**13. References**

**Security Mechanisms**

26 Wireless Sensor Networks / Book 1

[13] IEEE 802.15.4 WPAN Task Group (2006) IEEE Standard for Local and Metropolitan Area Networks - Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low Rate Wireless Personal Area Networks (LR-WPANs). [14] IEEE 802.16 Network Management Task Group (2004) IEEE Standard for Local and Metropolitan Area Networks - Part 16: Air Interface for Fixed Broadband Wireless

[15] Wan Q, Wang C (2011) Design of 3.1-10.6 GHz Ultra-Wideband CMOS Low Noise Amplifier with Current Reuse Technique, AEU-International Journal of Electronics and

[16] Sadler B.M (2005) Fundamentals of Energy-Constrained Sensor Network Systems, IEEE

[17] Mergen G, Zhao Q, Tong L (2006) Sensor Networks with Mobile Access: Energy and Capacity Considerations, IEEE Transactions on Communications. j. 54(11):2033-2044. [18] Baliga J, Ayre R, Sorin W.V, Hinton K, Tucker R.S (2008) Energy Consumption in Access Networks, Conference on Optical Fiber Communication/National Fiber Optic

Access Systems.

Communications. j. 65(12): 1006-1011.

Engineers Conference, 2008. j. 2:1-3.

Aerospace and Electronic Systems Magazine. j. 20(8):17-35.


**2. Case Presentation**

A 57-year-old Caucasian female was referred to the breast care unit of the Policlinic Hospital of the University of Bari Aldo Moro for a small mass in her right breast. The patient had been suffering from persistent and severe dry eyes and moderate dry mouth for several years. Three years earlier, a biopsy of the minor salivary glands, along with the presence of anti-Sjögren's syndrome A and B (anti-SSA/SSB) antibodies, lead to the diagnosis of primary SS, in the absence of other autoimmune diseases, as detected by clinical examination and serological tests. The revision of the original histopathological preparations confirmed the diagnosis of lymphocytic sialadenitis with a focus score >1/4 mm2, grade 4 according to Chisholm and Mason (Figure 1A,B).

**Figure 1.** Low power magnification of minor salivary gland biopsy ((**A**): hematoxylin and eosin, original magnification 100×); at higher magnification, small lymphocyte and plasma cell aggregates (i.e., more than one lymphocytic focus) associated with mildly collagenized stroma are detectable ((**B**): hematoxylin and eosin, original magnification 200×).

The patient reported that, immediately after the diagnosis of SS, she received methotrexate and prednisone for a few months; currently, she still is only undergoing antihypertensive and hydroxychloroquine therapy and shows no relevant signs of SS (e.g., parotid enlargement or eye/mouth dryness). Routine laboratory tests were within normal limits. As to the breast lesion, a painless swelling of small size was detected on palpation; conventional mammography showed a small radiolucency with regular and well-defined margins of the lower inner quadrant (Figure 2), while ultrasound examination highlighted a round opacity with regular edges (Figure 3A–D).

**Figure 2.** Digital cranio-caudal mammographic view: the lesion appears as a round opacity with regular edges located in the lower inner quadrant of the right breast (arrow).

**Figure 3.** Automated breast ultrasound scan on coronal (**A**,**C**) and axial planes (**B**,**D**). The lesion appears as an oval hypoechoic nodule with regular edges, mimicking duct ectasia (arrows).

Regardless of the benign appearance on both imaging investigations, a US-guided core needle biopsy of the lesion was performed; unexpectedly, the subsequent histopathological examination showed diffuse proliferation of small to medium-sized lymphoid cells, with slightly hyperchromatic nuclei, without plasmacytic differentiation, accompanied by stromal sclerosis and residual atrophic ducts.

Complimentary immunohistochemical investigations were performed to confirm the purportedly monoclonal nature of the lymphoid proliferation, highlighting the vast majority of infiltrating lymphocytes being of the B phenotype, and distinctly immunoreactive for CD20, CD79a and bcl2, while no immunoreactivity for CD3, CD5, CD10, CD23, cyclin D1, bcl6 and LEF1 was detected in lymphoid neoplastic cells. Less than 10% tumor cells displayed nuclear anti-Ki 67 (MIB 1) positivity. MALT gene rearrangement, involving the MALT1 locus at chromosome 18q21, using a MALT FISH Split Signal DNA Probe, could not be demonstrated. All such findings pointed at the diagnosis of primary extranodal MZL of MALT (MALT lymphoma) (Figure 4A–D). No lymphadenopathy, spleen enlargement, bone marrow involvement or other localizations of the disease were detected. Peripheral blood tests revealed the persistence of anti-SS A and B (anti-SSA/SSB) antibodies, cryoglobulins and low levels of C4 and C3.

**Figure 4.** Primary breast marginal zone NHL is characterized by diffuse proliferation of small to medium-sized lymphoid cells and accompanied by stromal sclerosis and residual atrophic ducts ((**A**): hematoxylin and eosin, original magnification 40×). The neoplastic lymphocytes are strongly immunoreactive for bcl2 ((**B**): original magnification 200×) and CD20 ((**C**): original magnification 200×). The immunohistochemical stain for Ki67 shows a very low proliferative index, pointing at an "indolent" lymphoma ((**D**): original magnification 100×).

