**3. Free membranes**

72 Recent Advances in Plasticizers

There are two substantial forces which influence the polymer adhesion: the strength of the interfacial bonds between the polymeric film and the surface of the solid, and the internal stresses within the film coating. The interfacial bonding between the tablet surface and polymer coating is made primarily with hydrogen bonds, to a lesser extent with dipoledipole and dipole-induced dipole interactions. The addition of plasticizing agents to coating formulations generally decreases the internal stress in the film by decreasing both the elastic modulus and the glass transition temperature of the film coating (Johnson Hathaway,

The measured force of adhesion, therefore, would be expected to increase with increased plasticization of the polymer. The contradictory results may be due to the interaction of the plasticizing agent with the polar groups of the polymer within the film structure (Felton

The adhesive property of Eudragit films is effected with organic esters used as plasticizers (triacetin, diethyl phthalate, dibutyl phthalate and tributyl citrate were tested). The molecular weight and solubility parameters of the plasticizer seemed to play an important role in changing the adhesive strength of the Eudragit films. The adhesion of Eudragit films is markedly increased when the concentration of the plasticizer is greater than 25%. Tributyl citrate may be the best choice of the plasticizer for the Eudragit film, particularly for the

The molecular weight and solubility parameters of the plasticizer seemed to play an important role in changing the adhesive strength of the Eudragit films (Lina et al., 2000).

The water soluble plasticizers, triethyl citrate and polyethylene glycol 6000, lower the Tg of the films to a greater extent than the hydrophobic plasticizers, tributyl citrate and dibutyl sebacate, and the coatings with the hydrophilic plasticizers exhibit stronger adhesion

The study of the influence of plasticizers on the adhesion of the acrylic polymer to either hydrophilic or hydrophobic tablet compacts revealed that increasing tablet hydrophobicity decreased adhesion of both cellulosic and acrylic polymers (Felton McGinity, 1996, 1997;

Polymer-plasticizer incompatibility, plasticizer migration out of polymer (and particularly leaching of plasticizer) as well sticking and aging of the film coated dosage influence not only the mechanical properties, but also drug release (Amighi Moes, 1996; Arwidsson et al., 1991; Bodmeier Paeratakul, 1992; Skultety Sims, 1987). For example, dibutyl sebacate unlike triethyl citrate was found to remain within the polymeric coating upon exposure to the dissolution media and thus mechanical properties of coatings during drug release are maintained. Leaching of triethyl citrate out of the film resulting in decreased mechanical

A blend of polymers can be used in the coating of solid dosage forms, and then the type of plasticizer must be chosen responsibly to ensure good quality of the coating. If the plasticizer has different affinities to the individual polymers forming the blend, the

1991; Rowe, 1981, 1992).

McGinity, 1999).

Eudragit E film.

Rowe, 1977).

(Entwistle Rowe, 1979; Felton McGinity, 1997).

**2.2 Factors negatively influencing the coating**

resistances facilitates crack formation.

A membrane can be in the form of a free film containing the active ingredient, or it can form the outer wall of the body which is its reservoir together with other excipients. The membrane has a defined thickness; it can be porous or compact, it can contain, in addition to the active ingredient, various excipients including plasticizers. In the case of dosage forms such as buccal and sublingual films, capsules intended for oral, rectal or vaginal administration, and inserts placed into cavities, their membranes and membrane systems possess the function of (1) the primary coating increasing stability and enabling administration, (2) separation from and protection against the influence of the biological environment after administration, or (3) controlled release.

Ethyl cellulose films of standard thickness were prepared by slow evaporation of the solvent from chloroform solutions of a 48.8% ethoxylated cellulose derivative and three plasticizers: classic dibutyl sebacate, vitamin D3 and vitamin E. The individual plasticizers were used in nine different concentrations up to 80 %. It has been demonstrated that primarily αtocopherol (vitamin E) in concentrations of 40 to 50 % is an advantageous and effective plasticizer of ethyl cellulose from the standpoint of toxicity, ultimate strength, toughness, and Young modulus (Kangarlou et al., 2008). Ethyl cellulose of the identical parameters was employed in the preparation of films using the same method, diethyl sebacate in a 40% concentration being used as the plasticizer. Chloroform was evaporated using the standard very slow evaporation procedure. A suitable velocity of stress action was found for the most conclusive stress-strain deformation diagrams (Sogol & Ismaeil, 2011).

