**5. Worker acts**

Employees' act is another important aspect to be considered at workplaces, to reduce or eliminate accidents that might result in illnesses, diseases, injuries, and death at workplaces. Certainly, no environment is free from all hazards, but workers' behavior toward handling the hazards is a crucial aspect of preventing accidents from occurring at workplaces. Undoubtedly, safety and health specialists need to have general knowledge of the impacts of any hazardous materials to the human body and train all employees on ways of preventing such effects, but because of human nature, how many adhere to the totality of this principle? Employees' safety responsibilities were acknowledged in 1999 by Bratton and Gold [20] that employee is to behave in a manner that safeguards his or her own health and safety and that of his/her co-workers. Furthermore, Jonathan and Mbogo documented the findings from the research carried out at The Research Centre Design and Technology of the Saxion University of Applied sciences on "Safety at work," which acknowledged that personal safety, a safe environment, and safe behavior were important components that employers need to ensure their availability within their organizations for proper productivity and safety of the employees. Liao et al. [21] acknowledged in their study that almost all accidents and injuries occurring at workplace are attributed to workers' unsafe acts, which are also a reflection of system deficiency and unsafe work condition. Dodoo and Al-Samarraie [22] concluded in their study that lack of adequate knowledge on safety and health, violation of safety rules, work pressure, stress, and non-use of protective equipment were the unsafe acts perpetrated by the workers sampled. Researcher has also referred unsafe act as a deliberate omission or deviation from the recommended safety standards Mason [23]. The same researcher described an unsafe act as an individual's likelihood of

**5**

**Author details**

of the readers.

Bankole K. Fasanya

University Northwest, USA

\*Address all correspondence to<EMAIL_ADDRESS>
provided the original work is properly cited.

Environmental Health and Safety Concentration, College of Technology, Purdue

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

*Introductory Chapter: Safety and Health for Workers - Theory and Applications*

not following standard safety rules, procedures, instructions, and specified criteria for work imposed by the organization management. Since the contributions of worker's unsafe acts to the workplace accidents is undeniable worldwide, research aimed at reducing this factor should be considered a discipline of great interest for

The physical and chemical properties of raw material use and produce are other essential factors that need attention to properly monitor the health and safety of workers at workplaces. To do this, detailed attention must be given to workplace Material Safety Data Sheet (MSDS). The importance of MSDS in handling worker exposure to hazardous materials in every workplace cannot be substituted for another record keeping data sheet for the purpose of workers' health and safety. According to Translink online website, MSDS is a document that contains information on the potential health effects of exposure to chemicals, or other potentially dangerous substances, and on safe working procedure when handling chemical products. It is an essential starting point for the development of a complete health and safety program. MSDS contains hazard evaluations on the use, storage, handling, disposal information, and emergency procedures related to that material. Therefore, it is imperative to consider the use of MSDS in every workplace to ensure

This book highlights significant factors contributing to workers' poor health and safety at work, which could result in injury, chronic diseases, and death. Both theories and practical applications are discussed in this book for easy understanding

*DOI: http://dx.doi.org/10.5772/intechopen.92196*

**6. Properties of materials used or produced**

better control of hazardous materials effect on the employees.

society in general.

not following standard safety rules, procedures, instructions, and specified criteria for work imposed by the organization management. Since the contributions of worker's unsafe acts to the workplace accidents is undeniable worldwide, research aimed at reducing this factor should be considered a discipline of great interest for society in general.


2.3.2. Poly(ether urethane) Synthesis

PEU synthesis was carried out according to Bo ffito et al. [21]. P407, HDI and CDM were reacted at 1:2:1 molar ratio and the synthesis was conducted under N2 atmosphere following a prepolymerization method with a first reaction between P407 and HDI to form an N=C=O-terminated prepolymer which was then chain extended through the addition of CDM. In detail, the required amount of HDI was added to a 20% *w*/*v* concentrated P407 solution previously prepared in DCE and equilibrated at 80 ◦C. DBTDL was also added at 0.1% *w*/*w* concentration with respect to P407 and the reagents reacted for 150 min. Then, the reaction mixture was cooled down at 60 ◦C and a 3% *w*/*v* concentrated CDM solution previously prepared in DCE was added to start the chain extension reaction which lasted 90 min and was finally terminated through the addition of MeOH. The synthesized PEU was collected by precipitation in an excess of petroleum ether (petroleum ether: DCE = 4:1 *v*/*v*) and further purified by solubilization in DCE (20% *w*/*v*) and precipitation in a diethyl ether/MeOH mixture (98:2 *v*/*v*, 5:1 volume ratio with respect to DCE). PEU was collected by centrifugation (MIKRO 220R, Hettich, Tuttlingen, Germany), dried overnight, grinded and stored under vacuum at 4 ◦C until use. Hereafter, the synthesized PEU will be referred to with the acronym CHP407, where C, H and P407 refer to CDM, HDI and Poloxamer®407, respectively.

## *2.4. Chemical Characterization of the As-Synthesized PEU*

The successful synthesis of CHP407 poly(ether urethane) was assessed by attenuated total reflectance Fourier transformed infrared (ATR-FTIR) spectroscopy and size exclusion chromatography (SEC). CHP407 and P407 ATR-FTIR spectra were registered using a Perkin Elmer (Waltham, MA, USA) Spectrum 100 instrument equipped with an ATR diamond crystal (UATR KRS5). Spectra were obtained as an average of 16 scans registered at RT within the spectral range 4000–600 cm−1 (resolution 4 cm−1). CHP407 molecular weight distribution was assessed by SEC using an Agilent Technologies 1200 Series (Agilent Technologies, Inc., Santa Clara, CA, USA) instrument equipped with a refractive index detector (RID) and two columns (Waters Styragel HR1 and HR4, Waters Corporation, Sesto San Giovanni, Italy). Analyses were conducted at 55 ◦C using N,N-dimethylformammide (DMF, CHROMASOLV Plus, inhibitor-free, for HPLC, Carlo Erba Reagents, Cornaredo, Italy), added with 0.1% *w*/*v* LiBr (Sigma Aldrich, Milano, Italy) as eluent at a flow rate of 0.5 mL/min. Samples were prepared at 2 mg/mL concentration in the mobile phase and filtered through a 0.45 μm syringe filter (Macherey-Nagel, Düren, Germany poly(tetrafluoro ethylene) membrane) before analysis. The Agilent ChemStation software was finally used to estimate CHP407 Number Average Molecular Weight (Mn) and polydispersity index (D) based on a calibration curve previously constructed starting from 10 poly(methyl methacrylate) standards with Mn ranging between 940 and 214,600 g/mol.

