**10. Acknowledgement**

Our research and publication were financially supported by NKFP 3A/061/2004 and TÁMOP-4.2.2/B-10/1-2010-0029. Special thanks should be given to John Michael Lynch and Emily Rauschert for the thorough linguistic corrections of our manuscript.


Acknowledgements

This work was supported by the University of Ferrara (Bando FIR-2016 to Silvia Vertuani) and Ambrosialab srl, Ferrara (Grant 2017). We thank Anna Maria Brincat (Clubclass Residential Language School, Swieqi, Malta) and Luigi Mastroianni (State Language High School "Giosuè Carducci" Ferrara, Italy) for their careful English revision.

Chemical name Name of common ingredient

2-Ethylhexyl 4-(dimethylamino)benzoate/padimate O (USAN:

2-Hydroxy-4-methoxybenzophenone-5-sulfonic acid and its

6-disulfonic acid)/bisdisulizole disodium (USAN)

ethylhexyl)oxy)phenol)/bemotrizinol

BAN)

2,20

2,20

sodium salt/sulisobenzone

butyl)phenol)/bisoctrizole

Sodium salt of 2,20

2-Ethylhexyl salicylate/octisalate Ethylhexyl salicylate 5%

Dimethicodiethylbenzalmalonate Polysilicone-15 10% Titanium dioxide Titanium dioxide 25%

Aminobenzoic acid (PABA) Up to 15% Avobenzone Up to 3% Cinoxate Up to 3% Dioxybenzone Up to 3% Homosalate Up to 15% Menthyl anthranilate Up to 5% Octocrylene Up to 10% Octyl methoxycinnamate Up to 7.5% Octyl salicylate Up to 5% Oxybenzone Up to 6% Padimate O Up to 8% Phenylbenzimidazole sulfonic acid Up to 4% Sulisobenzone Up to 10% Titanium dioxide Up to 25% Trolamine salicylate Up to 12% Zinc oxide Up to 25% Ensulizole Up to 4% Homosalate Up to 15%

Name of common ingredient glossary Concentration allowed

Benzoic acid, 2-[4-(diethylamino)-2-hydroxybenzoyl]-, hexylester Diethylamino hydroxybenzoyl

glossary

Benzophenone-4, benzophenone-5

Disodium phenyl

hexyl benzoate

Methylene bis-benzotriazolyl tetramethylbutylphenol

dibenzimidazole tetrasulfonate

Bis-ethylhexyloxyphenol methoxyphenyl triazine

Ethylhexyl dimethyl PABA 8%

Guidelines for the Development of Herbal-Based Sunscreen

http://dx.doi.org/10.5772/intechopen.72712

Maximum concentration in ready-for-use preparation

57

5% (as acid)

10% (as acid)

10%

10%

10% in sunscreen products


*Article* **A Robust Algorithm for State-of-Charge Estimation under Model Uncertainty and Voltage Sensor Bias**

**Yang Guo and Ziguang Lu \***

School of Electrical Engineering, Guangxi University, Nanning 530004, China<EMAIL_ADDRESS>**\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** Accurate estimation of the state of charge (SOC) of zinc–nickel single-flow batteries (ZNBs) is an important problem in battery management systems (BMSs). A nonideal electromagnetic environment will usually cause the measured signal to contain nonnegligible noise and bias. At the same time, due to the influence of battery ageing, environmental temperature changes, and a complex reaction mechanism, it is difficult to establish a very accurate system model that can be applied to various complex working conditions. The unscented Kalman filter (UKF) is a widely used SOC estimation algorithm, but the UKF will reduce the estimation accuracy and divergence under the influence of inaccurate model and sensor errors. To improve the performance of the UKF, a robust desensitized unscented Kalman filter (RDUKF) is proposed to realize an accurate SOC estimation of batteries in the context of different disturbances. Then, the proposed method is applied to cases of error interference, such as Gaussian noise, voltage sensor drift, an unknown initial state, and inaccurate model parameters. The simulation and experimental results show that compared with the standard UKF algorithm, the proposed estimation algorithm can effectively suppress the influence of various errors and disturbances and achieve higher accuracy and robustness.

**Keywords:** real-time estimation; robust desensitized unscented Kalman filter; state of charge; zinc–nickel single-flow batteries

**1. Introduction**

Renewable energy has been considered an alternative to coal-fired energy because its use can result in reduced pollution and greenhouse gas emissions. However, the fluctuation caused by the wide application of intermittent renewable energy, such as wind energy and solar energy, will increase the burden of the power grid. Integrating rechargeable batteries into the power grid as an energy storage system can effectively improve power quality and system reliability [1]. Redox flow batteries (RFBs) have become one of the most promising grid-connected energy storage technologies on the market because of their long life cycle, good scalability, high efficiency, and low spatial requirements [2,3]. The main difference between a typical RFB and other types of batteries is that the active materials of its positive and negative electrodes are stored in an external electrolyte storage tank in the form of electrolytes and circulated into the battery stack chamber by two independent auxiliary pumps. The RFB separates the two electrolytes through the ion exchange membrane to prevent the mixing of redox substances and to maintain the electrical neutrality of the energy storage system [4,5]. Since the concept of RFBs was put forward, different kinds of RFBs have rapidly been developed, but the high cost of ion exchange membranes and the interference of electrolyte cross pollution are still the main bottlenecks hindering the development of RFBs. A novel RFB structure was proposed in [6]. This battery dissolves zincate in high-concentration potassium hydroxide as the electrolyte. As only one electrolyte is used, there is no need for an expensive ion exchange membrane in the system design, and cross pollution of the electrolyte is avoided. At the same time, this RFB has a high open-loop voltage and energy density, is nontoxic, has a simple structure, and is low-cost [7–9]. This makes its commercialization easier.

**Citation:** Guo, Y.; Lu, Z. A Robust Algorithm for State-of-Charge Estimation under Model Uncertainty and Voltage Sensor Bias. *Energies* **2022**, *15*, 1537. https://doi.org/ 10.3390/en15041537

Academic Editors: Alon Kuperman and Alessandro Lampasi

Received: 29 November 2021 Accepted: 16 February 2022 Published: 19 February 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Zinc–nickel single-flow batteries (ZNBs) have been developed rapidly since they were first proposed. However, there are still some important problems to be solved in their practical engineering. State of charge (SOC), an important state monitored in battery management systems (BMSs), is very important to consider in attempting to avoid overcharging and/or overdischarging. At present, SOC estimation has three main research directions: the Coulomb counting method, the machine learning method, and the state observer method [10]. The Coulomb counting method is an open-loop method that is simple to implement and has a low calculation cost. However, its accuracy is greatly affected by the sensor accuracy and initial errors; thus, it is necessary to periodically use the charge– discharge cut-off voltage or open-circuit voltage to correct the estimated value [11–13]. Machine learning methods do not need to understand the internal electrochemical or external circuit characteristics of a battery. They estimate the SOC by fitting the nonlinear relationship between relevant factors such as the current, voltage, temperature, and SOC. The support vector machine (SVM) [14], neural network (NN) [15], fuzzy logic (FL) [16], and long short-term memory (LSTM) network [17] have been implemented for SOC estimation. If the appropriate training dataset is given, the machine learning method can estimate the SOC of a battery more accurately. When the training data and test data correspond to different working conditions, the robustness of the state estimation algorithm will worsen, and machine learning will require a longer training time and higher computing cost, which restricts the wide application of machine learning in actual SOC estimation. The observer method usually assumes that the battery is a dynamic system. The battery model is established to describe the change in battery state, and various filter algorithms are introduced to estimate the state variables. Among the different types of observers, the Kalman filter is the most widely used. The extended Kalman filter (EKF) was first used to solve the problem of battery state estimation. However, as the battery state space model is usually a nonlinear model and the EKF uses first-order Taylor series expansion to linearize a nonlinear model, there are large errors in estimating battery SOC using the EKF; in contrast, using the unscented Kalman filter (UKF) to estimate battery SOC results in higher robustness and accuracy [18,19].

The Kalman filter algorithm is based on a basic premise: that the established model is accurate enough to accurately track the change in battery terminal voltage under dynamic current conditions. This assumption is usually difficult to fully satisfy because the actual parameters of the model will change in accordance with the change in ambient temperature and battery life, and it is very difficult to establish a model considering all influencing factors. In [20], the error sources of SOC estimation are systematically analyzed from the perspectives of the measurement signal, model error, and state estimation algorithm. In [21], the model-based SOC estimation algorithm was shown to be more affected by voltage measurement errors and model errors than by current measurement errors and initial errors. To improve the accuracy of SOC estimation under different error sources, many different improved algorithms have been proposed.

In [22], the bias term of the model error was taken as a new state vector, which was jointly estimated with the SOC through an estimation algorithm to solve the problems of open-circuit voltage drift and voltage sensor drift [23]; a proportional integral (PI) state observer and a current integrator were used to estimate the SOC of the battery; and a drift current corrector was used to suppress the influence of the drift current. For the situation in which the battery model parameters are uncertain due to battery ageing and there is instrument measurement noise, [24] proposed a smooth variable structure filter (SVSF) to estimate the SOC and health state of lithium batteries. Compared with the EKF, the SVSF can produce more accurate SOC estimation results. In [25], the extended Kalman smoothing variable structure filter was proposed, which is a new algorithm combining the EKF and SVSF technologies. The experimental results showed that it has strong robustness with respect to inaccurate models and can improve the accuracy of SOC estimation. In [26], an observer with multiple feedback gains was designed to estimate the SOC. In the case of inaccurate modeling and current sensor drift, the SOC of the battery can be estimated

robustly, and the amount of calculation is low. In [27], an algorithm combining the strong tracking UKF and adaptive UKF was proposed, and the uncertainty of the model was identified through statistical information, which can effectively suppress the state estimation error caused by sensor drift. In [28], the observability of the second-order RC nonlinear model was analyzed, and it was proposed that accurate voltage measurement is more important for SOC estimation than current measurement accuracy, which is consistent with the conclusion of [21]. At the same time, Zhao et al. [28] proposed using the extended model to address the reduction in the accuracy of SOC estimation when errors occur in sensors and pointed out that when errors occur in voltage and current sensors, the method used may not accurately estimate the SOC and bias at the same time. In [29,30], the state estimation problem was studied when noise and bias occur in voltage sensors or current sensors. The DEKF was used to estimate the battery SOC, and the results showed that the proposed estimation method has good convergence and robustness. The two-layer state estimator proposed in [31] estimated the SOC of a battery, limiting the state estimation error to ±4% when the model is inaccurate and current deviation exists.

In summary, although voltage offset and model error are the main factors affecting the reduction in SOC estimation accuracy, few studies have considered the SOC estimation problem when voltage offset or model error exists. Moreover, the BMS is affected by external electromagnetic interference in harsh operating environments. The signals received from voltage and current sensors are usually polluted by random noise, which reduces the SOC estimation accuracy. To solve the above problems, a robust UKF algorithm is proposed to improve the robustness of the SOC estimation of ZNBs under Gaussian noise, inaccurate modeling, or voltage sensor drift. The proposed method has the following advantages: (1) it can suppress the influence of Gaussian measurement noise in a sensor, (2) it has good robustness to model parameter uncertainty, and (3) it improves the accuracy of battery SOC estimation in the presence of voltage sensor drift.

The structure of the paper is as follows: In Section 2, the equivalent circuit model of a ZNB is introduced. In Section 3, the application of the proposed robust UKF algorithm in battery SOC estimation is introduced. In Section 4, the estimation results of a robust UKF in the presence of model parameter disturbance or voltage bias are verified and compared with those of a UKF. Section 5 offers the main conclusions of the paper.

#### **2. Equivalent Circuit Model**

The SOC of ZNBs can be defined as the ratio of residual capacity to maximum capacity, which can be expressed by (1). *SOCt* and *SOC*0 are the initial *SOC* at time *t* and at the beginning of estimation, respectively, *Qn* is the maximum capacity of the battery, and *i*(*τ*) is the load current. It is assumed that the current is positive when the battery is discharged.

$$\text{SOC}\_{t} = \text{SOC}\_{0} - \frac{1}{Q\_{\text{u}}} \int\_{0}^{t} i(\mathbf{r}) d\mathbf{r} \tag{1}$$

At present, the commonly used battery models mainly include neural network models, electrochemical models, and equivalent circuit models (ECMs). Among the electrochemical models, the one-dimensional (1D) model [32] and pseudo-two-dimensional (P2D) model [33,34] are widely used. As the electrochemical model is the first-principles model and has clear physical significance, it can not only describe the relationship between current and voltage at the macroscale but also analyze the concentration, current and potential distribution of reaction products in the electrode and electrolyte at the microscale. However, the complex coupled nonlinear partial differential equations and the model parameters that are difficult to measure directly cause the electrochemical model to have high requirements regarding the amount of calculation. The neural network model is the opposite of the electrochemical model. This model does not consider the mechanism of a battery and uses only the data-driven method to approximate the dynamic characteristics of the battery. Compared with other models, it requires more data to train the network model. ECM uses circuit elements such as resistance, capacitance, and voltage source to build

a circuit network to simulate the dynamic response of voltage under different working conditions. The commonly used equivalent circuit models include the Thevenin model and second-order RC equivalent circuit model, which adds a group of RC networks. Therefore, the polarization and diffusion characteristics inside the battery can be better simulated.