Thin films made of poly(lactide-co-glycolide) were prepared from a dichloromethane solution with additions of 15%, 25%, or 50% polyethylene glycol 8 000 and 35 000, yielding potential implants of 40 µm in thickness containing the antitumor drug paclitaxel. Raman spectrometry was used to demonstrate partial separation of the plasticizer (Steele et al., 2011).

Soft gelatin capsules were formed by a wall of varying thickness containing about 40 to 45 % of gelatin, which is plasticized with water 20 to 30 % and glycerol, or sorbitol 30 to 35 % (Marques et al., 2009). The plasticizer increased the flexibility of gelatin, with increasing concentration to 45 % it decreased the sensitivity to humidity, decreased the tendency to recrystallization, decreased tensile strength and the elastic modulus, increases elongation at break (Bergo & Sobral, 2007).

Protein films possess good mechanical properties, but they are very sensitive to atmospheric humidity. Their hygroscopic character can be decreased by adding a hydrophobic plasticizer. Esters derived from citric acid such as triethyl citrate, tributyl citrate, acetyl triethyl citrate and acetyl tributyl citrate were tested in various concentrations (Andreuccetti et al., 2009). Their limited miscibility with an aqueous solution of gelatin was solved by

Pharmaceutical Applications of Plasticized Polymers 75

Matrix polymeric systems are heterogeneous systems composed of a polymer, plasticizers, other additives and the active ingredient, and they can be porous. They include primarily oral preparations with prolonged or retarded release of the active ingredient, or implants

Extrusion is a process of conversion of raw materials to form a product of a uniform shape and density by forcing it through a nozzle under controlled conditions (Breitenbach, 2002). Plasticizers facilitate the manufacture and improve the parameters of preparations such as porosity, tortuosity, mechanical resistance, diffusivity, or compatibility of the components. Hot-melt extrusion, in which it is possible to process materials with higher Tg values, is

Due to their little volatility, the plasticizers which are in the solid state in room temperature are advantageous. For plasticization of polyacrylate polymers, e.g. matrices from Eudragit RS PO, citric acid, in particular its monohydrate, turned out to be useful. Tensile strength and the elastic modulus were decreased, whereas elongation was increased. The plasticizing effect of citric acid achieved the plateau stage at its 25% concentration in polyacrylate, i.e. when achieving the solubility value of (Schilling et al., 2007). The continous process of extrusion and subsequent division of the extruded body into tablets was used in the formulation of enterosolvent preparations from the polyacrylate material Eudragit L100-55. Mixtures of two plasticizers, triethyl citrate and citric acid, were employed. Triethyl citrate markedly decreased the Tg value and thus decreased the temperatures in the individual zones of the extruder and the pressure during extrusion. The optimal concentration of citric acid was 17 %. 5-aminosalicylic acid was employed as the model active ingredient (Andrews

Subcutaneous implants of a diameter of 1.5 mm and length of 18 mm weighing 40 mg containing thermolabile recombinant proteins and peptides were prepared from plasticized poly(lactide-co-glycolide) as the carrier and ethanol as the plasticizer (Mauriac & Marion, 2006). First, a 10% solution of the polymer in ethanol was prepared, followed by drying to achieve an ethanol concentration of 20 %. The polymer plasticized in such a way was mixed with the unplasticized one and the mixture was extruded at 75 °C. The following step was grinding of the extrudate at -5 to -10 °C with an ethanol content of 8 %. The active ingredient and other additives were added to the powder and the mixture was extruded at a temperature up to 70 °C. Another example of extremely volatile plasticizer is supercritical carbon dioxide (Lakshman, 2008). This compoud is added to facilitate processing of