#### *2.5. Preparation of CHP407-Based Hydrogels Carrying Cu-MBGs Loaded with Ibu*

CHP407 thermosensitive hydrogels were prepared at a final PEU concentration of 15% *w*/*v* [16,21]. Cu-MBGs loaded with Ibu were encapsulated into CHP407-based hydrogels with final particle concentration of 20 mg/mL. In detail, hybrid hydrogels were prepared by adding an aliquot of particle suspension in ddH2O (100 mg/mL), previously sonicated for 3 min at 20% amplitude, to a CHP407-based solution prepared in physiological saline solution (0.9% NaCl). The starting concentration of CHP407 solution was determined in order to reach the desired PEU and particle contents in the final system. Both CHP407 solubilization and particle addition were carried out at 4 ◦C to avoid undesired gelation during sample preparation. Sample mixing was conducted using a Vortex mixer for 30 s to ensure homogeneous particle dispersion within the sol-gel systems. Particles were added to CHP407 aqueous solutions immediately before use to avoid premature payload release before characterization tests. Pure CHP407 sol-gel systems and CHP407 hydrogels loaded with Ibu were also prepared as control samples. Loading of Ibu in CHP407 sol-gel systems was carried out by adding CHP407 aqueous solutions prepared at higher concentration with a predefined volume of an Ibu stock solution (at 40 mg/mL in ethanol) to reach an average content equal to the Ibu amount incorporated into MBG\_Cu2%\_SG\_Ibu and MBG\_Cu2%\_SD\_Ibu, as assessed through TGA analysis. Based on this calculation, CHP407 sol-gel systems were loaded with Ibu at a final concentration of 2.5 mg/mL. Hereafter, the developed sol-gel systems will be referred to with the acronyms reported in Table 1. For all the conducted tests, hydrogels

were prepared in Bijou sample containers (inner diameter 17 mm, Carlo Erba Reagents, Cornaredo, Italy) at a final volume of 1 mL, to avoid geometry and volume influence on the performed characterizations.

**Table 1.** Compositional information and acronyms of the designed sol-gel systems.

## *2.6. Characterization of Hybrid Sol-Gel Systems*

The thermosensitive behavior of pure CHP407 (i.e., without particle) and hybrid hydrogels (i.e., containing MBG\_Cu2%\_SG\_Ibu, MBG\_Cu2%\_SD\_Ibu or Ibu) was studied through tube inverting test in temperature ramp mode and isothermal conditions (i.e., at 37 ◦C) to assess the effects of drug/particle incorporation on hydrogel sol-to-gel transition. In view of their potential clinical application as injectable depots for the prolonged and sustained payload release, the delivery profile of ibuprofen and copper ion was investigated in physiological mimicking conditions. Furthermore, gel swelling potential and residence time in watery environment at physiological temperature were also assessed.

## 2.6.1. Thermosensitive Behavior of CHP407-Based Sol-Gel Systems

Tube inverting test in temperature ramp conditions was carried out to estimate hydrogel Lower Critical Gelation Temperature (LCGT). In detail, samples were subjected to a step-by-step temperature increase within the range 5–40 ◦C at a rate of 1 ◦C/step. In each step, temperature was kept constant for 5 min and the samples were finally inverted for 30 s to visually inspect their sol, biphasic or gel state (in the gel state no flow was observed within the set inversion time). The same test was carried out in isothermal conditions at 37 ◦C to assess hydrogel gelation time. In this test, instead of temperature, incubation time at 37 ◦C was varied from 1 to 10 min with a rate of 1 min/step. Each step consisted in sample equilibration in the sol state for 10 min, incubation at 37 ◦C for the required time, inversion for 30 s and visual inspection.

## 2.6.2. Gel Swelling and Stability in Aqueous Media

Gel swelling and stability in physiological mimicking conditions, that is, in aqueous media and 37 ◦C, were investigated on the designed sol-gel systems up to 14 days incubation time [16]. Samples (1 mL) were first incubated at 37 ◦C (Memmert IF75, Schwabach, Germany) for 15 min to allow a complete gelation and then added with 1 mL of Trizma® (0.1 M, pH 7.4) previously equilibrated at 37 ◦C. Complete medium refresh was performed every two days. On predefined time points of 6h, 1d, 3d, 7d and 14d three samples were collected, weighted upon removal of the residual buffer, lyophilized (Martin Christ ALPHA 2–4 LSC, Osterode am Harz, Germany) and weighted again. The collected data were finally used to estimate sample percentage of swelling (weight change in wet state %) and polymer weight loss (weight loss in dry state %) [16]. Samples collected on day 14 were also analyzed by SEC to assess changes in polymer molecular weight distribution during incubation in aqueous media at 37 ◦C.


**6. Toxicity of medicinal plants**

Generally, medicinal plants contain bioactive compounds which demonstrate both intra- and inter-species variation in type and content. Plants by virtue of their chemical constituents are potentially toxic; thus, some plants used in traditional medicine are intrinsically toxic. Some plants well known in traditional medicine to be toxic or poisonous include *Atropa belladonna*, *Datura* spp., *Digitalis* spp. [17].

Many plants used in traditional medicine or used as food have demonstrated some toxicity (mutagenic and carcinogenic) effects [18]. The issue of the possible toxic, genotoxic and/or mutagenic effects of plants used in traditional medicine has been highlighted in the review by Fennell et al. [19]. However, some of the toxic plants are useful to man as medicines and also as poisons for hunting and for use as pesticides, for example, Datura (tropane alkaloids), Digitalis (cardiac glycosides) and Pyrethrum (pyrethrin insecticides). Well-known medicinal plants have demonstrated toxicity in laboratory studies and field observations. For example, *Lantana camara* used in the management of malaria and other diseases has been reported to be hepatotoxic in several animal species which could be of concern regarding its chronic use in man [20]. Similarly, *Momordica charantia*, a known anti-diabetic and antimalarial plant but also used in Ghana as an abortifacient [21, 22], has reportedly caused deadly hypoglycemia in children [23].


*2.3.2 Chemoproteomics*

Chemoproteomics is employed as a chemical tool for target identification. It can be used to investigate the signal transductions. This particular field of study has flourished as a key technology to characterize the action mechanism of chemical probes and drugs which can act as pharmacological modulators, hence validating the cellular targets of several therapeutic drug candidates. Chemoproteomics can be further characterized as affinity- and activity-based chemical proteomics [15]. In some cases when probe development is a difficult task, multiple kinase inhibitors are used for targeting the kinome effectively [16].