#### **3. Discussion**

Primary breast lymphomas (PBL) represent approximately 1% of all NHLs, 1.7–2.2% of all extra-nodal NHLs and 0.04–0.5% of all malignancies of the breast. [18–21] Around 9% of all primary breast lymphomas are MZLs of MALT and usually manifest an indolent clinical behavior [21–23].

It is generally accepted that chronic inflammatory diseases (such as SS, Hashimoto thyroiditis, *Borrelia Burgdorferi* dermatitis, Helicobacter pylori-associated chronic gastritis and HCV, HHV8, EBV and HTLV-1 infections) may play a role in lymphoma development, resulting in the transition from polyclonal B cell activation into monoclonal expansion of B-lymphocytes. The transition into B-cell NHL only affects a minority of the aforementioned patients harboring chronic inflammatory diseases and has been associated with increased overall disease mortality rate [5–9,11,24].

Bizjak et al. in 2015 [25] extensively reviewed the role of inflammation related to breast silicone implants and other silicone prostheses (such as cardiac pacemakers and defibrillators, cardiac valvular and testicular/penile prostheses) in lymphoma development. They assumed that chronic inflammation in predisposed individuals could evolve into severe scarring of peri-implant tissues and to persistent activation of the local/systemic immune system. Such a pathogenetic mechanism was demonstrated for a distinct type of NHL, namely breast implant-associated anaplastic large T-cell lymphoma (BI-ALCL).

Patients with autoimmune diseases represent 5% of NHL patients, and NHL surely is the most severe complication occurring during SS patients' follow-up; [5–10,24] nevertheless, the pathogenetic mechanism for such association has not been clarified [1–6,9–11,24,26].

As widely discussed by Vasaitis et al. in a recent population-based study [26], data available in the literature on NHL in SS patients are not at all uniform, and even basic data on gender preferences and overall lymphoma prevalence are not well established yet [26]. In view of the mostly indolent clinical behavior of NHL in SS patients, and in consideration of a median follow-up time that rarely exceeds 10 years in most reported studies, an accurate definition of epidemiologic data, including prevalence, can be hardly assessed. In addition, while several studies have reported on the occurrence of NHL in the breast, these were not focused on possible associations between breast NHL and SS [15,26–29].

In a recent update on prognostic markers of lymphoma development in SS patients, Retamozo et al. (2019) [30] stated that such patients show a seven-fold increased risk of lymphoma in comparison with systemic lupus erythematosus patients, four-fold with rheumatoid arthritis patients and globally >10-fold in comparison with the general population [5,30].

The same authors listed, point-to-point evaluated and discussed the different prognostic/predictive factors outlined in previously published studies, such as epidemiologic markers (age and sex), clinical markers (parotid enlargement, dry mouth and eyes, arthralgias, splenomegaly, lymphadenopathy, skin purpura/vasculitis), laboratory markers (systemic activity, hypergamma/raised IgG, CD4/CD8 ≤ 0.8, raised beta2-microglobulin, raised B-cell activating factors, anemia, leukopenia, lymphopenia, neutropenia, ANA, rheumatoid factor, Anti-Ro/La, low C4, C3 and CH 50 levels, cryoglobulins, mIgs) and histologic markers (focus score and ectopic primary or secondary follicle). They concluded that, although the association of more risk factors surely increases the risk of NHL, such prediction still remains imperfect; therefore, SS patients surely deserve closer follow-up, with attentive evaluation of the aforementioned risk factors, including cryoglobulin-related markers and increased EULAR SS disease activity index (ESSDAI), to more accurately identify patients at higher risk for SS-associated NHL [30,31].

As to primary breast lymphomas (PBLs), they are usually detected as palpable masses, associated or not with axillary lymph node enlargement, thus mimicking breast carcinoma or other breast neoplasms [32]. Furthermore, notwithstanding several attempts, no specific clinical or imaging patterns have been reported for breast lymphomas [33–36]. Radiologically, as for the case reported herein, PBL more commonly resembles inflammatory lesions, such as lymphocytic mastitis [37], IgG4-related sclerosing mastitis [38] and cutaneous lymphoid hyperplasia [39].

Consequently, the diagnosis of breast MZL of MALT usually is based on morphologic examinations. At this regard, fine needle aspiration cytology, a minimally invasive procedure, was proven effective to accurately diagnose the most common non-neoplastic (e.g., fibrocystic disease) and neoplastic (e.g., fibroadenoma and carcinoma) breast lesions at a pre-operative stage; nevertheless, such a diagnostic procedure may be of limited value when dealing with lymphoid proliferations, which may not show unequivocal morphologic features or may require extensive immunohistochemical investigations

to achieve the final diagnosis. Consequently, histological preparations are more frequently adopted in such cases, which may allow proper morphologic evaluation of the lymphoid populations, appropriate immunohistochemical characterization, along with the possible detection of genetic alterations by in situ hybridization techniques, whenever deemed necessary. In the current case, all necessary morphologic and ancillary procedures could be carried out, even if dealing with small tissue fragments, thus highlighting the appropriateness of core needle biopsy as a diagnostic tool for PBL.