Starch is the biomaterial which has excellent parameters from the standpoint of availability, price and biocompatibility. In the presence of water and suitable polar hydrophilic compatible plasticizers during extrusions at temperatures over 100 °C amylopectin flows and at the same time native starch granules partially melt. The material can have two markedly different values of Tg. Interactions between starch and plasticizers with strong hydrogen bonds (Pushpadass et al., 2008) were demonstrated in it. The use of glycerol in a concentration of 25 % and secondary plasticizers such as stearic acid, sucrose or urea in markedly lower concentrations are advantageous. It thus yields a flexible material suitable

**5. Matrix polymeric systems**

advantageous.

et al., 2008).

materials by extrusion method.

as a biodegradable drug carrier.

produced by extrusion or hot moulding or compression.

emulsification of plasticizers with soya lecithin. The tensile stress of the material was decreased with increasing concentrations of the plasticizer, permeability of water vapours was moderately increased.

Biodegradable films were prepared from a protein isolated from sunflower oil with additions of various polyalcohols, such as glycerol, ethylene glycol, propylene glycol, polyethylene glycols. The mixture was subjected to thermo-moulding at 150 °C using 150 MPa. Tensile strength of films was 6.3 to 9.6 MPa and elongation at break was 23 to 140 %. Permeability of water vapours was low; films were resistant against aqueous environment only in a limited degree, as migration of the plasticizer took place (Orliac et al., 2003). The plasticizers used were evaluated as suitable for protein films.


**Stochastic Control for Jump Diffusions** \*

Jingtao Shi

16 Will-be-set-by-IN-TECH

[13] M.H.A. Davis and A.R. Norman. Portfolio selection with transaction costs. *Mathematics*

[14] M.H.A. Davis, V.G. Panas, and T. Zariphopoulou. European option pricing with transaction costs. *SIAM Journal on Control and Optimization*, 31:470–493, 1993. [15] S.D. Hodges and A. Neuberger. Optimal replication of contingent claims under

[16] T.L. Lai, T.W. Lim, and K. Ross. A new approach to solving multi-dimensional singular stochastic control problems with applications to investment theories and queueing

[17] T.L. Lai, Y.-C. Yao, and F. AitSahlia. Corrected random wall approximations to free boundary problems in optimal stopping. *Advances in Applied Probability*, 39:753–775,

[18] T.L. Lai and T.W. Lim. Option hedging theory under transaction costs. *Journal of*

[19] T.L. Lai, T.W. Lim, and W. Zhou. Backward induction algorithms for singular stochastic control problems associated with transaction costs. Working paper, Stanford University,

[20] V.I. Zakamouline. European option pricing and hedging with both fixed and proportional transaction costs. *Journal of Economic Dynamics and Control*, 30:1–25, 2006. [21] X. Guo and P. Tomecek. Connections between singular control and optimal switching.

*of Operations Research*, 15:676–713, 1990.

2007.

2012.

transaction costs. *Review of Futures Markets*, 8:222–239, 1989.

networks. Working paper, Stanford University, 2012.

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Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/45719


**2. Material and methods**

368 Lipid Metabolism

characterized from wheat [45].

relatively to new findings.

above.

by molecular and biochemical approaches.

and precursors of oxidized lipids are released.

properties, LTPs are thought to be involved in membrane biogenesis and regulation of intracellular FA pools [38]. Many roles were suggested for LTPs: involvement in cutin formation, embryogenesis, symbiosis and adaptation of plants to various environmental conditions [39]. Among them, defensive role of LTPs has been proposed. Indeed, LTPs have been naturally classified as members of pathogenesis-related (PR) proteins belonging to the group PR-14 [40]. Some members of this family have the ability to inhibit the growth of fungal pathogens in barley and maize [41], in sunflower against *Fusarium solani* [42], in transgenic rice against *Magnaporthe grisea*, *Rhizoctonia solani* and *Xanthonomas oryzae* [43]. In transgenic wheat expressing *Ace-AMP,* the corresponding encoded LTP showed enhanced antifungal activity against *Bgt* [44]. *Ltp3F1,* a novel gene encoding an antifungal protein against *Alternaria sp*., *Curcularia lunata*, *Bipolaris oryzae* and *Sarocladium oryzae* was