**2.1 Quantum mechanics in a nutshell**

For the benefit of those readers who are unfamiliar with the standard formalism of quantum mechanics, we review its main facts:

#### **2.2 Naive tight-binding approach to band theory**

As argued in Section 1, the starting point of this approach is to express the electron's state vector as a linear combination of atomic orbitals (LCAO) located at the crystal's lattice sites (we illustrate the procedure in 1D, but clearly, it can be extended to any dimension and lattice symmetry). The eigenvalue problem of the isolated atom centered at *xc* is *H*atom ∣*ψ*atom⟩ ¼ *E*atom ∣*ψ*atom⟩, with *H*atom ¼ *T* þ *V x*ð Þ � *xc* . We then place a copy ∣*i*⟩ of ∣*ψatom*⟩ centered at each lattice site *i* (*xi* ¼ *ia*) and write the electron's state in the crystal as LCAO

$$|\psi\_{\text{crystal}}\rangle = \sum\_{i} c\_{i} |i\rangle \tag{1}$$

*Assuming* the states ∣*i*⟩ to be *orthogonal* to each other, the left-hand side of Eq. (3) reads ∑*i ci*∣*i*i � *γ* ∑*i ci* ð j*i* þ 1i þ *ci*þ1∣*i*⟩). If the number of sites in the crystal is large

Again invoking PBC, one tries the form *cj* ¼ exp *ijka* with �*π* , *ka*≤*π* (Bloch

• We know that *α* equals *Eatom* plus some correction, but we do not know what

• Similarly, we know that *γ* is the expectation value of the effective potential

atoms. We have kept just *j* ¼ *i* � 1, but even in this approximation, we do not

• To what extent can one assume the states ∣*i*⟩ to be *orthogonal* to each other? This assumption is correct in the absence of interatomic interaction, but not

*i* þ *Wi*

periodic boundary conditions (PBC). This allows to rearrange the sums (their indices become dummy), and Eq. (3) reads ∑*i E*crystal � *α* � �*ci* � *γ* ∑*i* ð*ci*�1þ � *ci*þ1Þ�∣*i*i ¼ 0 . Clearly, the LCAO assumes that the ∣*i*⟩ are *linearly independent* (be they orthogonal or not), so we are left with the system of difference equations:

*i*

*Ecrystal* � *α* � �*ci* � *γ* ∑

phase factors) and obtains the known cosine spectrum

What has been left behind? Much indeed:

), one can greatly simplify the problem by assuming

*E*crystal ¼ *α* � 2*γ* cos *ka,* � *π* , *ka* ≤*π:* (5)

� � felt by an electron at *x* � *ia*, due to the presence of other

� � and using Eq. (1), *E*crystal turns out

*ci* j j2 þ ∑ *ij Sijc* ∗ *i cj*

" #

*,* (6)

� � can be singled out from

involve only sites *i* and *j*,

∑ *i*

*αi* ≔ *Hii* ¼ 〈*i*∣*Wi*∣*i*〉*, γij* ≔ *Hij* ¼ *i*j*Wi*j *j*i*, Sij* ≔ h*i*j *j*i*, j* 6¼ *i:* � (7)

The contribution of the *Sij* (known as *overlap integrals*) to the band spectrum is our main concern in this section. But not less interesting are that of the *αi* terms which, as argued, shift the electronic energy in an atom from its isolated value *E*atom, as a collective effect of the other atoms—and that of the *γij*. The latter can be

ð Þ2

*ij* ≔ *i*j*Vj*j *j* � �

*ij* also involve the sum ∑*l*6¼*i,j V x*ð Þ � *xl* of the potentials

ð Þ¼ *ci*�1 þ *ci*þ1 0*, i* ¼ 1…*N* � 0*:* (4)

enough (usually it is �106

*Issues in Solid-State Physics*

*DOI: http://dx.doi.org/10.5772/intechopen.84367*

the correction is.

*Wi* ≔ ∑*j*6¼*i V x* � *xj*

to be [1, 2]

where

**7**

know what the correction is.

necessarily when atoms interact.

Recognizing that *H*crystal ¼ ∑*i H*atom

*E*crystal ¼ *E*atom þ ∑

**2.3 Tight-binding band calculation: properly done**

*i*

regarded as the sum of two contributions, as *Vj* ≔ *V x* � *xj*

ð Þ3

*Wi*. Then whereas the two-center integrals *γ*

the three-center integrals *γ*

*αi ci* j j2 þ ∑

*ij γijc* ∗ *i cj*

" #�

(clearly, *ci* ¼ 〈*i*∣*ψ*crystal〉). Now, even though the interatomic distance in the crystal (the "lattice spacing" *a*) is usually larger than the range *x*0 of the atomic orbitals, the atomic cores *do* interact, and one should include at least two effects:

It thus makes sense to write up the lattice Hamiltonian in terms of projection operators as

$$H\_{\text{crystal}} = \alpha \sum\_{i} |i\rangle\langle i| - \gamma \sum\_{i} (|i+\mathbf{1}\rangle\langle i| + |i\rangle\langle i+\mathbf{1}|). \tag{2}$$

Two timely comments are:

Using Eqs. (1) and (2), the eigenvalue problem *H*crystal ∣*ψ*crystal⟩ ¼ *E*crystal ∣*ψ*crystal⟩ for the electron in the crystal reads

$$
\nabla \left[ a \sum\_{i} |i\rangle \langle i| - \chi \sum\_{i} (|i+1\rangle \langle i| + |i\rangle \langle i+1|) \right] \sum\_{j} c\_{j} |j\rangle = E\_{\text{crystal}} \sum\_{j} c\_{j} |j\rangle. \tag{3}
$$

#### *Issues in Solid-State Physics DOI: http://dx.doi.org/10.5772/intechopen.84367*

• **Wave function**: a possible "basis" set is that of eigenstates (*X*∣*x*⟩ ¼ *x*∣*x*⟩) of the

*Solid State Physics - Metastable, Spintronics Materials and Mechanics of Deformable…*

here the role of the coefficients *αI*. In modern notation, *ψ*ð Þ *x* is written as h i *x*j*φ* ,

• **Orthogonality** (h i *φ*j*ψ* ¼ 0): a given eigenvalue *l*1 of a Hermitian operator *L* may have a single eigenstate ∣ *l*1⟩ (the normalized one out of a dimension-1 subspace over the complex numbers) or more (in this case, it is said to be *degenerate*). Eigenstates corresponding to *different* eigenvalues are *automatically*

As argued in Section 1, the starting point of this approach is to express the electron's state vector as a linear combination of atomic orbitals (LCAO) located at the crystal's lattice sites (we illustrate the procedure in 1D, but clearly, it can be extended to any dimension and lattice symmetry). The eigenvalue problem of the

*H*atom ¼ *T* þ *V x*ð Þ � *xc* . We then place a copy ∣*i*⟩ of ∣*ψatom*⟩ centered at each lattice