A typical second-order-equivalent circuit model is shown in Figure 1. *R*1 and *R*2 represent the polarization resistance, and C1 and C2 represent the polarization capacitance. The equivalent circuit model uses parallel RC branches to characterize the electrochemical polarization and concentration polarization of ZNBs during charge and discharge. *VOCV* represents the open-circuit voltage of the battery, which is the difference in the equilibrium electrode potential between the positive and negative electrodes when the battery is in a reversible equilibrium state during the open-circuit period. *R*0 is the ohmic resistance, which is used to characterize the energy loss caused by the resistance between the battery electrode and electrolyte. The terminal voltage and current of the battery are expressed as *Vt* and I, respectively, and the current during discharge is defined as a positive number. According to Kirchhoff's law and (1), the discrete state space equation of a dynamic battery can be easily deduced, as shown in (2) and (3). It is worth noting that for a ZNB, because the open-circuit voltage, polarization capacitance, and polarization resistance are functions related to the SOC, the state equation and output equation of this state space equation are nonlinear equations.

$$
\begin{bmatrix} SOC(k) \\ U\_1(k) \\ U\_2(k) \end{bmatrix} = \begin{bmatrix} 1 & 0 & 0 \\ 0 & \exp(-\Delta t/R\_1/C\_1) & 0 \\ 0 & 0 & \exp(-\Delta t/R\_2/C\_2) \end{bmatrix} \begin{bmatrix} SOC(k-1) \\ U\_1(k-1) \\ U\_2(k-1) \end{bmatrix} \tag{2}
$$

$$
+ \begin{bmatrix} -\eta \frac{\Delta t}{Q\_\pi} \\ [1 - \exp(-\Delta t/\tau\_1)]R\_1 \\ [1 - \exp(-\Delta t/\tau\_2)]R\_2 \end{bmatrix} I(k-1)
$$

$$
V\_t(k) = V\_{\text{OCV}}(SOC(k)) - lI\_1(k) - lI\_2(k) - R\_0I(k) \tag{3}
$$

**Figure 1.** Schematic diagram of the two-order RC ECM.

To simplify the model, the parameters are assumed to be related only to the SOC, but in fact, the model parameters are also affected by the ambient temperature, current, and battery ageing state. Ignoring these factors in modeling reduces the accuracy of the model in a complex working state and affects the accuracy of the algorithm when used to estimate the SOC.

#### **3. Robust Unscented Kalman Filter**

For nonlinear systems such as ZNBs, the corresponding discrete state space representation is as follows:

$$\mathbf{x}\_{k} = f(\mathbf{x}\_{k-1}, \mathbf{u}\_{k-1}) + \mathbf{w}\_{k-1} \tag{4}$$

$$y\_k = h(\mathbf{x}\_{k'} \boldsymbol{u}\_k) + \boldsymbol{v}\_k \tag{5}$$

where *xk* = [*SOC U*1 *U*2] *T* is the state at time *k*, and *uk* and *yk* are the input current and output voltage at time *k*, respectively. *f*(*xk*−1, *uk*−1) is the state transfer function, *h*(*xk*, *uk*) is the measurement function, and *wk*−1 and *vk* are the process noise and the measurement noise, which are usually assumed to be zero-mean white noise with covariance matrices *Q* and *R*, respectively.

Unlike the EKF, which uses only Taylor series expansion to approximate the nonlinear state space model [35], the UKF uses a traceless transformation (UT) to determine the sampling points near the estimation points and uses these sample points to approximately represent the random distribution of the nonlinear function [36]; thus, the UKF algorithm does not need to calculate the Jacobian matrix of the system model (compared with the EKF). At the same time, there is no need for random sampling (compared with the particle filter (PF) algorithm); therefore, the UKF can be regarded as a compromise between the EKF and PF. The SOC estimation process of a ZNB based on the UKF can be summarized as follows:

(1) Initialize the value of the state variable and the covariance:

$$\begin{cases} \overline{\mathbf{x}}\_0 = E(\mathbf{x}\_0) \\ P\_0 = E[(\mathbf{x}\_0 - \overline{\mathbf{x}}\_0)(\mathbf{x}\_0 - \overline{\mathbf{x}}\_0)^T] \end{cases} \tag{6}$$

where *E* (·) is the expected mean value.

(2) Generate 2*n* + 1 sigma vectors and corresponding weights at the *k* − 1 step, where *n* is the dimension of the state variable:

$$\begin{cases} \begin{aligned} \hat{\chi}\_{0,k-1} &= \hat{\mathfrak{x}}\_{k-1} \\ \hat{\chi}\_{i,k-1} &= \hat{\mathfrak{x}}\_{k-1} + \left(\sqrt{(n+\lambda)P\_{k-1}}\right)\_{i} i = 1, 2, \dots, n \\ \hat{\chi}\_{i,k-1} &= \hat{\mathfrak{x}}\_{k-1} - \left(\sqrt{(n+\lambda)P\_{k-1}}\right)\_{i} i = n+1, \dots, 2n \end{aligned} \end{cases} \tag{7}$$

$$\begin{cases} \omega\_{0}^{m} = \frac{\lambda}{(n+\lambda)} \\ \omega\_{0}^{c} = \frac{\lambda}{(n+\lambda)} + \left(1+\beta-\alpha^{2}\right) \\ \omega\_{j}^{m} = \omega\_{j}^{c} = \frac{1}{2(n+\lambda)}, j = 1, 2, \dots, 2n \end{aligned} \tag{8}$$

Here, *λ* is a scaling parameter, which can be expressed as *λ* = *α*2(*n* + *h*) − *n*, and *α* determines the spread of the sigma points around *x*ˆ*k*−1 and is usually set to a small positive value. *ωm* and ω*c* are covariance weights, and *β* is a parameter that is used to incorporate prior knowledge of the distribution of the state variable. For Gaussian distributions, one can assume *β* = 2.

(3) State prediction

Update the sample point and calculate the propagated mean:

$$\overline{\chi}\_{j,k} = f\left(\hat{\chi}\_{j,k-1}, \mu\_{k-1}\right), j = 0, 1, \dots, 2n \tag{9}$$

$$
\overline{\mathbf{x}}\_k = \sum\_{j=0}^{2n} \omega\_j^{(\mathfrak{m})} \overline{\chi}\_{j,k} \tag{10}
$$

Update the priori error covariance of state:

$$\mathcal{P}\_k = \sum\_{j=0}^{2n} \omega\_j^{(c)} [\overline{\chi}\_{j,k}^x - \overline{\chi}\_k] [\overline{\chi}\_{j,k}^x - \overline{\chi}\_k]^T + Q\_k \tag{11}$$

where *Qk* is the covariance of system process noise.

(4) Measurement update

Predict the measurement from the propagated sigma points and estimate the mean of the predicted measurement.

$$\mathcal{Y}\_{j,k} = h\left(\overline{\chi}\_{j,k'} \mu\_k\right), j = 0, \dots, 2L \tag{12}$$

$$\overline{y}\_k = \sum\_{j=0}^{2n} \omega\_j^{(m)} \mathcal{Y}\_{j,k} \tag{13}$$

Calculate the covariance of the measured variables and associated covariance matrices, where *Rk* is the covariance of measurement noise.

$$\overline{P}\_{yy,k} = \sum\_{j=0}^{2n} \omega\_j^{(c)} \left[ \mathcal{Y}\_{j,k} - \overline{y}\_k \right] \left[ \mathcal{Y}\_{j,k} - \overline{y}\_k \right]^T + \mathcal{R}\_k \tag{14}$$

$$\overline{P}\_{xy,k} = \sum\_{j=0}^{2n} \omega\_j^{(c)} \left[ \overline{\chi}\_{j,k} - \overline{\pi}\_k \right] \left[ \mathcal{Y}\_{j,k} - \overline{\mathcal{Y}}\_k \right]^T \tag{15}$$

Calculate the posterior estimate and the covariance:

$$\pounds\_k = \overline{\pi}\_k + K\_k (y\_k - \overline{y}\_k) \tag{16}$$

$$\mathcal{P}\_k = \overline{\mathcal{P}}\_k - \overline{\mathcal{P}}\_{xy,k} \mathcal{K}\_k^T - \mathcal{K}\_k \overline{\mathcal{P}}\_{xy,k}^T + \mathcal{K}\_k \overline{\mathcal{P}}\_{yy,k} \mathcal{K}\_k^T \tag{17}$$

where *Kk* is the Kalman gain, which can be expressed as *Kk* = *Pxy*,*kP*−1 *yy*,*k*.

The standard UKF has been successfully applied to battery SOC estimation [37,38]. It is easy to prove that the UKF is an unbiased state estimator when all system model parameters and noise statistics are accurately known. However, when the parameters of the system model are uncertain, the theoretical behavior and actual behavior of the filter will be inconsistent. Compared with the actual state value, the state estimation value will have a large deviation and even a serious divergence problem. Therefore, the RDUKF is then applied to the SOC estimation of ZNBs [39] to obtain a more robust SOC state estimation scheme.

Before using the RDUKF to estimate the battery SOC, the state equation and observation equation need to be changed to the following form:

$$\mathbf{x}\_{k} = f(\mathbf{x}\_{k-1}, \mathbf{u}\_{k-1}, \mathbf{a}) + w\_{k-1} \tag{18}$$

$$y\_k = h(\mathbf{x}\_{k\prime} u\_{k\prime} \boldsymbol{\omega}) + v\_k \tag{19}$$

where *α* is the uncertainty parameter in the established model.

The influence of model disturbance parameters on the accuracy of state estimation is measured by the sensitivity matrix of the state estimation error relative to the disturbance parameters. Therefore, (20) is used to evaluate the influence of uncertain parameters on a priori estimation and a posteriori estimation in the UKF algorithm, and *αi* is the I component of uncertain parameters.

$$
\overline{\sigma}\_{i\_k} = \frac{\partial \overline{\pi}\_k}{\partial \alpha\_i}, \hat{\sigma}\_{i\_k} = \frac{\partial \hat{\alpha}\_k}{\partial \alpha\_i} \tag{20}
$$

The sensitivity matrix can be generated by calculating the partial derivatives of the parameters in (10) and (16), where *l* is the number of uncertain parameters.

$$
\overline{\sigma}\_{i\_k} = \sum\_{j=0}^{2L} \omega\_j^{(m)} \frac{\partial \overline{\chi}\_{j,k}}{\partial \alpha\_i}, i = 1, \dots, \ell \tag{21}
$$

$$
\sigma i\_k = \overline{\sigma}\_{i\_k} - K\_k \frac{\partial \overline{y}\_k}{\partial \alpha\_i}, i = 1, \dots, \ell \tag{22}
$$

Corresponding to the UKF algorithm, the calculation of the sensitivity of the RDUKF can be divided into four steps:

(1) Calculate the sensitivities of the sigma points for each uncertain parameter.

$$\begin{cases} \frac{\partial \hat{\lambda}\_{0,k-1}^{\circ}}{\partial \boldsymbol{a}\_{i}} = \hat{\sigma}\_{i\_{k-1}}^{T} \\\\ \frac{\partial \hat{\lambda}\_{j,k-1}^{\circ}}{\partial \boldsymbol{a}\_{i}} = \hat{\sigma}\_{i\_{k-1}}^{T} + \sqrt{(n+\lambda)} \left(\frac{\partial \sqrt{\hat{P}\_{k-1}}}{\partial \boldsymbol{a}\_{i}}\right)\_{j}; j = 1, 2, \dots, n \\\\ \frac{\partial \hat{\lambda}\_{j,k-1}^{\circ}}{\partial \boldsymbol{a}\_{i}} = \hat{\sigma}\_{i\_{k-1}}^{T} + \sqrt{(n+\lambda)} \left(\frac{\partial \sqrt{\hat{P}\_{k-1}}}{\partial \boldsymbol{a}\_{i}}\right)\_{j}; j = 1, 2, \dots, n \end{cases} \tag{23}$$