In this review, we will discuss further and extend the study conducted by Renard-Merlier *et al*. [46], where a global investigation of total FA content in relation to treatment with four inducers of resistance and to powdery mildew infection was undertaken. Previous studies established that lipid metabolism is altered by Milsana®, Iodus 40®, HSA, SA and trehalose [8,10]; therefore, our work aimed to characterize their impact at the total FA level. During a time course experiment, content (quantitative analysis) and percentage (qualitative analysis) of FAs were compared in treated plants and in controls, as well as in non-inoculated (ni) plants and *Bgt*-challenged plants (i). Previous results will be considered and discussed

Moreover, the effect of one resistance inducer, namely SA, on lipid metabolism is evaluated

Phospholipids being the major membrane components, we investigated PC, PE, DAG and PA content variation in wheat leaves infiltrated with salicylic acid (SA). SA can modulate the content variation of these compounds, reservoirs from which biologically active lipids

At the transcriptional level, a PLC-encoding gene expression was investigated in an attempt to assign any participation of this pathway in the phospholipids equilibrium described

We also investigated free FAs and PLFAs content variations in SA-infiltrated wheat leaves; this pool of lipids is quite interesting since it ensures several functions, from being an energy source to acting as cellular messengers; the latter being highly related to resistance induction in plants. The lipoxygenase response to SA-infiltration, at the molecular and enzymatic level, was also evaluated; this enzyme activity is important for oxylipins biosynthesis in

An LTP-encoding gene expression was also monitored, taking into account the possible antifungal activity of LTPs as well as their ability to bind and transport membrane lipids,

plants, because of its position upstream the cascade of enzymatic lipid peroxydation.

thus participating in lipid-mediated signaling mechanisms.

### **2.1. Treatments application**

Wheat (*Triticum aestivum*) cultivar Orvantis was used throughout the experiments. It was provided by Benoit C.C. (Orgerus, France). This cultivar is fully susceptible to the MPEBgt1 powdery mildew isolate. First leaf of ten-day-old wheat plantlets was infiltrated with salicylic acid (1g/L) solution using a hypodermic syringe without needle. Infiltrated area was delineated with a marker pen. Control plantlets were infiltrated with distillated water.

Ten-day-old wheat seedlings were treated with solutions of Iodus 40® (1g/L), HSA (1g/L), Milsana® (0.3% v/v) and trehalose (15g/L) as described by Renard-Merlier *et al*. [46]. Treatments consisted in "up-to-run-off" sprayings. Two days after inducer treatments, seedlings to be inoculated were sprayed with conidia of *Bgt* suspended in Fluorinert FC43 at a concentration of 5.106 spores.mL-1.


*3.4. Adverse Effects of Oral Iron Therapy*

While oral iron therapy as the first-line treatment for IDA is indisputable, considerable side effects are reported. A systematic review and meta-analysis of 43 RCTs by Tolkien et al. [9] confirmed that ferrous sulphate was 2.32 times (95% CI: 1.74–3.08, *p* < 0.0001) and 3.05 times (95% CI: 2.07–4.48, *p* < 0.0001) more likely to cause gastrointestinal side effects than placebo and intravenous iron therapy, respectively. Notably, meta-regression from this study did not find a significant relationship between the odds ratios of gastrointestinal side effects and dose. The common gastrointestinal side effects reported in the RCTs are constipation, nausea, diarrhoea, abdominal pain, vomiting, heartburn, dark stools, and flatulence [9].