∣*ψ*crystal〉 ¼ ∑

(clearly, *ci* ¼ 〈*i*∣*ψ*crystal〉). Now, even though the interatomic distance in the crystal (the "lattice spacing" *a*) is usually larger than the range *x*0 of the atomic orbitals, the atomic cores *do* interact, and one should include at least two effects:

• A *correction* to the isolated atomic level *E*atom (we shall call *α* the corrected

• Electron tunneling between neighboring orbitals (let *γ* be a gauge of the energy

It thus makes sense to write up the lattice Hamiltonian in terms of projection

*i*

2. The minus sign in the second term ensures crystal stability (energy is released

Using Eqs. (1) and (2), the eigenvalue problem *H*crystal ∣*ψ*crystal⟩ ¼ *E*crystal ∣*ψ*crystal⟩

*j*

*cj*∣ *j*i ¼ *E*crystal ∑

*j*

*cj*∣ *j*i*:* (3)

j*i*i〈*i*∣ � *γ* ∑

1. The presence of ∣*i*⟩⟨*i* þ 1∣, the adjoint of ∣*i* þ 1⟩⟨*i*∣, ensures that *H*crystal be

ðj*i* þ 1i〈*i*∣þj*i*i〈*i* þ 1∣Þ� ∑

*i*

*dx ψ*ð Þ *x* ∣*x*⟩. The *wave function ψ*ð Þ *x* plays

*ci*∣*i*i (1)

ðj*i* þ 1ih*i*jþj*i*ih Þ *i* þ 1j *:* (2)

position operator, namely, ∣*ψ*⟩ ¼ Ð

**2.2 Naive tight-binding approach to band theory**

involved in such a "hopping" process)

*H*crystal ¼ *α* ∑

Two timely comments are:

by forming a crystal).

for the electron in the crystal reads

j*i*i〈*i*∣ � *γ* ∑

*i*

½*α* ∑ *i*

**6**

*Hermitian*.

*i*

*dx* ∣*x*⟩h i *x*j*φ* .

isolated atom centered at *xc* is *H*atom ∣*ψ*atom⟩ ¼ *E*atom ∣*ψ*atom⟩, with

site *i* (*xi* ¼ *ia*) and write the electron's state in the crystal as LCAO

so one writes ∣*φ*⟩ ¼ Ð

orthogonal.

level)

operators as

*Assuming* the states ∣*i*⟩ to be *orthogonal* to each other, the left-hand side of Eq. (3) reads ∑*i ci*∣*i*i � *γ* ∑*i ci* ð j*i* þ 1i þ *ci*þ1∣*i*⟩). If the number of sites in the crystal is large enough (usually it is �106 ), one can greatly simplify the problem by assuming periodic boundary conditions (PBC). This allows to rearrange the sums (their indices become dummy), and Eq. (3) reads ∑*i E*crystal � *α* � �*ci* � *γ* ∑*i* ð*ci*�1þ � *ci*þ1Þ�∣*i*i ¼ 0 . Clearly, the LCAO assumes that the ∣*i*⟩ are *linearly independent* (be they orthogonal or not), so we are left with the system of difference equations:

$$(\mathbf{E}\_{\text{crystal}} - \mathbf{a})\mathbf{c}\_i - \chi \sum\_{i} (\mathbf{c}\_{i-1} + \mathbf{c}\_{i+1}) = \mathbf{0}, i = \mathbf{1}...N \equiv \mathbf{0}.\tag{4}$$

Again invoking PBC, one tries the form *cj* ¼ exp *ijka* with �*π* , *ka*≤*π* (Bloch phase factors) and obtains the known cosine spectrum

$$E\_{\text{crystal}} = a - 2\gamma \cos ka, \ -\pi \le ka \le \pi. \tag{5}$$

What has been left behind? Much indeed:

#### **2.3 Tight-binding band calculation: properly done**

Recognizing that *H*crystal ¼ ∑*i H*atom *i* þ *Wi* � � and using Eq. (1), *E*crystal turns out to be [1, 2]

$$E\_{\text{crystal}} = E\_{\text{atom}} + \left[\sum\_{i} a\_i |c\_i|^2 + \sum\_{ij} \gamma\_{ji} c\_i^\* c\_j\right] \Big/ \left[\sum\_i |c\_i|^2 + \sum\_{ij} \mathbf{S}\_{ji} c\_i^\* \, c\_j\right],\tag{6}$$

where

$$a\_i \coloneqq H\_{\vec{n}} = \langle i | W\_i | i \rangle, \gamma\_{\vec{\eta}} \coloneqq H\_{\vec{\eta}} = \langle i | \mathcal{W}\_i | j \rangle, \mathcal{S}\_{\vec{\eta}} \coloneqq \langle i | j \rangle, j \neq i. \tag{7}$$

The contribution of the *Sij* (known as *overlap integrals*) to the band spectrum is our main concern in this section. But not less interesting are that of the *αi* terms which, as argued, shift the electronic energy in an atom from its isolated value *E*atom, as a collective effect of the other atoms—and that of the *γij*. The latter can be regarded as the sum of two contributions, as *Vj* ≔ *V x* � *xj* � � can be singled out from *Wi*. Then whereas the two-center integrals *γ* ð Þ2 *ij* ≔ *i*j*Vj*j *j* � � involve only sites *i* and *j*, the three-center integrals *γ* ð Þ3 *ij* also involve the sum ∑*l*6¼*i,j V x*ð Þ � *xl* of the potentials of the remaining atoms in the solid. Hence, the *γ* ð Þ3 *ij* can be interpreted as the collective effect on the overlap between orbitals *i* and *j*.

Variation of Eq. (6) with respect to the LCAO coefficients of Eq. (1)—namely, *∂E*crystal*=∂a*∗ *j* ¼ 0—yields *Hij* � *E*crystal*Sij aj* ¼ 0*,* ∀*j*. Assuming PBC, *Hij* and *Sij* are functions only of the interatomic distance *na*, with *n* ¼ ∣*i* � *j*∣. Again using *an* ¼ exp *inka* with �*π* , *ka*≤ *π*, Eq. (6) yields

$$E\_{\text{crystal}} = E\_{\text{atom}} + \left[a + 2\sum\_{n} H\_n \cos nka\right] \Big/ \left[\mathbf{1} + 2\sum\_{n} \mathbf{S}\_n \cos nka\right]. \tag{8}$$

**3. Quantum Monte Carlo method for systems with strongly**

The *state vectors* dealt with in Section 1 represent *pure* states. They are the ones

*bles* of quantum states, the object of interest is the Hermitian operator exp ð Þ �*βH* , called the *density matrix* operator (here β ≔ ð Þ *kBT* �1 and *kB* ¼ *R=NA* are Boltzmann's

What drives our interest in the *density matrix*—namely, the matrix elements between pure states of exp ð Þ �*βH* —is the fact that it can be used to find the ground state of many-body systems by stochastic methods. For *β* large enough, exp ð Þ �*βH* acts effectively as a *projector* over the lowest-lying energy eigenstate to which the initial (trial) state ∣*φ*⟩ is not definitely orthogonal. Let *E* be the corresponding eigenvalue, and consider another trial state ∣*χ*⟩ over which we will project the result.