*Article* **4,5-Dihydro-5-Oxo-Pyrazolo[1,5-a]Thieno[2,3-c]Pyrimidine: A Novel Scaffold Containing Thiophene Ring. Chemical Reactivity and In Silico Studies to Predict the Profile to GABAA Receptor Subtype**

**Letizia Crocetti , Gabriella Guerrini \* , Fabrizio Melani, Claudia Vergelli and Maria Paola Giovannoni**

Neurofarba, Pharmaceutical and Nutraceutical Section, University of Florence, 50019 Sesto Fiorentino, Italy<EMAIL_ADDRESS>(L.C<EMAIL_ADDRESS>(F.M<EMAIL_ADDRESS>(C.V<EMAIL_ADDRESS>(M.P.G.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +39-055-457-3766

**Abstract:** The isosteric replacement of the benzene with thiophene ring is a chemical modification widely applied in medicinal chemistry. Several drugs containing the thiophene ring are marketed for treating various pathologies (osteoporosis, peripheral artery disorder, psychosis, anxiety and convulsion). Taking into account this evidence and as a continuation of our study in the GABAA receptor modulators field, we designed and synthesized new compounds containing the thiophene ring with 4,5-dihydro-5-oxo-pyrazolo[1,5-a]thieno[2,3-c]pyrimidine and pyrazolo[1,5-a]thieno[2,3 c] pyrimidine scaffold. Moreover, these cores, never reported in the literature, are isosteres of pyrazolo[1,5-a]quinazolines (PQ), previously published by us as GABAAR subtype ligands. We introduced in the new scaffold those functions and groups (esters, ketones, alpha/beta-thiophene) that in our PQ derivatives were responsible for the activity, and at the same time, we have extensively investigated the reactivity of the new nucleus regarding the alkylation, reduction, halogenation and hydrolyses. On the six final designed compounds (**12c**–**f**, **22a,b**) molecular docking and dynamic simulation studies have been performed. The analysis of dynamic simulation, applying our reported model 'Proximity Frequencies', collocates with high probability **12c**, **22b**, in the agonist class towards α1β2γ2-GABAAR.

**Keywords:** 4,5-dihydro-5-oxo-pyrazolo[1,5-a]thieno[2,3-c]pyrimidines; novel scaffold; thiophene; molecular dynamic studies; proximity frequencies (PF); GABAA receptor subtype; GABAA receptor ligands

### **1. Introduction**

Thiophene is known to be an isostere of the benzene ring, thus representing a good tool in the field of medicinal chemistry to develop new drugs. The concept of isosterism applied to the thiophene ring was established in 1932 by Erlenmeyer, which proposed the equivalence between -CH = CH- and -S- (in benzene and thiophene, respectively) in terms of size, mass and capacity to provide an aromatic lone pair. Over time, compounds containing thiophene have been extensively investigated in medicinal chemistry, since they show a variety of pharmacological activities, such as anti-inflammatory, antioxidant, antimicrobial, antitumor, and antidepressant action [1–4]. Moreover, several thiopheneheterocycles-fused compounds have been approved by FDA as therapeutic agents for the treatment of osteoporosis (Raloxifene), peripheral artery disorder (Ticlopidine), psychosis (Olanzapine), anxiety and convulsion (Etizolam); see Figure 1.

**Citation:** Crocetti, L.; Guerrini, G.; Melani, F.; Vergelli, C.; Giovannoni, M.P. 4,5-Dihydro-5-Oxo-Pyrazolo[1,5 a]Thieno[2,3-c]Pyrimidine: A Novel Scaffold Containing Thiophene Ring. Chemical Reactivity and In Silico Studies to Predict the Profile to GABAA Receptor Subtype. *Molecules* **2023**, *28*, 3054. https://doi.org/ 10.3390/molecules28073054

Academic Editor: Joseph Sloop

Received: 9 March 2023 Revised: 27 March 2023 Accepted: 28 March 2023 Published: 29 March 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Figure 1.** Thiophene-heterocycles-fused compounds known in literature.

Starting from these evidences, we addressed our research towards the synthesis of heterocyclic compounds containing the thiophene ring. We applied this strategy to the synthesis of potential GABAAR subtypes ligands, which represent a field of research extensively investigated by us for many years, by obtaining some interesting derivatives with pyrazolobenzotriazine, pyrazolopyrimidine and pirazoloquinazoline scaffold [5–8]. This choice is also supported by the fact that in the literature are present some GABAAR ligands (Ro 19-4603, TB21007, Comp. 4, Comp. 16), in which the thiophene ring is fused or bonded to other cycles (Figure 2) [9–11]. In particular, we report here the synthesis of new compounds with 4,5-dihydro-5-oxo-pyrazolo[1,5-a]thieno[2,3-c]pyrimidine and pyrazolo[1,5-a]thieno[2,3-c]pyrimidine scaffold as result of the isosteric replacement of the benzene with thiophene ring in our pyrazolo[1,5-a]quinazolines previously published as GABAAR subtype ligands [8,12] (Figure 3). Moreover, these new compounds can be considered as analogues of the abovementioned GABAAR ligand Ro 19-4603, in which we formally operated a contraction of the central diazepine ring. As a first approach, in this new scaffold, we tried to introduce at position 3 those functions and groups (esters, ketons, alpha/beta-thiophene) responsible for activity in our pyrazolo[1,5-a]quinazolines and in compounds of the literature (i.e., Ro 19-4603 and Comp 16) (Figure 2).

The pyrazolo[1,5-a]thieno[2,3-c]pyrimidine core is not found in the literature (SciFinder, Reaxys), with the exception of the ethyl 4,5-dihydro-5-oxo-pyrazolo [1,5-a]thieno[2,3 c]pyrimidine-3-carboxylate (RN 942034-93-7), of which neither the synthesis nor the characterization is reported, however. In addition, this single compound is not mentioned in any published work. Thus, it was very intriguing to investigate the feasibility and the reactivity of this nucleus toward the most common reactions, such as alkylation, reduction, halogenation, and hydrolysis. Finally, molecular docking studies and evaluation of the 'Proximity Frequencies' (exploiting our reported model) [8,13] were performed on all the final compounds to predict their profile on the α1β2γ2-GABAAR subtype.

**Figure 2.** GABAAR ligands with a thiophene ring reported in the literature (numbering is that of the original manuscript).

**Figure 3.** Aim of the work.

#### **2. Results**

#### *2.1. Chemistry*

The synthetic pathways for obtaining derivatives with pyrazolo[1,5-a]thieno[2,3 e]pyrimidine scaffold are depicted in Schemes 1–6. In this synthetic section, we report not only the procedures for obtaining the final designed products mentioned in Figure 3, but also some reactivity studies on this new scaffold which, furthermore, have produced interesting results. NMR spectra, elemental analysis and other structural information are reported in Supplementary Materials, Table S1.

The first step in building the pyrazolo[1,5-a]thieno[2,3-c]pyrimidine core is a diazotization reaction in the usual manner followed by reduction with SnCl2, on the commercial material methyl 3-aminothiophene-2-carboxylate **1**, obtaining the corresponding 3-hydrazino hydrochloride derivative **2** [14]. This latter was then reacted with ethoxymethylenmalononitrile and ethyl 2-cyano-3-ethoxyacrylate, affording the pyrazolo[1,5-a]thieno[2,3-e]pyrimidin-5(4H)-ones 3-carbonitrile and 3-ethoxycarbonyl derivative **3** and **4**, respectively; further hydrolysis of **3** with H2SO4 conc. gave the 3-carboxamide derivative **5**. Compounds **6**–**8** were instead obtained by treatment of the same 3-hydrazino hydrochloride derivative **2** with 3-(dimethylamino)-2- (thien-2-carbonyl)acrylonitrile (compound **6**), 3-oxo-2-

(thien-3-yl)propionitrile (compound **7**), and 3-oxo-2-(thien-2-yl)propionitrile (compound **8**), respectively, following the procedure reported in our references [15,16] (Scheme 1).

**Scheme 1.** Reagent and conditions: (a) NaNO2/H2O, HCl, SnCl2 , 0 ◦C; (b) ethoxymethylenmalononitrile for **3** and ethyl 2-cyanoethoxyacrylate for **5**, DMF/AcONa, refluxing temperature; (c) H2SO4 , 80 ◦C; (d) 3-(dimethylamino)-2-(thien-2-ylcarbonyl)acrylonitrile, DMF/AcONa, refluxing temperature; (e) 3-oxo-2-(thien-3-yl)propionitrile for **7** and 3-oxo-2-(thien-2-yl)propionitrile for **8**, AcOH, refluxing temperature.

All the **3***–***8** pyrazolo[1,5-a]thieno[2,3-e]pyrimidin-5(4H)-ones intermediates synthesized in Scheme 1 were then subjected to halogenation, reduction and alkylation reactions (Scheme 2) and the results differed depending on the function/group bound at position 3 (**3**, R3 = CN; **4**, R3 = COOEt; **5**, R3 = CONH2; **6**, R3 = thien-2-yl carbonyl; **7**, R3 = 3-thienyl; **8**, R3 = 2-thienyl). Starting from the halogenation reaction, the treatment with POCl3/PCl5 only for compounds **5** and **6** gave the corresponding 5-chloro derivatives **9c** and **9d**, which were effortlessly isolated and purified. From compound **7**, the intermediate 5-chloro derivative results as one spot in TLC but was not isolated (**9e**) and used as such for the subsequent reduction reaction. Differently, for compounds bearing a cyano group (**3**) or a thienyl ring at the 3-position (**7** and **8**), the 5-chloro derivatives were not obtained or were not easily isolable. In particular, from compounds **3** and **8** dark tares resulted in TLC many spots of complex purification. For the next step, involving the C-Cl bond cleavage and the resulting reduction to the 4,5-dihydro derivative **10c**,**d**, we choose NaBH4 in the EtOH/CH2Cl2 mixture. The ethyl 5-chloropyrazolo[1,5-a]thieno [2,3-e]pyrimidine-3-carboxylate **9c** and the 3-(thien-2-ylcarbonyl)-5-chloropyrazolo [1,5-a]thieno[2,3-e]pyrimidine **9d** were rapidly transformed into the final compounds **10c**,**d**, which were easily recovered and purified; the (hetero) aromatization was then realized by treating **10c** and **10d** in toluene with Pd/C at refluxing temperature, obtaining the final products **11c** and **11d**.

A particular reactivity was evidenced for not isolated intermediate **9e**, the 3-(thien-3-yl)-5-chloropyrazolo[1,5-a]thieno[2,3-e]pyrimidine. In fact, when the reduction is performed with NaBH4, starting and final products mixture was anyway recovered, also largely changing the reaction conditions. On the other hand, performing a catalytic transfer hydrogenation (CTH) with HCOONH4 and Pd/C in EtOH [17], it was possible to highlight in TLC a spot that could be the 4,5-dihydro derivate, which rapidly and spontaneously converts into the 3-(thien-3-yl)pyrazolo[1,5-a]thieno[2,3-e]pyrimidine **11e**. Moving to the alkylation reactions, which afforded the final desired compounds **12c**–**f**, the lactam derivatives (**3***–***8**) were all treated following the classical method (DMF/K2CO3/CH3I), but depending on the substituent at the 3-position, different reactivity was evidenced, specifically:

**Scheme 2.** Reagent and conditions: (a) POCl3/PCl5 refluxing temperature; (b) NaBH4/EtOH/CH2Cl2 , RT◦ , 40 min; (c) Toluene, Pd/C, refluxing temperature for **10c**,**d**; (d) HCOONH4/EtOH, Pd/C, refluxing temperature for **9e**; (e) DMF/K2CO3/CH3 I, 80 ◦C for **12a**,**c**,**d**–**f** and **13b**,**e**,**f**; at reflux temperature for **12b**; (f) H2SO4 conc.

The regioselective O-alkylation observed for compound **5** could be due to a prevalence of the tautomeric form −N = C-OH with respect −NH-C = O; the predominance of the first one could indeed be associated with the amide function at position 3, since the CONH2 group could form H-bonds with N-4, thus favoring the tricyclic heteroaromatic structure (see Figure 4). On the other hand, the rising temperature (reflux) can promote a free rotation of the C3-CO bond of the amide group, no longer involved in an H-bond, and thus, allowing alkylation at N-4 (compound **12b**).