There are also other negative impacts of oral iron therapy on the gastrointestinal tract besides symptoms of discomfort. Free iron, Fe2, is a divalent metal cation that can react with hydrogen peroxide in cells to produce hydroxyl radicals, which induce oxidative stress responses in cells. These radicals are cytotoxic to endothelial and smooth muscle cells and have deleterious impacts on health [110,125,126]. Hence, excess iron from oral iron supplements can lead to lipid peroxidation, which causes ferroptosis, mitochondrial damage, and endoplasmic reticulum dysfunction, leading to the destruction of the intestinal epithelial cells, affecting the intestinal mechanical barrier's integrity [126]. Such are the possible underlying mechanisms of frequent gastrointestinal side effects of oral iron therapy. Moreover, anaemic patients taking oral iron supplements also demonstrated increased systemic oxidative stress as measured by lipid peroxide, protein carbonyl, conjugated dienes, lipid hydroperoxide and oxidised glutathione levels accompanied by a reduced total antioxidant level [127]. Hence, it is advisable to increase dietary antioxidant intake while taking oral iron supplements.

Excessive unabsorbed iron may cause gut dysbiosis, as it can modify gut microbiota composition, promoting the growth of pathogenic bacteria at the expense of the healthy ones [128–130]. Specifically, increased luminal iron concentration appeared to favour the growth of *Escherichia coli*, *Salmonella*, and *Bacteroides* species of pathogenic bacteria while lowering the abundance of probiotics such as *Lactobacillus* and *Bifidobacterium* species [128]. The disruption of gut microbiota equilibrium can also lead to inflammation and chronic conditions such as inflammatory bowel disease and metabolic dysfunction [128,131], which, in turn, can also hamper iron absorption and reduce the effectiveness of iron supplementation [132,133].

Overuse of iron supplements also carries considerable risks. Intake of iron above 60 mg/kg body weight can result in severe toxicity leading to gastrointestinal tract injury and impaired cellular metabolism in the heart, liver, and central nervous system, which can be fatal without immediate medical attention [134,135]. Moreover, prolonged ingestion of iron supplements for years has been reported to cause iron overload, where the body stores too much iron, a condition that can cause severe organ damage [136].


**4. Machine Learning Methods**

### *4.1. Adaptive Neuro-Fuzzy Inference System*

Jang et al. [55] introduced the fuzzy adaptive system of adaptive neurology, called ANFIS, as an improved ML method and a data-driven modeling approach to evaluate the behavior of complex dynamic systems [56,57]. ANFIS aims to systematically generate unknown fuzzy rules from a given set of input and output data. ANFIS creates a functional map that approximates the internal system parameter estimation method [58–60]. Fuzzy systems are rule-based systems developed from a set of language rules. These systems can represent any system with good accuracy and are, therefore, considered to be universal approximators. Thus, ANFIS is the most popular neuro-fuzzy hybrid network used for the modeling of complex systems. The ANFIS model's main strength is that it is a universal approximator with the ability to request interpretable "if–then" rules [61]. In ANFIS, a Sugeno-type fuzzy system was used to construct the five-layer network.


*3.1. Datasets for Mentor and Student*

#### 3.1.1. Dataset for Mentor Training

CIFAR-10 [45] is an established dataset used for object recognition. It consists of 60,000 (32 × 32) RGB images from 10 classes, with 6000 images per class. There are 50,000 training images and 10,000 test images in the official data. The mentor is a pretrained model on the CIFAR-10 dataset. We use the test set (from this dataset) to measure the success rate of our mentor and student models. Note that the training set of the CIFAR-10 dataset is never used in the training process by the student (to conform to our assumption that the student has no access to the data used by the mentor for training), and the test subset, as mentioned above, is used for validation only (without training).

#### 3.1.2. Dataset for Mimicking Process

ImageNet [46] is a dataset containing complex, real-world size images. In particular, ImageNet\_ILSVRC2012 contains more than 1.2 million (256 × 256) RGB images from 1000 categories. We use this dataset (without the labels, i.e., an unlabeled dataset) for the mimicking process. Each image is down-sampled (32 × 32) and fed into the mentor model, and the prediction of the mentor model is recorded (for later mimicking by the student). Note that any large unlabeled image dataset could be used instead, and we used this common large dataset for convenience only.