But what is yet more interesting is that in the process, we find a good estimate of

The first step in this computation is to divide the interval 0½ � *; β* into *L* "time"

a.We take our language from the formal analogy between the density matrix

b.Note that in our case, exp ð Þ �*βH* is *not meant to be traced over* as it should be in a thermodynamic calculation: here it must rather be considered as a formal

If we can decompose *H* into a sum of several terms *Hi* which (although not commuting among them) are themselves *sums of commuting terms*, then for *L* large

*U* ¼ exp ½�Δ*τ*ð Þ *H*1 þ 2 � ¼ exp ð Þ �Δ*τ*1 exp ð Þ �Δ*τH*2 exp �ð Þ Δ*τ* 2

. Hence

3 We have already stated that the Fermi level is the *chemical potential* of an ideal free-electron gas. This

the eigenstate itself, namely, its composition in terms of a known basis.

*β*!∞ ½ � ⟨ *χ*j exp ½�ð Þ *β* þ Δ*β H*�j*φ*⟩*=*⟨ *χ*j exp ð Þj �Δ*βH φ*⟩ *:* (11)

3 When one deals with *statistical ensem-*

½ � *H*1*; H*2 n o

which display the spectacular effects seen in recent experiments. Since in this section, we will allow *creation annihilation* of electron states, we must work in the

**correlated fermions**

*Issues in Solid-State Physics*

constant).

**3.1 Quantum** *statistical* **mechanics in a nutshell**

framework of the *grand canonical ensemble*.

*DOI: http://dx.doi.org/10.5772/intechopen.84367*

Then we may numerically compute *E* from

**3.2 Monte Carlo pursuit of the ground state**

tool to make sense in the limit *β* ! ∞.

c. We may call *U* ¼ exp ð Þ �Δ*τH* the *transfer matrix* operator.

½ �þ *H*1*; H*2 … n o � *U*1*U*2

and the evolution operators.

enough, the error of approximating

¼ *U*1*U*2 1 � ð Þ Δ*τ* 2

**9**

would be at most of order ð Þ Δ*τ* 2

concept is peculiar of the *grand canonical ensemble*.

slices of width Δ*τ* ¼ *β=L*. Some comments are in order:

exp ð Þ¼ �Δ*βE* lim

Note however that the number of multicenter integrals to be computed is immense! Because of that, most tight-binding calculations plainly ignore almost all the multicenter integrals (keeping only those involving nearest neighbors) and neglect orbital non-orthogonality. This way, the familiar cosine spectrum is obtained. Often, multicenter integrals are just regarded as parameters to fit the results of more sophisticated calculations made by other methods at the highest symmetry points of the Brillouin zone.

In the following, we compute *all* the multicenter integrals *exactly* in the framework of a simple model for the atomic potential. The results help get an intuition on the effect on band spectrum of neglecting overlap integrals and distant-neighbor interactions.

#### **2.4 A simple model that yields an exact tight-binding band spectrum**

We restrict ourselves to a 1D monoatomic crystal and assume the interatomic distance *a* to be larger than the effective range of the screened Coulomb potential representing the atomic core. In such a situation, we can approximate the latter by a Dirac *δ*-function (complete screening up to the scale of the nucleus):

$$V\_{\text{crystal}}(\mathbf{x}) = -V\_0 \sum\_n \delta(\mathbf{x} - na). \tag{9}$$

The solution to *H*atom ∣*ψ*atom⟩ ¼ *E*atom ∣*ψ*atom⟩, with *H*atom ¼ � ℏ2 2*m d*2 *dx*2 � *V*0*δ*ð Þ *x* , is an exponential function of the form *ψ*atomð Þ¼ *x* 〈*x*j i *ψ*atom ¼ *x* �1 2 0 exp � j j *x x*0 . Its range *x*0 is related to *E*atom by�*E*atom ¼ ℏ2 *=*2*mx*2 0 ¼ *mV*0*=*2ℏ2 .

The only two spatial scales involved in this problem are *x*0 and the lattice spacing *a*. The parameter *t* ¼ *a=x*0 will thus allow us to follow the formation of energy bands (*k*-space picture) as atoms get close together (real-space picture). All the multicenter integrals can be computed analytically in terms of *t*. The results are *Sn* ¼ ð Þ 1 þ *nt* exp ð Þ �*t* , *α* ¼ 2*E*atom exp ð Þ �*t =*sinh *t*, and *γn* ¼ 2*E*atom½ � *n* þ exp ð Þ �*t =*sinh *t* exp ð Þ �*nt* [3]. We thus get the following closed expression for *λ* ≔ *E*crystal � *E*atom *=E*atom:

$$\lambda(k,t) = \left[A\_0(t) + A\_1(t)\cos ka\right] / \left[1 + \mathcal{S}(t)\cos ka\right], \ -\pi \le ka \le \pi,\tag{10}$$

where *A*0 ¼ exp ð Þ �*t* sinh *t=*½ � sinh *t*cosh *t* � *t* , *A*1 ¼ sinh *t=*½ � sinh *t*cosh *t* � *t* , and *S* ¼ ½ � *t*cosh *t* � sinh *t =*½ � sinh *t*cosh *t* � *t* [3].

Explicit evaluation of Eq. (10) at the bottom (*ka* ¼ 0) and top (*ka* ¼ *π*) of the band shows that for *t* , 4, the cosine spectrum of Eq. (5) underestimates both. Moreover, the multicenter integrals neglected in the cosine spectrum shift *unevenly* the top and bottom of the exact spectrum. Hence, the approximation performs worse for the top than for the bottom of the band.

of the remaining atoms in the solid. Hence, the *γ*

*j* ¼ 0—yields *Hij* � *E*crystal*Sij*

*an* ¼ exp *inka* with �*π* , *ka*≤ *π*, Eq. (6) yields

*E*crystal ¼ *E*atom þ *α* þ 2 ∑

symmetry points of the Brillouin zone.

*∂E*crystal*=∂a*∗

interactions.

collective effect on the overlap between orbitals *i* and *j*.