**Figure 4.** A possible explanation for O-alkylation.

Scheme 3 describes the different reactivity of the 3-ethyl carboxylate derivatives **4** and **12c** towards the alkaline or acid hydrolysis. The starting ethyl 5-oxo-4,5-dihydropyrazolo [1,5-a]thieno[2,3-e]pyrimidine-3-carboxylate **4** behaves in the usual manner to alkaline or acid hydrolysis, giving the corresponding 3-carboxylic acid **14,** which in turn undergoes decarboxylation in HCl 12M, at reflux temperature, yielding compound **15**. Instead, starting from the ester **12c**, also using different reaction conditions (NaOH 10% or LiOH in THF/water or AcOH/HCl or conc. HCl), it has never been possible to obtain the 3-carboxylic acid, but only the 3-decarboxylate derivative **16** is recovered.

**Scheme 3.** Reagent and conditions: (a) for **4** NaOH 10% solution or HCl conc.; for **12c** NaOH 10% solution or LiOH or HCl conc.; (b) HCl conc. reflux temperature.

Thus, to get the 4-methyl-5-oxo-4,5-dihydropyrazolo[1,5-a]thieno[2,3-e] pyrimidine- -3 carboxylic acid, as a key intermediate for obtaining the final designed esters, we followed a reported procedure [18], which involves diazotization of the 3-carboxamide **12b** (Scheme 4); the reaction did not afford the desired 3-carboxylic acid, but a mixture of two compounds, one identified as the 3-decarboxylate **16** and a possible mechanism of decarboxylation process is reported. The second compound, colored green, was assumed to be a 3-nitroso

derivative **17**; this hypothesis could be supported by the fact that, after decarboxylation, a high concentration of nitrosonium ion (NO+ ) in the chemical environment could be able to make an electrophilic attach at position 3 of the pyrazolothienoquinazoline scaffold. The 1H-NMR spectrum of the supposed compound **17**, in addition to the absence of the proton at position 3, shows a shift of the methyl bound to N-4 (4.23 ppm) that is not consistent with the chemical shift values of the 4-methyl-5-oxo-4,5-dihydropyrazolo[1,5-a]thieno[2,3 e]pyrimidine derivatives, whose N-methyl group constantly falls in the 3.3–3.7 ppm range. Moreover, the chemical shift of the proton in position 2 is lower than in other products with different substituents (CN, COOEt, CONH2, COOH), suggesting a different electronic/steric environment resulting from the substituent in position 3. The mass analysis confirmed the structure of compound **17**. The same reaction performed on the 5-methoxypyrazolo [1,5-a]thieno[2,3-e]pyrimidine-3-carboxyamide **13b** also gave a mixture of two products, but in this case, the green 3-nitroso derivative **19** was obtained together with the desired 5-methoxypyrazolo[1,5-a]thieno[2,3-e]pyrimidine-3-carboxylic acid (**18**). The mass analysis again confirmed the structure. A possible mechanism of decarboxylation is reported in Figure 5.

**Scheme 4.** Reagent and conditions: (a) H2SO4 conc., NaNO2/H2O, RT for 12 h.

**Figure 5.** A possible mechanism of decarboxylation.

In order to obtain the desired 4-methyl-5-oxo-4,5-dihydropyrazolo [1,5-a]thieno[2,3 e]pyrimidine-3-carboxylic acid **21**, we explored a further synthetic strategy reported in Scheme 5. The 3-unsubstituted compound **16** was treated with HMTA obtaining the 4 methyl-5-oxo-4,5-dihydropyrazolo[1,5-a]thieno[2,3-e]pyrimidin- 3-carboxaldehyde derivative **20** and its further oxidation (KMnO4/water/acetone/sodium hydroxide) finally gave the desired 3-carboxylic acid **21**. From acid **21**, the final desired esters **22a,b** were obtained by treatment with thionyl chloride and further addition of the suitable alcohol (t-BuOH and 2-thiophenemethanol respectively) in CH2Cl2.

The hydrolysis of the ester function to carboxylic acid also created problems on the ethyl pyrazolo[1,5-a]thieno[2,3-e]pyrimidine-3-carboxylate **11c** (Scheme 6). In fact, the desired product **23** was recovered in a meagre yield together with a big amount of the 3-decarboxylate **24** only if the hydrolysis of **11c** was performed in an alkaline medium; the reaction then evolved spontaneously towards the total formation of compound **24**. On the contrary, in acid medium, compound **11c** underwent a decarboxylation, directly giving the 3-unsubstituted derivative **24**.

Therefore, we explored a different synthetic way, starting from the 5-oxo-4,5-dihydropyrazolo[1,5-a]thieno[2,3-e]pyrimidin-3-carboxylic acid **14**, by using LiAlH4 as reducing agent for the lactam function. Thus, after quenching the reaction and performing the standard workup, the residue was refluxed in toluene and Pd/C. The presence in 1H-NMR spectrum of H5 at 9.06 ppm confirmed that the pyrazolothienopyrimidine core was indeed dehydrogenated but, at the same time, the carboxylic function was transformed in a methyl group, as evidenced by the peak at 2.33 ppm, compound **25**, again preventing the desired **23**.

**22a,b**

**Scheme 5.** Reagent and conditions: (a) AcOH/HMTA, 80 ◦C; (b) aceton/water, KMnO4 , NaOH 10%, 80 ◦C; (**c**) SOCl2 , CH2Cl2 , t-BuOH for **22a** and 2-thiophenemethanol for **22b**, 50 ◦C.

**Scheme 6.** Reagent and conditions: (a) NaOH 10% solution, or LiOH; (b) HCl or AcOH; (c) LiAlH4, THF abs., then: toluene, Pd/C, refluxing temperature.

#### *2.2. Molecular Dynamic Studies*

On the six final designed compounds (**12c**–**f** and **22a**,**b**), a molecular docking study and an evaluation of the 'Proximity Frequencies' [8,13] were performed to predict their profile on the α1β2γ2-GABAAR subtype.

The value of Proximity Frequencies (PFs), used in a linear discriminant function (LDA), was able to correctly collocate 70.6% of agonists and 72.7% of antagonists by combining a double PF (αVal203-γThr142) with a triple PF (αHis102-αTyr160-γTyr58). The predictive capacity was evaluated on an appropriate training set of molecules with a cross-validation 'leave one out' (LOO) procedure. During a molecular dynamic simulation (60 ns), the agonist compounds were simultaneously close to the αVal203 and γThr142 amino acids, with a frequency of 37% compared to the frequency of 16% found by the antagonist compounds, while the antagonist compounds were simultaneously close to the αHis102, αTyr160, and γTyr58 amino acids, with a frequency of 35% against a frequency of 13% for agonist compounds. All the 3D structures of the molecules, as a training set and new final compounds, were designed [19] (DS ViewerPro 6.0 Accelrys Software Inc., San Diego, CA, USA) and placed in the binding site of the BDZs with the AUTODOCK 4.2 [20] docking program. The structure of the BDZ binding site was obtained from the recently solved GABAAR structure (PDB ID 6D6T) [21].

The docking program performed on the selected compounds (**12c**–**f** and **22a**,**b**) gave a number of clusters of conformation(s) for each compound (rmsd 2.0). The evaluation of trajectories in the dynamic simulation was performed on the conformations that covered at least 90% of poses; the dynamic simulations were performed on an isolated portion of the protein between the α and γ chains comprising all amino acids within a radius of 2 nm from the center of the benzodiazepine binding site. Applying the PF model to the new selected compounds, it emerges that all six ones are collocated in the agonist class, **12d**–**f** with low probability, while **12c** and **22b**, the 3-ethyl and the 3-(2-thienylmethyl)carboxylate, respectively, with a percentage of prediction of 74% and 78%, are more probable. The 3-tbuthylcarboxylate **22a** shows a percentage of prediction slightly lower (69%), but always in the agonist class; see Table 1.

**Table 1.** Profile prediction obtained by PF model (LDA results).

Compounds **12c** and **22b** have in the position 3 an ester group which is able to engage a strong hydrogen bond interaction with γThr142 through the carbonyl moiety. Additionally, **22a** has an ester group in the 3 position but the steric hindrance of the *t*-butyloxycarbonyl fragment makes less probable the hydrogen bond interaction of the carbonyl group with the γThr142 residue; see Figures 6–8.

**Figure 6.** Hydrogen bond interaction of compound **12c** in the last frame of dynamic simulation.

**Figure 7.** Hydrogen bond interaction of compound 22b in the last frame of dynamic simulation.

**Figure 8.** Hydrogen bond interaction of compound **22a** in the last frame of dynamic simulation.

These results are in accordance with our previously reported data [6,12], which evidenced the importance of the carbonyl group of the ester moiety to engage a strong hydrogen bond interaction with receptor protein. Compounds missing the ester group (**12d**–**f**) show a weak interaction with γThr142 in agreement with the low prediction percentage.

#### **3. Experimental Section**

### *3.1. Chemistry*

**General procedure for the synthesis of compounds 3,4,6**-**8**. To a solution of 2 (1 mmol, 0.170 g) in DMF abs. and sodium acetate (1.3 mmol) was added 2-ethoxymethylenmalononitrile or ethyl-2-cyano-3-ethoxyacrylate, 3-(dimethylamino)- 2-(thien-2-carbonyl)acrylonitrile [15] (1.12 mmol) to obtain **3**, **4** and **6**. The solvent was AcOH when 3-oxo-2-(thien-3 yl)propionitrile and 3-oxo-2-(thien-2-yl)propionitrile [16] were used to obtain **7** and **8**. The reaction was refluxed for three hours, and after cooling, the addition of water and ice gave a precipitate that was filtered and purified by a suitable solvent

**5-Oxo-4,5-dihydropyrazolo[1,5-a]thieno[2,3-e]pyrimidine-3-carbonitrile (3)**. From **2** and 2-ethoxymethylenmalononitrile. Recrystallized by *i*-propanol, yield 60%, cream crystals, mp > 300 ◦C. TLC: toluene/ethyl acetate/methanol 8:2:1.5 *v*/*v*/*v* (Rf: 0.4); 1H-NMR (400 MHz, DMSO-d6) δ 13.47 (bs, 1H, NH, exch.); 8.28 (m, 2H, H-2 and H-7); 7.67 (d, 1H, H-8, *J =* 5.2 Hz). 13C-NMR (100 MHz, DMSO-d6) δ 163.70, 156.56, 148.45, 144.35, 138.87, 135.86, 129.09, 117.24, 115.45. Anal. C9H4N4OS (C, H, N).

**Ethyl 5-oxo-4,5-dihydropyrazolo[1,5-a]thieno[2,3-e]pyrimidine-3-carboxylate (4).** From **2** and ethyl 2-cyano-3-ethoxyacrylate. Recrystallized by ethanol, yield 58%, cream crystals, mp 215–216 ◦C.  Anal. C11H9N3O3S (C, H, N).

**3-(Thiophene-2-carbonyl)pyrazolo[1,5-a]thieno[2,3-e]pyrimidin-5(4H)-one (6).** From **2** and 3-(dimethylamino)-2-(thien-2-carbonyl)acrylonitrile [15]. Recrystallized by ethanol, yield 95%, cream crystals, mp > 300 ◦C. TLC: toluene/ethyl acetate/methanol 8:2:1.5 *v*/*v*/*v* (Rf: 0.7); 1H-NMR (400 MHz, DMSO-d6) δ 11.59 (bs, 1H, NH, exch.); 8.63 (s, 1H, H-2); 8.37 (d, 1H, H-7, *J =* 5.2 Hz); 8.17 (s, 1H, H-50 ); 8.05 (d, 1H, H-30 , *J =* 4.4 Hz); 7.74 (d, 1H, H-8, *J =* 5.2 Hz); 7.29 (m, 1H, H-40 ). 13C-NMR (100 MHz, DMSO-d6) δ 157.90, 142.76, 142.25, 138.37, 134.86, 133.09, 129.39, 117.34. Anal. C13H7N3O2S2 (C, H, N).

**3-(Thiophene-3-yl)pyrazolo[1,5-a]thieno[2,3-e]pyrimidin-5(4H)-one (7).** From **2** and 3-oxo-2-(thien-3-yl)propionitrile [16]. Recrystallized by ethanol, yield 94%, cream crystals, mp 274–276 ◦C. TLC: toluene/ethyl acetate/methanol 8:2:1.5 *v*/*v*/*v* (Rf: 0.7); 1H-NMR (400 MHz, DMSO-d6) δ 12.06 (bs, 1H, NH, exch.); 8.29 (d, 1H, H-7, *J =* 5.2 Hz); 8.18 (s, 1H, H-2); 7.86 (s, 1H, H-20 ); 7.67 (d, 1H, H-8, *J =* 5.2 Hz); 7.06 (m, 1H, H-40 ); 7.52 (m, 1H, H-50 ). 13C-NMR (100 MHz, DMSO-d6) δ 165.10, 144.35, 141.71, 139.60, 129.95, 128.30, 127.90, 124.75, 117.20. Anal. C12H7N3OS2 (C, H, N).