#### *3.2. Composite Data Generation*

Our goal is to create a diverse dataset that will allow observing the predictions of the mentor on many possible inputs. By doing so, we would gain insights into the way the mentor behaves for different samples. That is, the more adequate the input space sample is, the better the performance of the mimicking process becomes. The entire available unlabeled data, which is the down-sampled ImageNet, is contained in an array *dataArr*. For each training example to be generated, we randomly choose two indexes *i*1, *i*2, such that 0 <= *i*1, *i*2 < *N*, where is *N* equal to the number of samples we create and use for training the student model. In our composite method, we choose *N* = 1,000,000, so the amount of generated training samples created in each epoch is 1, 000, 000. Next, we randomly choose a ratio *p*. Once we have *i*1, *i*2, and *p*, we generate a composite sample, which is created by combining two existing images in the dataset. The ratio *p* determines the relative influence of the two random images on the generated sample:

$$x\_{-
\text{gen}} = p\*
\cdot
\operatorname{dataArr}[i\_1] + (1-p)\*
\cdot
\operatorname{dataArr}[i\_2]$$

where the label of *x*\_*gen* is a "one-hot" vector; i.e., the index containing the '1' (corresponding to the maximal softmax value) represents the label predicted by the mentor. The dataset is generated for every epoch; hence, our composite dataset changes continuously, and it is dynamic. We gain the predictions of a mentor model on new images during the entire training process (with less overfitting). Note that even though in our data-generating mechanism, we create a composite of two random images (with a random mixture between them), it is possible to create composite images of *N* images where *N* > 2 as well.

Algorithm 1 provides the complete composite data-generation method, which is run at the beginning of each epoch. Figure 1 is an illustration of composite data samples created by Algorithm 1.

#### *3.3. Student Model Architecture*

The mentor neural network (which we intend to mimic) is an already trained model that reaches 90.48% test accuracy on the CIFAR-10 test set. Our goal in choosing an architecture for the student is to be generic, such that it would perform well, regardless of the mentor we try to mimic. Thus, with small adaptations to the input and output size, we created a modification of the VGG-16 architecture [47] for the student model. In our model, we use two dense layers of size 512 each and another dense layer of size 10 for the softmax output (while in the original VGG-16 architecture, there are two dense layers of size 4096 and another dense layer of size 1000 for the softmax layer). Table 1 presents the architecture of our student model.

#### **Algorithm 1** Composite Data Generation.

(**a**) 75% cat 25% dog (**b**) 70% horse 30% kangaroo (**c**) 30% horse 70% ship

(**d**) 40% ship 60% parrot (**e**) 50% tiger 50% dog (**f**) 20% car 80% elephant

**Figure 1.** Illustration of images created using our composite data-generation method. The images and their relative mixture are random. Using this method during each epoch we create an entirely new dataset, with random data not seen before by the model.


Toxic Gas Detectors Based on a MnSb2O6 Oxide Chemical Sensor

*José Trinidad Guillen Bonilla, Héctor Guillen Bonilla, Maricela Jiménez Rodríguez, Alex Guillen Bonilla, Verónica María Rodríguez Betancourtt, Víctor Manuel Rangel Cobian, María Eugenia Sánchez Morales and Antonio Casillas Zamora*