ð Þ3

*aj* ¼ 0*,* ∀*j*. Assuming PBC, *Hij* and *Sij* are

1 þ 2 ∑ *n*

Variation of Eq. (6) with respect to the LCAO coefficients of Eq. (1)—namely,

*Hn* cos *nka*

In the following, we compute *all* the multicenter integrals *exactly* in the framework of a simple model for the atomic potential. The results help get an intuition on the effect on band spectrum of neglecting overlap integrals and distant-neighbor

We restrict ourselves to a 1D monoatomic crystal and assume the interatomic distance *a* to be larger than the effective range of the screened Coulomb potential representing the atomic core. In such a situation, we can approximate the latter by a

*=*2*mx*2

*λ*ð Þ¼ *k; t* ½ � *A*0ðÞþ*t A*1ð Þ*t* cos *ka =*½ � 1 þ *S t*ð Þ cos *ka ,* � *π* , *ka*≤ *π,* (10)

where *A*0 ¼ exp ð Þ �*t* sinh *t=*½ � sinh *t*cosh *t* � *t* , *A*1 ¼ sinh *t=*½ � sinh *t*cosh *t* � *t* , and

Explicit evaluation of Eq. (10) at the bottom (*ka* ¼ 0) and top (*ka* ¼ *π*) of the band shows that for *t* , 4, the cosine spectrum of Eq. (5) underestimates both. Moreover, the multicenter integrals neglected in the cosine spectrum shift *unevenly* the top and bottom of the exact spectrum. Hence, the approximation performs

The only two spatial scales involved in this problem are *x*0 and the lattice spacing

*a*. The parameter *t* ¼ *a=x*0 will thus allow us to follow the formation of energy bands (*k*-space picture) as atoms get close together (real-space picture). All the multicenter integrals can be computed analytically in terms of *t*. The results are

*γn* ¼ 2*E*atom½ � *n* þ exp ð Þ �*t =*sinh *t* exp ð Þ �*nt* [3]. We thus get the following closed

*V*crystalð Þ¼� *x V*0∑*nδ*ð Þ *x* � *na :* (9)

0 ¼ *mV*0*=*2ℏ2 .

2*m d*2

�1 2 0 exp � j j *x*

*dx*2 � *V*0*δ*ð Þ *x* , is

. Its

*x*0

Note however that the number of multicenter integrals to be computed is immense! Because of that, most tight-binding calculations plainly ignore almost all the multicenter integrals (keeping only those involving nearest neighbors) and neglect orbital non-orthogonality. This way, the familiar cosine spectrum is obtained. Often, multicenter integrals are just regarded as parameters to fit the results of more sophisticated calculations made by other methods at the highest

functions only of the interatomic distance *na*, with *n* ¼ ∣*i* � *j*∣. Again using

*Solid State Physics - Metastable, Spintronics Materials and Mechanics of Deformable…*

*n*

**2.4 A simple model that yields an exact tight-binding band spectrum**

Dirac *δ*-function (complete screening up to the scale of the nucleus):

The solution to *H*atom ∣*ψ*atom⟩ ¼ *E*atom ∣*ψ*atom⟩, with *H*atom ¼ � ℏ2

an exponential function of the form *ψ*atomð Þ¼ *x* 〈*x*j i *ψ*atom ¼ *x*

*Sn* ¼ ð Þ 1 þ *nt* exp ð Þ �*t* , *α* ¼ 2*E*atom exp ð Þ �*t =*sinh *t*, and

*=E*atom:

range *x*0 is related to *E*atom by�*E*atom ¼ ℏ2

expression for *λ* ≔ *E*crystal � *E*atom

**8**

*S* ¼ ½ � *t*cosh *t* � sinh *t =*½ � sinh *t*cosh *t* � *t* [3].

worse for the top than for the bottom of the band.

*ij* can be interpreted as the

*Sn* cos *nka*

*:* (8)


**7.1 General principles**

Thomas Starzl and his team are the pioneer of multiple organ harvesting from the deceased donor and the technique that they described has minimal change till now after more than 3 decades [27]. All the procurement procedures have some steps in common:


**1. Introduction**

Nowadays, yogurt is considered to be one of the most popular fermented milk products and it has gained widespread consumer acceptance as a healthy food [1]. It is prepared by fermenting milk with bacterial cultures consisting of a mixture of *Streptococcus thermophiles* and *Lactobacillus* [2]. Yogurt is as nutritious as many other fermented milk products, since it contains high level of milk solids in addition to the nutrients developed during the fermentation process. In the yogurt industry, the major concern is the production and maintenance of a product with optimal consistency and stability during transportation and storage. The texture, viscosity at high total solids, variation in the processing variables, and characteristics of the starter culture are the main components that determine yogurt's consistency [3].

The consumption of whole fat products (e.g., full fat yogurt) has declined due to the awareness of the probable harmful e ffect of fat on consumer's health, thus the dietary habits of consumers have

changed and market interest moved in favor of low or nonfat dairy products [4].

In yogurt products, milk fat plays a major role in the texture, flavor, and color development of the final products [7]. Therefore, the reduction in fat will subsequently reduce the total solids content (in low-fat and nonfat yogurt), leading to weak body, poor texture, and increased whey separation, unless various stabilizers are used [8].

Food hydrocolloids, such as starches, are usually used in the food industries as thickeners, stabilizer, gelling agents, syneresis controller, and emulsifiers [9,10]. On the other hand, they regulate flavor and aroma release [11]. The use of stabilizers in manufactured dairy products, such as yogurt, is very important for appropriate viscosity, sensory properties, and inhibiting/reducing wheying-o ff during storage and transportation, as well as boosting the ratio of total solids. There are many kinds of stabilizers, for instance, synthetic (carboxyl methyl cellulose) or natural. Plant origin stabilizers are considered to be the cheapest and they include the commonly used ones, such as corn starch. Starch is preferred in the yogurt industry, because it is a good thickener and its ability to reduce yogurt flaws by improving texture and make the product more appealing to consumers [12–14]. Starches, such as corn, sweet potato, potato, and chestnut, are commonly used by the yogurt industry at 0.25 to 1%". Although the use of starch in yogurt manufacturing is currently practiced, a comparison between the performance of starches form di fferent sources and dissimilar amylose content, such as tubers, cereals, or legumes, is not done to the best of our knowledge. Therefore, the focus of this study was to explore the e ffect of corn, sweet potato, potato, Turkish beans, and chickpea starches on the physicochemical, rheological, and sensory properties of the none-fat set yogurt during and after storage.