**3-(Thiophene-2-yl)pyrazolo[1,5-a]thieno[2,3-e]pyrimidin-5(4H)-one (8).** From **2** and 3-oxo-2-(thien-2-yl)propionitrile [16]. Recrystallized by ethanol, yield 89%, green light crystals, mp 248–250 ◦C. TLC: toluene/ethyl acetate/methanol 8:2:1.5 *v*/*v*/*v* (Rf: 0.7); 1H-NMR (400 MHz, DMSO-d6) δ 12.18 (bs, 1H, NH, exch.); 8.30 (d, 1H, H-7, *J =* 5.2 Hz); 8.00 (s, 1H, H-2); 7.67 (d, 1H, H-8, *J =* 5.2 Hz); 7.46 (d, 1H, H-50 , *J =* 4.8 Hz); 7.40 (d, 1H, H-30 , *J =* 2.8 Hz); 7.11 (dd, 1H, H-40 , *J1 =* 4.8 Hz, *J2 =* 4.0 Hz). 13C-NMR (100 MHz, DMSO-d6) δ 157.90, 141.71, 137.66, 128.40, 124.61, 117.19Anal. C12H7N3OS2 (C, H, N).

**5-Oxo-4,5-dihydropyrazolo[1,5-a]thieno[2,3-e]pyrimidine-3-carboxamide (5).** Compound **3** (0.23 mmol, 0.05 g) was suspended in H2SO4 conc., 1 mL and heated at 80 ◦C under stirring. After the starting material disappeared in TLC (toluene/ethyl acetate/methanol 8:2:1.5 *v*/*v*/*v*, as eluent, Rf: 0.2), the reaction was stopped; the addition of ice/water gave a

precipitate which was filtered and purified by recrystallization with ethanol. Yield 92%, white crystals, mp > 300 ◦C. TLC: 1H-NMR (400 MHz, DMSO-d6) δ 10.75 (bs, 1H, NH, exch.); 8.33 (d, 1H, H-7, *J =* 4.8 Hz); 8.28 (s, 1H, H-2); 7.83 (bs, 1H, CONH, exch.); 7.68 (d, 1H, H-8, *J =* 4.8 Hz); 7.32 (bs, 1H, CONH, exch.).13C-NMR (100 MHz, DMSO-d6) δ 165.22, 163.70, 156.56, 150.55, 144.35, 143.91, 135.85, 132.06, 129.00. Anal. C9H6N4O2S (C, H, N).

**General procedure for the synthesis of compounds 9c,d.** Compounds **4**, **6** and **7** (0.6 mmol) were suspended in a mixture of POCl3 (5.5 mL) and PCl5 (0.91 mmol, 0.190 g) and refluxed for three hours. The evaporation to dryness to eliminate the excess of POCl3 gave a residue recuperated with ice/water filtered and purified with a suitable solvent, obtaining **9c**,**d**, starting from **4** and **6**, respectively. From compound **7**, the 5-chloro intermediate **9e** was not isolated but used as such, see below.

**Ethyl 5-chloropyrazolo[1,5-a]thieno[2,3-e]pyrimidine-3-carboxylate (9c).** From **5**. Recrystallized by ethanol, yield 90%, cream crystals, mp > 300 ◦C. TLC: toluene/ethyl acetate/methanol 8:2:1.5 *v*/*v*/*v* (Rf: 0.7); 1H-NMR (400 MHz, DMSO-d6) δ 8.61 (m, 2H, H-2 and H-7); 8.02 (d, 1H, H-8, *J =* 5.2 Hz); 4.29 (q, 2H, CH2, *J =* 7.2 Hz); 1.30 (t, 3H, CH3, *J =* 7.2 Hz). 1H-NMR (400 MHz, CDCl3) δ 8.55 (s, 1H, H-2); 8.08 (d, 1H, H-7, *J =* 5.6 Hz); 7.95 (d, 1H, H-8, *J =* 5.6 Hz); 4.45 (q, 2H, CH2, *J =* 7.2 Hz); 1.44 (t, 3H, CH3, *J =* 7.2 Hz). 13C-NMR (100 MHz, DMSO-d6) δ 161.80, 155.06, 145.85, 145.55, 130.55, 125.66, 126.70, 118.30, 60.95, 14.13. Anal. C11H8N3O2SCl (C, H, N).

**(5-Chloropyrazolo[1,5-a]thieno[2,3-e]pyrimidin-3-yl)(thiophen-2-yl)methanone (9d).** From **6**. Recrystallized by *i*-propanol, yield 95%, cream crystals, mp 186–188 ◦C. TLC: toluene/ethyl acetate/methanol 8:2:1.5 *v*/*v*/*v* (Rf: 0.5); 1H-NMR (400 MHz, DMSO-d6) δ 8.80 (s, 1H, H-2); 8.65 (d, 1H, H-7, *J =* 5.2 Hz); 8.14 (s, 1H, H-50 ); 8.07 (m, 2H, H-8 and H-30 ); 7.30 (m, 1H, H-40 ). 13C-NMR (100 MHz, DMSO-d6) δ 173.70, 155.06, 145.88, 145.53, 144.35, 135.81, 133.72, 130.50, 127.06, 126.74, 125.66, 126.70, 118.30. Anal. C13H6N3OS2Cl (C, H, N).

**General procedure for the synthesis of compounds 10c,d.** Compounds **9c**,**d** (0.4 mmol) was dissolved in a mixture of CH2Cl2/EtOH (7.5 mL/15 mL) and NaBH4 (3.6 mmol, 0.136 g) was added in small portions. The reaction was maintained at room temperature for 40 min, and then the evaporation to dryness of the solvent gave a residue which was recovered with water, filtered and purified with a suitable solvent, obtaining **10c**,**d**, respectively.

**Ethyl 4,5-dihydropyrazolo[1,5-a]thieno[2,3-e]pyrimidine-3-carboxylate (10c).** From **9c**. Recrystallized by water, yield 45%, cream crystals, mp 126–128 ◦C.  Anal. C11H11N3O2S (C, H, N).

**4,5-Dihydropyrazolo[1,5-a]thieno[2,3-e]pyrimidin-3-yl(thiophen-2-yl)methanone (10d).** From **9d**. Recrystallized by ethanol, yield 40%, cream crystals, mp 186–188 ◦C. TLC: toluene/ethyl acetate/methanol 8:2:1.5 *v*/*v*/*v* (Rf: 0.3); 1H-NMR (400 MHz, DMSOd6) δ 8.11 (s, 1H, H-2); 7.97 (s, 1H, H-50 ); 7.91 (d, 1H, H-7, *J =* 4.8 Hz); 7.78 (s, 1H, NH, exch.); 7.61 (d, 1H, H-8 *J =* 4.8 Hz); 7.22 (m, 2H, H-30 and H-40 ); 4.79 (s, 2H, NCH2). 13C-NMR (100 MHz, DMSO-d6) δ 172.98, 158.01, 144.82, 144.35, 137.85, 135.71, 133.57, 129.06, 127.74, 123.66, 119.30, 51.55. Anal. C13H9N3OS2 (C, H, N).

**General procedure for the synthesis of compounds 11c,d.** Compounds **10c,d** (0.4 mmol) was dissolved in toluene (15 mL), and Pd/C as catalyst was added. The reaction was refluxed for 3–5 h and filtered off the catalyst. The evaporation to dryness of the solution yielded a residue recovered with water, filtered and purified with a suitable solvent, obtaining **11c** and **11d**, respectively.

**Ethyl pyrazolo[1,5-a]thieno[2,3-e]pyrimidine-3-carboxylate (11c).** From **10c**. Recrystallized by ethanol yield 50%, white crystals, mp 159–160 ◦C. TLC: toluene/ethyl acetate 8:2 *v*/*v* (Rf: 0.1); 1H-NMR (400 MHz, DMSO-d6) δ 9.42 (s, 1H, H-5); 8.60 (s, 1H, H-2); 8.57 (d, 1H, H-7, *J =* 5.2 Hz); 7.98 (d, 1H, H-8, *J =* 5.2 Hz); 4.31 (q, 2H, CH2, *J =* 6.8 Hz); 1.32 (t, 3H,

CH3, *J =* 6.8 Hz). 13C-NMR (100 MHz, DMSO-d6) δ 162.34, 148.66, 145.99, 145.25, 141.82, 140.02, 123.02, 115.95, 103.38, 60.08, 14.92. Anal. C11H9N3O2S (C, H, N).

**Pyrazolo[1,5-a]thieno[2,3-e]pyrimidin-3-yl(thiophen-2-yl)methanone (11d).** From **10d**. Recrystallized by *i*-propanol, yield 68%, white crystals, mp 73–75 ◦C. TLC: toluene/ ethyl acetate/methanol 8:2:1.5 *v*/*v*/*v* (Rf: 0.2); 1H-NMR (400 MHz, DMSO-d6) δ 9.42 (s, 1H, H-5); 8.74 (s, 1H, H-2); 8.59 (d, 1H, H-7, *J =* 4.8 Hz); 8.23 (d, 1H, H-50 *J =* 2.4 Hz); 8.03 (m, 2H, H-30 and H-8); 7.28 (m, 1H, H-40 ). 13C-NMR (100 MHz, DMSO-d6) δ 173.08, 149.01, 145.82, 144.85, 144.35, 135.81, 133.43, 130.57, 129.06, 126.74, 125.60, 119.10. Anal. C13H7N3OS2 (C, H, N).

**3-(Thiophen-3-yl)pyrazolo[1,5-a]thieno[2,3-e]pyrimidine (11e).** Compound **7**, 3-(thiophene-3-yl)pyrazolo[1,5-a]thieno[2,3-e]pyrimidin-5(4H)-one (0.6 mmol), was suspended in a mixture of POCl3 (5.5 mL) and PCl5 (0.91 mmol, 0.190 g) and refluxed for three hours. The evaporation to dryness to eliminate the excess of POCl3 gave the corresponding 5-chloro derivative (**9e**), not isolated but used as such for the next reduction step through a CTH (catalytic transfer hydrogenation). Thus, this intermediate suspended in EtOH (20 mL) was added of ammonium formate (4.08 mmol, 0.275 g) and 10% Pd/C as catalyst. The reaction was maintained at reflux temperature for several hours, during which it was possible to evidence, by TLC, the formation of the 4,5-dihydro derivative in a mixture with the final 4,5-dehydro compound **11e**. When the reaction finished, the catalyst was filtered off, the solution evaporated to dryness, and the residue recovered with water. Recrystallized by ethanol 80%, yield 90%, cream crystals, mp 157–160 ◦C. TLC: toluene/ethyl acetate 8:2 *v*/*v* (Rf: 0.5); 1H-NMR (400 MHz, DMSO-d6) δ 9.24 (s, 1H, H-5); 8.65 (s, 1H, H-2); 8.45 (d, 1H, H-7, *J =* 5.2 Hz); 8.05 (s, 1H, H-20 ); 7.95 (d, 1H, H-8, *J =* 5.2 Hz); 7.86 (d, 1H, H-50 , *J =* 4.8 Hz); 7.65 (m, 1H, H-40 ). 13C-NMR (100 MHz, DMSO-d6) δ 145.56, 141.21, 138.47, 126.77, 119.61, 115.88. Anal. C12H7N3S2 (C, H, N).

**General procedure for the synthesis of compounds 12a,c–f and 13b,e,f.** A solution of DMF abs. (5 mL), compounds **3**–**8** (0.40 mmol) and K2CO3 anhydrous (0.80 mmol) was maintained for 15 min at room temperature. After this time, methyl iodide (0.80 mmol) was added and enhanced temperature to 80 ◦C. After one hour and monitoring the reaction by TLC, adding water gave a precipitate, filtered and purified by a suitable solvent. In the case of compounds **3**, **4** and **6**, only 4-N-CH3 derivatives were formed (**12a**,**c**,**d**). From compound **4**, only the 5-methoxyderivative **13b** was recovered, while if the reaction is performed at reflux temperature, only the 4-methyl derivative **12b** was obtained. From **7** and **8**, a mixture of two products was recovered at the end of alkylation. The chromatographic separation permits isolating the 4-NCH3 (**12e,f**) and the 5-OCH3 derivatives (**13e,f**).