### **Abstract**

We synthesized the semiconductor oxide MnSb2O6 through a wet chemical process assisted by low-power microwave radiation. A gas-sensitive sensor was elaborated from the MnSb2O6 powders obtained by calcination at 600°C. The sensor was electrically characterized in static CO and C3H8 atmospheres by measuring direct current signals at 100, 200, and 300°C. The toxic gases' concentrations were 1, 5, 50, 100, 200, 300, 400, and 500 ppm of C3H8; and 1, 5, 50, 100, 200, and 300 ppm of CO. From the MnSb2O6's electrical resistance results, a sensor's operational point and a low-cost analog circuit were proposed, obtaining two new prototypes: one for detecting C3H8 and a second one for detecting CO. We selected the response at 200°C and 5 ppm for both cases. Notably, this concentration (5 ppm) is selectable with a calibration resistance, generating an alarm signal of ≈11*:*3 *V* at a supply voltage of 120 V AC. The toxic gas detectors showed excellent functionality. The resistive sensor showed high sensitivity and good electrical response, while the analog circuit presented a rapid response. Due to the operating temperature employed (200°C), these devices could find practical applications, for example, exothermic generators and heaters.

**Keywords:** semiconductor oxide MnSb2O6, sensor was electrically characterized, good electrical response, resistive sensor, CO and C3H8 atmospheres, toxic gas detectors, analog circuit

### **1. Introduction**

The detection of polluting gases is of great importance worldwide because an alarming increase in respiratory, ocular, skin, and lung diseases has been detected [1, 2]. There is no adequate control on the emission of toxic gases into the atmosphere by many large industries and transportation systems [2]. Therefore, a huge effort in research and synthesis of new materials capable of detecting different levels of toxic

gases has been undertaken. The current aim is the development of new inexpensive, reliable, sensitive, efficient, and thermally stable detectors in toxic atmospheres (like in CO, CO2, C3H8, NO2, etc.) [1–4]. The most widely studied semiconductors for their application as gas detectors are the p and n-type binary oxides (like SnO2, ZnO, NiO, CuO, In2O3, WO3, Fe2O3, etc.) [4–6], as well as the ternary perovskite-type oxides (like YCoO3 and LaFeO3) [7, 8], and the oxides with spinel-type structure (like CoFe2O4 and ZnFe2O4) [9, 10]. However, recent reports informed that other transition-metal ternary semiconductors with a trirutile-type crystalline structure (AB2O6, where A and B possess divalent and pentavalent bonds, respectively) are alternative materials for toxic-gas sensors [11, 12]. Among these materials are the CoSb2O6 [11], the NiSb2O6 [12], the MgSb2O6 [13], and the ZnSb2O6 [14]. These oxides have been studied in CO2, CO, C3H8, LPG, H2S, and NO2 atmospheres [11–17].

The MnSb2O6 oxide (manganese antimonite, where in this case the Mn ion substitutes the A) can show a hexagonal crystalline structure with the spatial group P321 [18] or a trigonal chiral-type crystalline structure [19]. The MnSb2O6 possesses good catalytic activity in toxic atmospheres (such as CO) at temperatures above 100°C [20, 21]. In Ref. [20], it was reported that the MnSb2O6 showed a maximum sensitivity magnitude (S) of �8.98 at 300 ppm of CO and 300°C. At 500 ppm of C3H8 (propane), the response was �0.439 at the same temperature. In Ref. [21], the MnSb2O6 was synthesized by the colloidal method and it showed increased sensitivity as a function of the increase in the operating temperature and the gases' concentrations. The response in 500 ppm of propane had a maximum of �165.66 at 300°C. In 300 ppm of CO, the maximum response was �14.929, also at 300°C. The good MnSb2O6's electrical response was attributed to the nanometric particle's microstructure (porosity, morphology, and size) obtained during the synthesis process.

We studied the MnSb2O6 regarding its application as a gas detector and found little information on the application of the oxide in devices for detecting C3H8 and CO, which form atmospheres with a high risk of explosion (propane) or intoxication (CO). In this work, we designed two novel gas detectors: one of them detects C3H8 concentrations and the second one detects CO concentrations. Both are analog detectors and were designed based on the oxide's electrical response. They are capable to detect concentrations of 5 ppm, which can be modified through a calibrating resistance. They generate an alarm signal at � 11*:*3 V, use a supply voltage of 120 V in alternate current (AC), and an operating voltage of Vcc ¼ 12 V. They possess rapid response, and are easy to build, install, and repair. Both are ideal for industrial applications where combustion is involved.