#### **2. Materials and Methods**


**3. Inconsistencies in examining the impacts of ESG investments**

Although the subject of ESG investing has been examined and researched extensively, we have yet reached the definitive implications of this investment concept. Some studies find that ESG investing often leads to higher returns, while others view this issue differently and suggest that the benefits of ESG investing are being exaggerated. In this section, we will use the framework developed by Kotsantonis and Serafeim [59], which explores four dimensions of inconsistencies in ESG reporting to explain the contradictory findings of ESG investing discussed in the previous sections. Furthermore, we will use this framework to provide future research direction, which might help to reduce theses contradictions.

#### **3.1 Data inconsistency**

Kotsantonis and Serafeim [59] argue that there is a question of whether ESG data accurately capture a firm's performance and suggest that there are evidence of the relationship between economic outcomes and ESG information, even though the data are of poor quality. Generally, researchers examine the relationship between ESG information and firm performance assuming explicitly or implicitly that the firm performance relative to the ESG metric is normally distributed. Companies that perform well will be located toward the upper tail of the normal distribution, whereas the underperforming firms could be found toward the lower tail of the distribution. The first plausible inconsistency lies in the fact that the relationships between company performance and ESG matrices might not be normally distributed. This inconsistency often arises from the ways that companies report their ESG data, and generally these companies report their ESG data in various different ways. This, inevitably, creates problems for researchers when they examine the effect of ESG investment and corporate performance. Kotsantonis and Serafeim [59] demonstrate this problem by using examples of 50 large Fortune 500 companies from various sectors. To report the data on Employee Health and Safety in their sustainability report, these companies measured the data in more than 20 different ways, with different terminology and different units of measure. For example, while some companies use "Accident Rate" to describe Employee Health and Safety, others use "Accidents Requiring Time Off," "Injury Rate," "Lost Day rate," or "Financial Lost Due to Injury," among others, to measure Employee Health and Safety. Besides using different metrics to measure an ESG data (such as Employee Health and Safety), the units of measure are, many times, different. For example, some companies use the unit of measurement in term of "Number," while other companies use "Ratio" or "Percentage" instead. Kotsantonis and Serafeim [59] suggest that these inconsistencies make comparisons among companies to be rather challenging since it is unclear whether these metrics provide measurements of the same thing. The performance data on these ESG metrics might lie on different forms of distribution, rather than being normal, thus making comparison of these ESG data problematic.

To see this issue in a more concrete way, we will illustrate this issue using some studies that have been reviewed in the previous sections. While Kumar et al. [8] find that investors should benefit from ESG investing due to the evidence of lower risk and higher return for high ESG factors from their study; however, study by Landi and Sciarelli [25] documented otherwise. They find no significant relationship between ESG and abnormal returns. If we look closer in each of these two studies, one dimension that we can observe in regard to data consistency or the lack of it is the time periods used in these two studies. In Kumar et al. [8], the data cover a 2-year period, from January 2014 to December 2015. On the other hand, Landi and Sciarelli [25] examine the impact of ESG rating on abnormal return by using data from 2007 to 2015. Obviously, the data examined by Landi and Sciarelli [25] cover a much longer period. Looking closer, we can see that the economies were very much different between the 2007–2013 period and the 2014–2015 period. For instant, starting from the second half of 2007 to the middle of 2009, the world economies experienced the Great Recession observing significant declines in national economies worldwide.

Not only that, in the post Great Recession period, the speed of recoveries among these economies was very much different across regions.

As stated by Kotsantonis and Serafeim [59], another dimension of data inconsistency is the ways the data were measured. As in the case of ESG measurements, there are several different methods to compile ESG data. Zhao et al. [10] find that there is a positive relationship between financial and ESG performances for listed power generation companies in China. It is also interesting to note that, because of the nature of the energy industry, the ESG evaluation index requires a specific ESG assessment and evaluation system. In Zhao et al. [10], the ESG evaluation index is based on the model proposed by the OECD and the UNEP based on the business activities of the power generation group and social development. In addition, the company's ESG will also be normalized based on the actual and standard values comparison principle, whereas the actual values are reported by the power generation companies themselves, and the standard values are determined by the internationally recognized values and global averages with inputs from the experts in the field. Importantly, certain ESG indicators are also industry specific indicators. For example, "Power plant power consumption rate" and "Power supply standard coal consumption" are two indexes, among others, used to construct the index evaluation system and have "%" and "g/kWh" as the units of measurements. Furthermore, they use the non-dimensionalized model to determine the sustainability and evaluation indexes. The comprehensive evaluation index is then calculated as the weighted sum of the sustainability index of the evaluation index.

Constantinescu [28], on the other hand, finds that there is a negative relationship between firm value and the environmental factor in the energy sector. Constantinescu [28] uses the ESG scores obtained from the Refinitiv Eikon platform. According to Refinitiv, their ESG scoring methodology is based on a five-step process flow as follows: (1) ESG category scores, (2) Materiality matrix, (3) Overall ESG score calculation and pillar score, (4) Controversies scores calculation, and (5) ESG Combined (ESGC) score. Broadly speaking, we can see that the ways ESG scores are constructed in Zhao et al. [10] and in Constantinescu [28] are very much different in terms of how each data point is calculated and how the composite or combined ESG is determined. This represents an example of the data inconsistency issue in studies that investigate the relationship between firm performance and ESG information.

In order to remedy this issue, we recommend some future directions for future studies as follows. First, there should be studies that systematically incorporate these factors into considerations and try to identify findings that are robust to different types of measurements. Second, future studies might consider statistically constructing an integrated index from all different measures so that there could be a single consistent measure to capture an ESG performance. Quantitative approaches such principal component analysis may also help to address the data inconsistency issue.

#### **3.2 Inconsistencies in benchmarking**

Another source of data inconsistencies is the use of benchmarking. Kotsantonis and Serafeim [59] point out that a crucial point when comparing ESG information among companies, i.e., which companies have better or worse ESG performance, is to look at the definition of performance that leads to the benchmark for the ESG score comparison. As such, in order to determine the company's (ESG) performance, studies often need to look at the ESG performance of a peer group or by using a predefined

#### *Perspective Chapter: The Environmental, Social, and Governance (ESG) Investment and Its… DOI: http://dx.doi.org/10.5772/intechopen.108381*

level of performance on ESG metrices. Either one of these benchmarking methods could lead to the results that are subjected to the data inconsistency issues.

To overcome this issue, it is important to note that the choice of the ESG scoring systems or peer groups has influences that affect the interpretation of the assessment [59]. We propose that future studies may consider employing methodologies such as machine learning to construct ESG scores with appropriate peer rating from unstructured corporate data. These methods would allow the analysis to be performed systematically by machine at the raw data level, which can reduce human bias and error. On the other hand, focusing on classification or clustering methods such as K-mean clustering should yield results that are robust and are comparable across market environments. For example, Margot et al. [60] demonstrated that using machine learning to classify ESG funds into appropriate groups plays an important role in affecting the return of the constructed portfolio. They found that the machine learning constructed portfolio outperformed the best-in-class approach and the benchmark approach.