**4-Methyl-5-oxo-4,5-dihydropyrazolo[1,5-a]thieno[2,3-e]pyrimidine-3-carbonitrile (12a).** From **3**. Recrystallized by ethanol, yield 89%, cream crystals, mp 224–226 ◦C. TLC: toluene/ethyl acetate/methanol 8:2:1 *v*/*v*/*v* (Rf: 0.6); 1H-NMR (400 MHz, DMSO-d6) δ 8.44 (s, 1H, H-2); 8.36 (d, 1H, H-7, *J =* 4.0 Hz); 7.71 29/03/2023 (d, 1H, H-8, *J =* 4.0 Hz); 3.73 (s, 3H, NCH3). 13C-NMR (100 MHz, DMSO-d6) δ 163.76, 156.51, 148.40, 144.37, 144.30, 135.86, 129.07, 115.41, 106.58, 36.50. Anal. C10H6N4OS (C, H, N).

**4-Methyl-5-oxo-4,5-dihydropyrazolo[1,5-a]thieno[2,3-e]pyrimidine-3-carboxamide (12b).** From **5** at reflux temperature. Recrystallized by ethanol, yield 93%, cream crystals, mp > 300 ◦C. TLC: dichlorometane/methanol 9:1 *v*/*v* (Rf: 0.3); 1H-NMR (400 MHz, DMSO-d6) δ 8.28 (d, 1H, H-7, *J =* 4.8 Hz); 8.11 (s, 1H, H-2); 7.86 (bs, 1H, CONH, exch.); 7.65 (d, 1H, H-8, *J =* 4.8 Hz); 7.32 (bs, 1H, CONH, exch.); 3.73 (s, 3H, NCH3). 13C-NMR (100 MHz, DMSO-d6) δ 165.30, 163.76, 156.51, 150.40, 145.67, 144.31, 135.86, 132.05, 129.08, 36.69. Anal. C10H8N4O2S (C, H, N). The treatment of compound **12a** with sulfuric acid at 60 ◦C and the subsequent addition of ice/water gave the precipitate, **12b** recovered by filtration.

**Ethyl 4-methyl-5-oxo-4,5-dihydropyrazolo[1,5-a]thieno[2,3-e]pyrimidine-3-carboxylate (12c).** From **4**. Recrystallized by ethanol, yield 75%, cream crystals, mp 173–174 ◦C.  Anal. C12H11N3O3S (C, H, N).

**3-(Thiophene-2-carbonyl)-4-methylpyrazolo[1,5-a]thieno[2,3-e]pyrimidin-5(4H)-one (12d).** From **6**. Recrystallized by ethanol, yield 95%, cream crystals, mp 183–186 ◦C. TLC: toluene/ethyl acetate/methanol 8:2:1.5 *v*/*v*/*v* (Rf: 0.5); 1H-NMR (400 MHz, DMSO-d6) δ 8.37 (d, 1H, H-7, *J =* 5.2 Hz); 8.33 (s, 1H, H-2); 8.10 (d, 1H, H-50 *J =* 4.8 Hz); 7.82 (d, 1H, H-30 , *J =* 3.6 Hz); 7.74 (d, 1H, H-8, *J =* 5.2 Hz); 7.30 (m, 1H, H-40 ); 3.56 (s, 3H NCH3). 13C-NMR (100 MHz, DMSO-d6) δ 179.37, 156.18, 145.24, 144.91, 141.71, 138.40, 135.92, 135.62, 129.12, 117.39, 106.50, 33.07. Anal. C14H9N3O2S2 (C, H, N).

**3-(Thiophene-3-yl)-4-methylpyrazolo[1,5-a]thieno[2,3-e]pyrimidin-5(4H)-one (12e).** From **7**, after chromatographic separation, second eluting band (toluene/ethyl acetate/ methanol 8:2:1.5 *v*/*v*/*v* as eluent, Rf: 0.5), yield 35%, cream crystals, mp 109–111 ◦C. 1H-NMR (400 MHz, DMSO-d6) δ 8.29 (d, 1H, H-7, *J =* 4.8 Hz); 7.85 (s, 1H, H-2); 7.68 (d, 1H, H-8, *J =* 4.8 Hz); 7.63–7.59 (m, 2H, H-20 and H-40 ); 7.25 (m, 1H, H-50 ); 3.40 (s, 3H, NCH3). 1H-NMR (400 MHz, CDCl3) δ 7.83 (d, 1H, H-7, *J =* 5.2 Hz); 7.71 (s, 1H, H-2); 7.65 (d, 1H, H-8, *J =* 5.2 Hz); 7.40 (m, 1H, H-20 ); 7.26 (m, 1H, H-40 ); 7.11 (d, 1H, H-50 , *J =* 4.4 Hz); 3.41 (s, 3H, NCH3). 13C-NMR (100 MHz, DMSO-d6) δ 157.94, 144.97, 140.00, 138.10, 137.90, 130.86, 125.99, 114.34, 114.23, 106.12, 20.54. Anal. C13H9N3OS2 (C, H, N).

**3-(Thiophene-2-yl)-4-methylpyrazolo[1,5-a]thieno[2,3-e]pyrimidin-5(4H)-one (12f).** From **8** after chromatographic separation, second eluting band (toluene/ethyl acetate/ methanol 8:2:1.5 *v*/*v*/*v* as eluent, Rf: 0.5), yield 36%, yellow light crystals, mp 140–141 ◦C. 1H-NMR (400 MHz, DMSO-d6) δ 8.30 (d, 1H, H-7, *J =* 5.2 Hz); 7.90 (s, 1H, H-2); 7.68 (d, 1H, H-8, *J =* 5.2 Hz); 7.62 (d, 1H, H-50 , *J =* 5.2 Hz); 7.18 (m, 1H, H-30 ); 7.13 (dd, 1H, H-40 , *J1 =* 4.8 Hz, *J2 =* 3.6 Hz); 3.31 (s, 3H, NCH3). 13C-NMR (100 MHz, DMSO-d6) δ 165.46, 154.88, 145.71, 137.76, 135.14, 131.79, 129.06, 117.48, 114.01, 56.89. Anal. C13H9N3OS2 (C, H, N).

**5-Methoxypyrazolo[1,5-a]thieno[2,3-e]pyrimidine-3-carboxamide (13b).** From **5** at room temperature. Recrystallized by ethanol, yield 75%, cream crystals, mp 269–270 ◦C. TLC: dichlorometane/methanol 8:2 *v*/*v* (Rf: 0.6); 1H-NMR (400 MHz, DMSO-d6) δ 8.42 (d, 1H, H-7, *J =* 4.8 Hz); 8.36 (s, 1H, H-2); 7.90 (d, 1H, H-8, *J =* 4.8 Hz); 7.49 (bs, 1H, CONH, exch.); 7.34 (bs, 1H, CONH, exch.); 4.22 (s, 3H, OCH3). 13C-NMR (100 MHz, DMSO-d6) δ 164.40, 162.26, 145.87, 145.50, 130.56, 125.68, 114.45, 107.32, 53.79. Anal. C10H8N4O2S (C, H, N).

**5-Methoxy-3-(thiophene-3-yl)pyrazolo[1,5-a]thieno[2,3-e]pyrimidine (13e).** From **7**, after chromatographic separation, first eluting band (toluene/ethyl acetate/methanol 8:2:1.5 *v*/*v*/*v* as eluent, Rf: 0.8), yield 35%, cream crystals, mp 127–130 ◦C. 1H-NMR (400 MHz, DMSO-d6) δ 8.49 (s, 1H, H-2); 8.38 (d, 1H, H-7, *J =* 5.2 Hz); 7.98 (m, 1H, H-20 ); 7.85 (d, 1H, H-8, *J =* 5.2 Hz); 7.81 (d, 1H, H-50 , *J =* 4.8 Hz); 7.62 (m, 1H, H-40 ); 4.20 (s, 3H, OCH3). 13C-NMR (100 MHz, DMSO-d6) δ 162.20, 144.86, 139.60, 133.47, 128.37, 128.20, 126.71, 125.60, 124.75, 114.56, 107.90, 53.75. Anal. C13H9N3OS2 (C, H, N).

**5-Methoxy-3-(thiophene-2-yl)pyrazolo[1,5-a]thieno[2,3-e]pyrimidine (13f).** From **8** after chromatographic separation, first eluting band (toluene/ethyl acetate/methanol 8:2:1.5 *v*/*v*/*v* as eluent, Rf: 0.8), yield 24%, yellow light crystals, mp 118–120 ◦C. 1H-NMR (400 MHz, DMSO-d6) δ 8.43 (s, 1H, H-2); 8.39 (d, 1H, H-7, *J =* 5.2 Hz); 7.85 (d, 1H, H-8, *J =* 5.2 Hz); 7.57 (m, 1H, H-50 ); 7.42 (d, 1H, H-30 , *J =* 4.8 Hz); 7.11 (m, 1H, H-40 ); 4.20 (s, 3H, OCH3). 13C-NMR (100 MHz, DMSO-d6) δ 162.20, 144.86, 138.20, 133.65, 130.57, 128.67, 128.00, 126.74, 125.60, 114.56, 107.90, 53.50. Anal. C13H9N3OS2 (C, H, N).

**5-Oxo-4,5-dihydropyrazolo[1,5-a]thieno[2,3-e]pyrimidine-3-carboxylic acid (14).** To a suspension of **4** (0.40 mmol) in NaOH 10% solution (10 mL), was added 0.5 mL of methoxyethanol to favour the solubilization. The reaction was refluxed for 1.30 h, then ice/water and HCl 6N until pH 1. The precipitate formed was filtered, washed with water and purified by a suitable solvent. Recrystallized by ethanol, yield 78%, cream crystals, mp 282–284 ◦C.  Anal. C9H5N3O3S (C, H, N).

**5-Oxo-4,5-dihydropyrazolo[1,5-a]thieno[2,3-e]pyrimidine (15).** The acid **14** (0.50 mmol) was suspended in 10 mL of HCl conc. and maintained at reflux temperature for 5 h. Adding ice/water gave a residue filtered and purified by recrystallization with ethanol. Recrystallized by ethanol, yield 65%, cream crystals, mp 290–291 ◦C. TLC: toluene/ethyl acetate/methanol 8:2:1.5 *v*/*v*/*v* (Rf: 0.7); 1H-NMR (400 MHz, DMSO-d6) δ 12.30 (bs, 1H, NH, exch.); 8.26 (s, 1H, H-7); 7.74 (s, 1H, H-2); 7.64 (s, 1H, H-8); 5.93 (s, 1H, H-3). 13C-NMR (100 MHz, DMSO-d6) δ 164.76, 156.26, 148.45, 144.37, 135.85, 133.21, 129.09, 114.15. Anal. C8H5N3OS (C, H, N).

**4-Methyl-5-oxo-4,5-dihydropyrazolo[1,5-a]thieno[2,3-e]pyrimidine (16).** The ester **12c** (0.50 mmol) was suspended in 10 mL of NaOH 10% solution and maintained at 80 ◦C until the starting material disappeared. The extraction with ethyl acetate and the next usual work up gave a residue which was filtered and purified by recrystallization with ethanol. Yield 71%, cream crystals, mp 157–159 ◦C.  Anal. C9H7N3OS (C, H, N).

**General procedure for the synthesis of compounds 17 and 19.** A suspension of **12b** or **13b** (0.32 mmol) in H2SO4 conc. (8 mL) was stirred until a solution was obtained and then cooled at 0 ◦C; to this solution, sodium nitrite (0.22g, 3.2 mmol/5 mL of water) was slowly added and the green suspension was maintained for 3 h at 0 ◦C. The suspension was made alkaline and extracted with ethyl acetate. After the standard work-up, the evaporation of the organic layer gave a green residue that was purified and characterized.

**4-Methyl-3-nitrosopyrazolo[1,5-a]thieno[2,3-e]pyrimidin-5(4H)-one (17).** From **12b**. Recrystallized by ethanol, yield 50%, green crystals, mp 240–242 ◦C. TLC: toluene/ethyl acetate/acetic acid 8:2:1 *v*/*v*/*v* (Rf: 0.9); 1H-NMR (400 MHz, DMSO-d6) δ 8.44 (d, 1H, H-7, *J =* 5.6 Hz); 7.74 (d, 1H, H-8, *J =* 5.6 Hz); 7.72 (s, 1H, H-2); 4.21 (s, 3H, NCH3). 13C-NMR (100 MHz, DMSO-d6) δ 163.70, 156.59, 148.49, 144.30, 134.99, 133.25, 129.01, 104.05, 36.99. ESI-HRMS (m/z) calculated for [M+H]+ ion species C9H6N4O2S = 235,0295; found: 235,0284. Anal. C9H6N4O2S (C, H, N).

