RG. Chemical Hardness: Applications from Molecules to Solids. Manhattan, New York City: Springer-VCH; 2005. p. 210. ISBN: 978-3-527-60617-7'},{id:"B15",body:'Drago RS, Wong N, Bilgrien C, Vogel C. E and C parameters from Hammett substituent constants and use of E and C to understand cobalt-carbon bond energies. Inorganic Chemistry. 1987;26(1):9-14'},{id:"B16",body:'Vacca A, Nativi C, Cacciarini M, Pergoli R, Roelens S. A new tripodal receptor for molecular recognition of monosaccharides. A paradigm for assessing glycoside binding affinities and selectivity by 1H NMR spectroscopy. Journal of the American Chemical Society. 2004;126(50):16456-16465'},{id:"B17",body:'Marcotte N, Taglietti A. Transition-metal-based chemo sensing ensembles: ATP sensing in physiological conditions. Supramolecular Chemistry. 2003;15(7):617-717'},{id:"B18",body:'Boiocchi M, Bonizzoni M, Fabbrizzi L, Piovani G, Taglietti A. A di-metallic cage with a long ellipsoidal cavity for the fluorescent detection of dicarboxylate anions in water. Angewandte Chemie, International Edition. 2004;43(29):3847-3852'},{id:"B19",body:'Gampp M, Maeder M, Mayer CJ, Zuberbuhler AD. Calculation of equilibrium constants from multiwavelength spectroscopic data-I: Mathematical considerations. Talanta. 1985;32'},{id:"B20",body:'Frassineti C, Alderighi L, Gans P, Sabatini A, Vacca A, Ghelli S. Determination of protonation constants of some fluorinated polyamines by means of 13C NMR data processed by the new computer program Hyp-NMR 2000. Protonation sequence in polyamines. Analytical and Bioanalytical Chemistry. 2003;376(7):1041-1052'},{id:"B21",body:'Jiaxin Z, Guoyu T, Peng S. Understanding thermodynamic and kinetic contributions in expanding the stability window of aqueous electrolytes. 2018;4(12):2872-2882'},{id:"B22",body:'Gans P, Sabatini A, Vacca A. Investigation of equilibria in solution. Determination of equilibrium constants with the HYPERQUAD suite of programs. Talanta. 1996;43(10):1739-1753'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Jagvir Singh",address:"singhjagvir0143@gmail.com",affiliation:'
Department of Chemistry, ARSD College, University of Delhi, India
Department of Zoology, Raghuveer Singh Govt Degree College, India
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The socioeconomic impact of this research project could be important, considering that in Argentina similar studies using natural compounds derived from medicinal mushrooms for cancer therapy have not yet been performed. 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Open Access publishing helps remove barriers and allows everyone to access valuable information, but article and book processing charges also exclude talented authors and editors who can’t afford to pay. The goal of our Women in Science program is to charge zero APCs, so none of our authors or editors have to pay for publication.
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