#### **3.3 Methods used to impute the data**

It is possible that the "raw" data used to construct a certain element of ESG are missing. According to Kotsantonis and Serafeim [59], 50% of top 50 companies in the fortune 500 did not provide information about their health and safety policy. For another related category, 85% of top 50 companies in the fortune 500 did not provide information about their "lost time incident rates and workplace fatalities." However, the final information from various ESG data provider seems to be complete after aggregation and rarely mention about the issue associated with missing observation. Kotsantonis and Serafeim [59] argued that "two different imputed figures can deliver significantly different ESG performance ratings," and inappropriately impute missing data can lead to bias in the associated statistical analyses.

Broadly speaking, missing observations can arise from three types. First, values in a data are "missing completely at random" or MCAR. Little [61] defined MCAR as the missing values in the data that are independent of both observable and unobservable factors. One simply way to understand MCAR is when missing values in the data are generated by a random process. Second, values in the data are "missing at random" or MAR. Little [61] defined MAR as the missing values in the data that are dependent on observable factors but are independent on unobservable factors. One simply way to understand MAR is when missing values in the data are generated by a known process. A key difference between MCAR and MAR is that for MCAR, all analyses (e.g., examining the relationship between ESG performance on some measures of firms' risks or returns) can be performed without any adjustment, and the results will be unbiased. On the other hand, for the case of MAR, all analyses must use some statistical procedures to take into an account the "known" missing process that depends on observable factors. Otherwise, the results from the analyses can be biased. Finally, the last type is "missing not at random" or MNAR. For MNAR, the missing values in the data are dependent on both observable and unobservable factors. In the case of MNAR, all analyses will be biased.

Taking the example from Kotsantonis and Serafeim [59], they showed that using different imputation methods that consist of rule-based approaches (e.g., using average value of an industry) and statistical-based approach (e.g., using regression analysis, predictive mean matching, single imputation, or multiple imputation) can lead to a difference from a real value that can be as large as 8.9%. Thus, missing data in ESG reporting can be a serious issue not only to the investors who need to know reliable information about ESG scores but also the researchers who need to examine and uncover the relationship between ESG and various measures of returns and risks. We suggest that future studies examining the relationship between ESG and various measures of returns and risks need to incorporate the missing observation issue and use proper statistical methods to control for the "known" nature of gap filling in the ESG data. We also suggest that the ESG data providers must be transparent in reporting how they actually impute the ESG data. Another possibility is that the ESG providers can report two different sets of the data in which one set of data is the "raw data" that does not involve any imputation, which will be suitable for researchers who need to examine the relationship between ESG and various measure of returns and risks, and the second set of data is the "complete data" that does involve imputation, which will be suitable for investors for making investment decision.


**About the Editors**

#### **Juliana Maria Leite Nobrega de Moura Bell**

Juliana Maria Leite Nobrega de Moura Bell, Ph.D; Associate Professor: Dr. de Moura Bell research includes the development and application of environmentally friendly technologies to replace the incumbent technology for extracting and fractionating of major food components such as oil, protein, and carbohydrates. Her goal is to develop structure/function-based processes to produce foods that will improve human health, with the translation of these processes into the industrial realm being the ultimate goal of her work. Specifically, she is interested in bio-processing techniques such as enzyme-assisted aqueous extraction, fermentation, and less harsh techniques like supercritical and subcritical extractions. Her laboratory research interests include: (1) Scaling-up extraction and downstream recovery processes from laboratory to pilot-scale; (2) Determining the effects of processing conditions (extraction, heat treatment, enzymatic modifications and recovery strategies) on the functionality and biological activities of food components, and (3) The conversion of agricultural waste streams/food processing by-products into high added-value compounds.

#### **Blanca Hern ´andez-Ledesma**

Blanca Hernandez-Ledesma, PhD. Tenured Scientist. Head of the Group Development and ´ Innovation in Alternative Proteins (INNOVAPROT) at CIAL (CSIC-UAM). Her research has been focused on the biological activity of food proteins/peptides aiming at a better understanding of their health implications and the development of novel food ingredients. Author of 90 JCR articles, 37 book chapters, and 3 patents (h-index of 39, WoS). Her results have been presented in 90 international and national conferences. She has supervised 4 Doctoral Thesis (4 are currently ongoing) and 17 Master Thesis. She has participated in 30 international and national Research Projects. She has participated as member of Selection Board for Tenured Scientists, PhD and Master Thesis Dissertation committees, reviewer in international PhD theses, and member of National and International Projects Evaluation Panels. She is member of the Editorial Committees of several books and journals, and reviewer for more than 90 journals.

#### **Roberta Claro da Silva**

Roberta Claro da Silva, Ph.D. Assistant Professor: Dr. Silva's research includes lipid science and technology and the physical properties of foods. Her research focuses on developing lipid alternatives to replace saturated and trans fats in food products to improve consumers' health. Dr. Silva's goal is to develop environmentally friendly technologies to replace traditional processes by translating these processes into the industrial realm. Specifically, she is interested in techniques such as oleogelation, nanoemulsification, and sonication. Dr. Silva's team expects to substitute highly saturated fat in food products such as dairy, processed meat, and bakery products without compromising the expectations associated with these conventional food products. Her laboratory research interests include 1. Understand the fat crystallization of structured oleogels and emulsions as a function of time, temperature, and shear using a microstructural approach. 2. Examine both oil-in-water and water-in-oil emulsions, whereby we aim to improve their stability and understand the functional role of various ingredients. 3. Apply the alternative lipids sources in food matrixes and evaluate the impact on the physical and sensorial quality.


Temporomandibular Joint - Surgical Reconstruction and Managements

*Edited by Raja Kummoona*

Published in London, United Kingdom

Temporomandibular Joint - Surgical Reconstruction and Managements http://dx.doi.org/10.5772/intechopen.98032 Edited by Raja Kummoona

#### Contributors

Pablo Andrés Crespo Reinoso, Emilio Ruiz Delgado, James Jerez Robalino, Oleg Valentinovich Slesarev, Vladimír Machoň, Michal Beňo, Siddharth Mehta, Kadarkarai Kirupa, R. Rajashri, Kamali Raman, Aishwarya Balaji, Pavithra Elango, Swetha Karupaiah, Leelavathy Gopalakrishnan, Sundaram Ravanasamudram Rajaram, Gauthami Sundar, Rajaram Ravanasamudram Sundaram, Jolanta E. E. Loster, Justyna Grochala, Raja Kummoona

#### © The Editor(s) and the Author(s) 2023

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