**5-Methoxy-3-nitrosopyrazolo[1,5-a]thieno[2,3-e]pyrimidine (19).** From **13b**. Recrystallized by ethanol, yield 48%, green crystals, mp 218–220 ◦C. TLC: toluene/ethyl acetate/acetic acid 8:2:1 *v*/*v*/*v* (Rf: 0.9); 1H-NMR (400 MHz, DMSO-d6) δ 8.86 (s, 1H, H-2); 8.54 (d, 1H, H-7, *J =* 5.6 Hz); 7.94 (d, 1H, H-8, *J =* 5.6 Hz); 4.23 (s, 3H, OCH3). 13C-NMR (100 MHz, DMSO-d6) δ 163.65, 156.60, 148.49, 144.30, 134.99, 133.20, 128.99, 104.10, 55.60. ESI-HRMS (m/z) calculated for [M+H]+ ion species C9H6N4O2S = 235,0294; found: 235,0284. Anal. C9H6N4O2S (C, H, N).

**5-Methoxy-3-nitrosopyrazolo[1,5-a]thieno[2,3-e]pyrimidin-3-carboxylic acid (18).** From **13b**, after acidification of the alkaline solution. The carboxylic acid was recrystallized by ethanol, yield 30%, white crystals, mp 218–220 ◦C.  Anal. C10H7N3O3S (C, H, N).

**4-Methyl-5-oxo-4,5-dihydropyrazolo[1,5-a]thieno[2,3-e]pyrimidin-3-carbaldehyde (20).** A suspension of **16** (150 mg, 0.73 mmol) in glacial acetic acid (6 mL) was added of hexamethylenetetramine (HTMA, 0.36 g) and maintained at reflux temperature for 10 h. After disappearing the starting material, evaluated by TLC (CHX/EtOAc 1:5, *v*/*v* as eluent,

Rf: 0.8), the addition of ice gave a precipitate that was recovered by filtration. Yield 85%, white crystals, mp 222–224 ◦C. 1H-NMR (400 MHz, CDCl3) δ 10.02 (s, 1H, CHO); 8.27 (s, 1H, H-2); 7.91 (d, 1H, H-7, *J =* 5.2 Hz); 7.88 (d, 1H, H-8, *J =* 5.2 Hz); 4.03 (s, 3H, OCH3). 13C-NMR (100 MHz, DMSO-d6) δ 170.35, 163.20, 156.25, 145.80, 144.59, 135.84, 133.50, 129.50, 107.15, 37.50. Anal. C10H7N3O2S (C, H, N).

**4-Methyl-5-oxo-4,5-dihydropyrazolo[1,5-a]thieno[2,3-e]pyrimidin-3-carboxylic acid (21).** The aldehyde **20** (150 mg, 0.73 mmol) was suspended in acetone and water (5 mL/5 mL) and a solution of potassium permanganate (1.1 mmol) in water was added after the suspension was made alkaline with sodium hydroxide 10%. The reaction was heated for 8 h, and after cooling and elimination of the manganese dioxide by filtration, the alkaline aqueous phase was extracted to eliminate the starting material not reacting. The next acidification of the aqueous phase gave the corresponding carboxylic acid that was recovered by extraction. Yield 60%, white crystals, mp 220–223 ◦C. TLC: CHX/EtOAc 1:5, *v*/*v* (Rf: 0.2); 1H-NMR (400 MHz, DMSO-d6) δ 12.68 (bs, 1H, OH, exch.); 8.32 (d, 1H, H-7, *J =* 4.0 Hz); 8.21 (s, 1H, H-2); 7.67 (d, 1H, H-8, *J =* 4.0 Hz); 3.87 (s, 3H, NCH3). 13C-NMR (100 MHz, DMSO-d6) δ 169.10, 162.40, 145.85, 145.59, 130.50, 126.55, 125.80, 115.50, 107.10, 37.70. Anal. C10H7N3O3S (C, H, N).

**General procedure for the synthesis of compounds 22a,b.** The carboxylic acid **21** (0.5 mmol) was transformed into the corresponding 3-carbonyl chloride by reaction with excess SOCl2 in anhydrous conditions. After the standard work-up, the residue was suspended in dichloromethane (6 mL), and the suitable alcohol (excess 0.15 mL) was added; TLC monitored the reaction until the disappearance of the starting material. Then, the final solution was evaporated to dryness, and the residue recuperated with isopropyl ether and recrystallized.

*tert***-Butyl 4-methyl-5-oxo-4,5-dihydropyrazolo[1,5-a]thieno[2,3-e]pyrimidine-3-carboxylate (22a).** From **21** and *tert*-butanol, white crystals recrystallized by 80% ethanol, yield 25%; mp > 300 ◦C. TLC: toluene/ethyl acetate/methanol 8:2:1.5 *v*/*v*/*v* (Rf: 0.5); 1H-NMR (400 MHz, CDCl3) δ 8.10 (s, 1H, H-2); 7.85 (d, 1H, H-7, *J =* 4.8 Hz); 7.63 (d, 1H, H-8, *J =* 4.8 Hz); 4.01 (s, 3H, NCH3); 1.50 (s, 9H, (CH3)3). 13C-NMR (100 MHz, DMSO-d6) δ 161.78, 156.10, 145.24, 142.00, 141.69, 138.12, 117.25, 116.95, 98.60, 33.57, 14.59. Anal. C14H15N3O3S (C, H, N).

**Thiophen-2-yl-methyl 4-methyl-5-oxo-4,5-dihydropyrazolo[1,5-a]thieno[2,3-e]pyrimidine-3-carboxylate (22b).** From **21** and 2-thiophenmethanol, white crystals recrystallized by 80% ethanol, yield 30%; mp 190–195 ◦C. TLC: toluene/ethyl acetate/methanol 8:2:1.5 *v*/*v*/*v* (Rf: 0.6); 1H-NMR (400 MHz, CDCl3) δ 8.19 (s, 1H, H-2); 7.85 (d, 1H, H-7, *J =* 5.2 Hz); 7.63 (d, 1H, H-8, *J =* 5.2 Hz); 7.35 (d, 1H, H-5 Thiophene, *J =* 5.2 Hz); 7.18 (s, 1H, H-3 Thiophene); 7.03 (d, 1H, H-4 Thiophene, *J1 =* 4.8 Hz); 5.47 (s, 2H, OCH2); 4.03 (s, 3H, NCH3). 13C-NMR (100 MHz, CDCl3) δ 145.60, 135.92, 128.25, 126.89, 117.02, 60.81, 33.87. Anal. C15H11N3O3S2 (C, H, N).

**Pyrazolo[1,5-a]thieno[2,3-e]pyrimidine-3-carboxylic acid (23).** Compound **11c** (0.40 mmol) was treated with 10% NaOH solution and maintained at 80 ◦C until the starting material disappeared in TLC (toluene/ethyl acetate/acetic acid 8:2:2 *v*/*v*/*v* as eluent). However, the reaction gave many compounds, and the fast-eluted band's fluorescent spot was recovered by purification with a chromatography column (toluene/ethyl acetate/acetic acid 8:2:2 *v*/*v*/*v* as eluent, Rf: 0.5).  Anal. C9H5N3O2S (C, H, N).

**Pyrazolo[1,5-a]thieno[2,3-e]pyrimidine (24).** Compound **11c** (0.40 mmol) were treated with HCl/CH3COOH solution and maintained at 70 ◦C until the starting material disappeared in TLC (toluene/ethyl acetate/acetic acid 8:2:2 *v*/*v*/*v* as eluent, Rf: 0.6). The reaction from 11c, monitored by TLC evidenced the formation of 3-carboxylic acid (23) that quickly evolved in the decarboxylated compound 24. The final solution was extracted with ethyl acetate, and after the normal work-up, the residue was purified by recrystallization

from ethanol.  Anal. C8H5N3S (C, H, N).

**3-Methylpyrazolo[1,5-a]thieno[2,3-e]pyrimidine (25).** Compound **14** (0.34 mmol) was suspended in THF anhydrous (5 mL) and 1.50 mmol of LiAlH4 was added, using a 1M solution of LiAlH4 in THF. After 1 h, the starting material disappeared and adding ice/water quenched the reaction. The extraction with ethyl acetate gave the intermediate 4,5-dihydro derivative not isolated but identified since not fluorescent in TLC, which was treated with toluene and 10% Pd/C at reflux temperature until the dehydrogenation was complete. The final suspension was filtered, toluene was evaporated and the residue was recrystallized by ethanol; yield 35%, white crystals, mp 127–130 ◦C. TLC: toluene/ethyl acetate/methanol 8:2:1.5 *v*/*v*/*v* (Rf: 0.7); 1H-NMR (400 MHz, DMSO-d6) δ 9.06 (s, 1H, H-5); 8.39 (d, 1H, H-7, *J =* 5.2 Hz); 8.04 (s, 1H, H-2); 7.87 (d, 1H, H-8, *J =* 5.2 Hz); 2.33 (s, 1H, CH3). 13C-NMR (100 MHz, DMSO-d6) δ 149.90, 132.74, 132.90, 130.56, 126.70, 125.65, 119.10, 115.63, 14.30. Anal. C9H7N3S (C, H, N).

#### *3.2. Molecular Docking and Molecular Dynamic Simulation*

The structure of the binding site was obtained from the Human α1β2γ2-GABAA receptor subtype in complex with GABA and flumazenil, conformation B (PDB ID 6D6T) [21], considering all the amino acids within a distance of about 2 nm from the structure of the Flumazenil. The ligands were placed at the binding site through AUTODOCK 4.2 [20]. The molecular dynamics simulations of ligand binding-site complexes were performed on a minimum number of conformations (maximum 2) such as to cover at least 90% of the poses found by AUTODOCK. A 60 ns MD simulation were performed for all complexes using GROMACS v5.1 program, and it was conducted in vacuum [22]. The DS ViewerPro 6.0 program [19] was used to build the initial conformations of ligands. The partial atomic charge of the ligand structures was calculated with CHIMERA [23] using AM1-BCC method, and the topology was created with ACPYPE [24] based on the routine Antechamber [25]. The OPLS-AA/L all-atom force field [26] parameters were applied to all the structures. To remove bad contacts, the energy minimization was performed using the steepest descent algorithm until convergence is achieved or for 50,000 maximum steps. The next equilibration of the system was conducted in two phases:

The initial velocity was randomly assigned taken from Maxwell–Boltzman distribution at 300 K and computed with a time step of 2 fs, and the coordinates were recorded every 0.6 ns for MD simulation of 60 ns. During the simulated trajectory, 100 conformations were collected. The 'Proximity Frequencies' (PFs) [1,2] with which the 100 conformations of each binding-site ligand complex intercepts two or more amino acid during the dynamic simulation have calculated. The 'Proximity Frequency' (PF) is the frequency with which the ligand was, during the molecular dynamic simulation, at a distance of less than 0.25 nm from an amino acid of the binding-site and also, simultaneously, from 2, 3 and 4 amino acids of the binding site.

#### **4. Conclusions**

The synthesis and the study on the reactivity of the new scaffold 5-oxo-4,5-dihydropyrazolo [1,5-a]thieno [2,3-e]pyrimidine was performed with the aim of isosteric replacement

of the benzene ring in our 5-oxo-4,5-dihydro pyrazolo[1,5-a]quinazoline (PQ), already identified as α1β2γ2-GABAAR ligands.

The introduction in this new scaffold of those fragments responsible for the activity in our PQ [6,12] gave six final designed compounds (**12c**–**f** and **22a,b**), which were, in turn, studied in the 'Proximity Frequency' model [13] to predict their potential profile on the α1β2γ2-GABAAR.

The results indicate for all six products an agonist profile, highlighting the suitability of the nucleus and confirming the importance of the carbonyl group of the ester moiety to engage strong hydrogen bond interaction with the receptor protein. In particular, the esters derivatives (**12c** and **22b**) are able, through the carbonyl group, to interact with the amino acid residue γThr142 with a strong hydrogen bond (2.07 Å). Additionally, **22a** has an ester group in position 3, but the steric hindrance of the *t*-butyloxycarbonyl fragment makes the hydrogen bond interaction of the carbonyl group with the γThr142 residue less probable. Compounds missing the ester group (**12d**–**f**) show a weak interaction with γThr142 in agreement with the low prediction percentage.

In conclusion, the synthesis of the 5-oxo-4,5-dihydropyrazolo [1,5-a]thieno [2,3-e]pyrimidine scaffold allowed us to identify a new chemical class of compounds potentially active on GABAA receptor subtype, and the in silico results should be completed and confirmed with biological assays.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/molecules28073054/s1, Chemistry. Materials and methods; 1H-NMR spectra of all compounds; 13C-NMR spectra of some representative compounds (**6**, **8**, **10c**, **11c**, **11e**, **12c**, **12d**, **12e**, **12f**, **22a**, **22b, 23**); Elemental analysis (Table S1); General chemical structure with atoms numeration.

**Author Contributions:** Conceptualization, G.G. and M.P.G.; methodology, G.G.; software, G.G. and F.M.; validation, G.G. and F.M.; formal analysis, L.C.; investigation, L.C. and G.G.; resources, L.C.; data curation, L.C., G.G. and F.M.; writing—original draft preparation, G.G.; writing—review and editing, G.G., M.P.G., L.C. and C.V.; visualization, L.C.; supervision, G.G. and M.P.G.; project administration, M.P.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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**1. Introduction**

The supply of energy from renewable sources (i.e., hydro, solar, photovoltaic, wind, geothermal, biomass, and others) is a fundamental element of any country's energy strategy and is driven by the concern for the local and global environment, as well as for energy security and sustainable development. Changes in recent years have resulted in an increase in the ways in which renewable energy is used and shared in total electricity generation. This change has been facilitated by environmental investments, market trends, appropriate policies (support for technology development), changes in legal regulations as well as business opportunities.

Renewable energy helps secure national resources, mitigate pollution and climate change, and provides cost-effective services [1].

Each type of renewable energy is characterised by its uniqueness of the physical specificity and, therefore, differences in technological solutions.

When describing renewable energy sources (RES), the authors focus primarily on electricity: ways of obtaining it and meeting the country's energy needs.

Hydropower storage is a well-established and commercially acceptable technology for industrial-scale electricity storage and has been used since the 1890s. Hydropower is not only a renewable and sustainable source of energy, but its flexibility and storage capacity

**Citation:** Huterski, R.; Huterska, A.; Zdunek-Rosa, E.; Voss, G. Evaluation of the Level of Electricity Generation from Renewable Energy Sources in European Union Countries. *Energies* **2021**, *14*, 8150. https://doi.org/ 10.3390/en14238150

Academic Editor: Tomonobu Senjyu

Received: 22 October 2021 Accepted: 2 December 2021 Published: 5 December 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

also enable it to improve grid stability and support the deployment of other intermittent renewable energy sources such as wind and solar [2].

Wind energy is one of the cheapest renewable energy sources. The cost of producing it using wind in regions with good wind resources is comparable with producing electricity from fossil fuels. In most cases, the cost is lower or almost the same [3].

Photovoltaics, in turn, is a relatively young technology within the renewables group. Therefore, it is rather difficult to assess its environmental impacts and related costs due to uncertainties in assessing the causal impact of photovoltaic technologies on the environment and human health at each stage of the technology's life cycle [4].

Biomass, or more precisely its availability for energy purposes, is a result of the adopted forestry and agricultural model and the rate of introduction of more efficient energy crop plantation [5]. In Poland, biofuels are primarily used to produce thermal energy.

The aim of this paper is to present the use of renewable sources in electricity production in Poland and compare it with other European Union countries in selected years.

In order to achieve the aim, one of the methods of multidimensional comparative analysis—the taxonomic development measure (which allows for the ranking of countries according to the level of RES use for electricity production) was applied along with other panel models.

The paper sets out the following research hypothesis:

H: The level of RES use for electricity production in European Union countries depends on the level of development of these countries, measured by GDP per capita.


*5.2. Assignments*

As NH stretching vibrations can be difficult to assign, deuteriation is often a good tool. As most NH stretching frequencies are found around 3000 cm−1 the ND stretching frequencies will be around 2100 cm−1, which is in a region with few other resonances. However, the NH/ND ratio is not fixed. This was originally studied by Novak [109] and later supplemented by Sobczyk et al. [110] primarily using OH stretching frequencies. Novak included mostly intermolecular hydrogen bonds whereas Sobczyk et al. added intramolecular hydrogen bonds. The broad trend is a decrease of the isotope ratio as the NH stretching frequency decreases. However, as seen in Figure 27 based on data by Chiara et al. [89,103] for nitro-substituted enaminones and nitro-substituted enamino esters and supplemented with data on enamino esters the picture is not so clear cut at all. This is also seen in the review by Sobczyk et al. [73] but is to some extent masked by the inclusion of a correlation line.

**Figure 27.** Plot of the νNH/νND ratio vs. νND. Series 1 β-amino-α-nitro-α,β-unsturated ketones, hydrogen bonding to the keto group, Series 2 Hydrogen bonding to the nitro group from [89] and Series 3 enaminoesters from [89,103].


**3.4 Characterization of gongo oil and biodiesel**

#### *3.4.1 Fourier transform infrared spectroscopy*

The elucidation of the chemical behavior of the products of the homogeneous acid esterification and homogeneous alkaline transesterification reactions of gong oil was investigated using Fourier transform infrared absorption spectroscopy (FTIR).

**Figure 5.** *Physical aspect of gong and its crude and esterified oils [20].*

#### **Figure 6.**

*Products obtained from the biodiesel processing steps. Caption: a: gongo sludge; b: gongo oil in natura; c: esterified gongo oil and d: gongo biodiesel [20].*

The IR spectra of the esterified gong oil (**Figure 7a**) and biodiesel (**Figure 7b**) samples are shown in **Figure 5**. They showed similar band positions, intensity and wave number. [22, 26, 27] Characterized vegetable oils and biodiesels produced from different vegetable oils and obtained FTIR results similar to the results revealed in this work.

In the region of the functional groups that comprises the range 4000–1650 cm1 few bands were observed. There, three sets of well-defined bands were revealed, two bands in the range 2853–2925 cm1 referring to the CH2 stretching of alkanes and one at 1744 cm1 indicative of the C=O (carbonyl) stretching that characterizes the double bond region.

The region between 1650 and 500 cm1 is called the fingerprint region. In this region more bands were found in relation to the region of the functional groups, however, the bands obtained between 1171 and 1172 cm1 allusive to the vibrations of the C-O bond, indicative of the stretching of the ester grouping, stand out. Between 722 and 723 cm1 bands referring to asymmetric CH deformation, characteristic of long hydrocarbon chains, were revealed. Another relevant factor observed in **Figure 7a** and **b** was the absence of absorption of broad bands in the region between 2500 and 3300 cm1 , showing that the gongo oil and biodiesel had low moisture content. This fact was confirmed in the course of the immediate chemical analyses shown in **Table 2**.

**Figure 7.** *FTIR spectrum. (a) Gong oil; (b) Biodiesel.*

*Processing of Gong Oil (*Pachymerus nucleorum*) to Obtain Biodiesel by Methyl Route DOI: http://dx.doi.org/10.5772/intechopen.97721*

#### *3.4.2 Fat profile of biodiesel obtained by GC-FID*

In Brazil, the National Agency of Petroleum, Natural Gas and Fuel (ANP) is the body responsible for overseeing the quality parameters of biodiesel through Resolution No. 45, dated August 13, 2014. It regulates the characteristics of biofuels. The quality parameters are used to determine whether a given product is fit for consumption and not harmful to the population and the environment.

The ideal biodiesel for Resolution 045/2014-ANP should contain in its chemical constitution 100% of fatty acid methyl esters (FAME), however, in view of the low conversion of monoacylglycerols (MAG), diacylglycerols (DAG) and triacylglycerols (TAG) and competition between the secondary reactions throughout the transesterification process it becomes difficult to separate the esters formed and the reaction impurities. Consequently, at the end of the transesterification reaction, a mixture containing biodiesel and impurities is formed. Hence the importance of characterization of biodiesel and the feedstock that gave rise to it.

Among the specific quality parameters for biodiesel determined by the ANP is the ester content. The ANP has established an ester content of at least 96.5%. A low ester content directly influences the physical–chemical properties of biodiesel. Therefore, to obtain a high quality biofuel, the transesterification reaction must be complete and the purification process after the reaction must be effective. Thus, the content of remaining contaminants (FFA, methanol traces, water, etc.) should be low, so that the ester content meets the ANP specifications.

The methyl biodiesel produced from the esterified gong oil was characterized by gas chromatography with flame ionization detector (GC-FID) technique for ester conversion. The chromatographic profile in terms of fatty acid methyl esters (FAME) of the biodiesel is presented in **Figure 8**.

The FAME centesimal composition of the biodiesel is shown in **Table 3**. Therein it was observed that the majority constituent was dodecanoic acid methyl ester (methyl laurate, C 12:0) with 36.85%; followed by octadecanoic acid methyl ester (methyl stearate, C18:0) with 25.47%, tetradecanoic acid methyl ester (methyl myristate, C14:0) with 23.37% and hexadocanoic acid methyl ester (methyl palmitate, C16:0) with 14.31%.

The technique of gas chromatography with flame ionization detector (GC-FID) is suggested by standards EN 14103 and NBR 15764 for determining the content of fatty acid methyl esters (FAME) from the transesterification reaction of vegetable oils and, to confirm the results are necessary to use analytical standards for each FAME.

**Figure 8.** *Chromatographic profile of methyl esters contained in gong biodiesel. Source: the author himself.*

#### **Table 3.**

*Composition of fatty acid methyl esters from gong biodiesel [20].*

According to the ANP (2014), the determination of the ester content can be done following the EN 14103 methodology. The ester content based on this standard is performed by internal standardization with methyl heptadecanoate (C17:0) and only considers the esters of chain from C14:0 to C24:1 in the calculation of the ester content. Since in the characterization of biodiesel produced from gong oil it was revealed that the majority fatty acid methyl ester was C12:0 (methyl laurate), the EN 14103 technique did not allow determining the conversion of esterified gong oil into biodiesel (methyl esters). Due to financial and technical difficulties that permeate our institution it was not possible to use other means to do the conversion. On the other hand, comparing the FTIR results for the esterified gongo oil and biodiesel with the results revealed for the fat profile of gongo biodiesel through the GC-FID technique is notable the presence of bands in the infrared absorption region allusive to the axial deformation of carbonyl (C=O) indicating the presence of fatty acid methyl esters, which were confirmed in the GC-FID tests, and the absence of bands between 2500 and 3300 cm1 indicating low percentage of water in biodiesel studied. Such occurrences allow us to predict the results of some quality control parameters of biodiesel. For example, the values shown in **Table 3** (methyl ester composition) for gongo biodiesel lead us to infer as to the degree of FAME saturation that the gongo biodiesel will have:

*Processing of Gong Oil (*Pachymerus nucleorum*) to Obtain Biodiesel by Methyl Route DOI: http://dx.doi.org/10.5772/intechopen.97721*

2500 to 3300 cm1 allows us to predict that gong biodiesel contains low moisture content.


**1.1 Rationale for the research**

No coastal lagoon and its immediate catchment area remain static over any timescale (short or long). The natural balance of the coastal lagoon can be seen as the sustainability of the natural ecosystem between the sea and the coastal lagoon. However, no matter how carefully managed the natural balance of the lagoon and its ecosystem, it will be susceptible to change. As a result of the general morphological features, lagoons are naturally very sensitive to dynamic balance in all aspects [16].

The rapidly induced changes in the morphological nature of coastal lagoons due to an incessant increase in population around the coast are prominently brought into display around the Lagos Lagoon, Nigeria (West Africa), the study area in this research. This is the major force that propels this investigation of the morphological and hydrodynamic changes in the Lagos Lagoon. Lagos' population is currently about 17 million, up from 2 to 3 million in the 1970s. Despite this pressure, research to date on the lagoon only identifies ecological studies [17–19], lagoon sensitivity and pollution studies [17, 20, 21], fishery and plankton sustainability [22, 23] and

*Morphodynamics in a Tropical Shallow Lagoon: Observation and Inferences of Change DOI: http://dx.doi.org/10.5772/intechopen.90189*

partial pressures on the lagoon ecosystem habitat. All these, although, are part of the outcome of the impact created by the growing population (about 17 million) of the city of Lagos Nigeria around the Lagos Lagoon. However, despite the high pressure on the lagoon and its ecosystem, no specific studies have been undertaken to address the lagoon's morphological changes.

The physical variability is not considered in geological or other long-term timescales but in the short-term. This means that changes in the lagoon over a long timescale of about hundred years to thousands of years are not the concern of this study but there is a focus on changes within the range of about 20–50 years due to human activities since post-industrial expansion in Lagos. Consequent upon this, a general research question is generated on which the research aim is focused.