Based on the data available in the literature about PBL-SS association [40–44], and the current theories about lymphoma prevalence at immune-privileged sites [45], we can assume that the diagnosis of SS-related lymphoid proliferations, especially when occurring in the breast, currently is very challenging and would probably benefit from wider studies including SS patients with prolonged follow-up (>10 years). Therefore, we may suggest more attentive monitoring for lymphoma development in those SS patients who display higher risk factors (such as palpable purpura, low C4, mixed monoclonal cryoglobulinemia) and to incorporate breast surveillance in such patients.

**Author Contributions:** Conceptualization, S.C., G.I., E.M., G.F.; methodology, G.F.A. and, P.T.; validation, M.M., M.G.M., S.C., E.M.; investigation, G.I., E.M., M.G.M., M.M.; resources, P.T. and G.F.A.; writing—original draft preparation, S.C. and G.I.; writing—review and editing, E.M. and G.F.; visualization, V.D.R., G.F., L.L.; supervision, G.I., S.C., E.M., G.F. All authors have read and agreed to the published version of the manuscript.

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

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


*Edited by Aamir Shahzad*

Plasma science and technology (PST) is a discipline investigating fundamental transport behaviors, interaction physics, and reaction chemistry of plasma and its applications in different technologies and fields. Plasma has uses in refrigeration, biotechnology, health care, microelectronics and semiconductors, nanotechnology, space and environmental sciences, and so on. This book provides a comprehensive overview of PST, including information on different types of plasma, basic interactions of plasma with organic materials, plasma-based energy devices, lowtemperature plasma for complex systems, and much more.

Published in London, UK © 2022 IntechOpen © Halgatewood.com / unsplash

Plasma Science and Technology

Plasma Science

and Technology

*Edited by Aamir Shahzad*


**2. The evolution of Ankara and Ataturk Forest Farm, based on the development plan**

Ankara was founded in an area that could fit into a single photo frame when it was declared the capital (**Figure 1**) [2].

Ankara is an arid city in the middle of Anatolia, the symbol of the modern Turkish Republic and its window to the world. Ankara's development plan and practices shaped the new form of the Turkish Republic, as well as the city itself.

**Figure 1.** *An old view from the Atatürk forest farm.*

The development plan considered the impact on the city of biotic and abiotic factors, such as climate, fauna, flora, geology, geomorphology, and soil. Its approach also observed the impact of the city on people and the environment.

One of the most important factors here is undoubtedly the population projection, indicating how many people are estimated to be present in a certain place as well as the dimensions of this place. The number of people living in the Turkish Republic was 13,648,000 during those years, and the first census was performed for the first time after the proclamation of the republic was performed on October 28, 1927. Ankara's urban population was 74,784, of whom 49,439 were male and 25,345 were female [3]. Since the number of people living in rural areas of the Turkish Republic was 76% of the total population, a total population of 250,000–300,000 people was estimated for the whole Ankara area.

Carl Lörcher, a German architect and planner, had prepared two separate plans for the old and new city in 1924 and 1925 for Ankara's development between 1925 and 1930. However, the extent of his spatial planning studies and the housing conditions suggested in the plans were not adequate for forming a living area for 250–300,000 people, so a new development plan was needed. Exceeding the aforenoted population projection considered for Ankara's development plan between 1925 and 1930 forced the city to undergo spatial expansion, and new development plans were needed. Accordingly, an international contest was held and won in 1928 by Hermann Jansen in 1928 [4].

During these years, the farms called Orman, Balgat, Yağmur Baba, Macun, Güvercinlik, Ahimesud (Etimesud) and Çakırlar, constitute 80,000 decares of Ataturk Forest Farm land (102,000 decares in total), were purchased from the relevant owners using Ataturk's account number 2 in İş Bank [5]. By 1932, Hermann Jansen delivered the final development plans, and the surface area of Ataturk Forest Farm covered a larger area than the macro form of Ankara [6]. Ataturk Forest Farm reached its maximum size of 102,000 decares in the 1930s, but Ankara continued its urban development. This process can be described with the following comparison: Ataturk Forest Farm is greater than Ankara, and Ankara is greater than Ataturk Forest Farm from the period between 1930 and current times. Two important objectives of the Republic of Turkey: the agricultural development Mustafa Kemal conducted with Ataturk Forest Farm, and ensuring the urban development of Ankara that started with the plans of Hermann Jansen and that was based on modern and scientific grounds.

*Introductory Chapter: Understanding the Ataturk Forest Farm from an Ecological Perspective DOI: http://dx.doi.org/10.5772/intechopen.102880*

#### **2.1 Agriculture and Ataturk Forest Farm**

Agricultural development was one of the most important objectives of the Turkish Republic during the 1920s because most of the country's total imports consisted of main food elements, such as wheat. During those years, more than half of the total exports consisted of agricultural products, such as cotton, figs, grapes, hazelnuts, and tobacco [5].

Ataturk explained his purpose in founding Ataturk Forest Farm as follows: "The basis of the national economy is agriculture. Therefore, we attribute great importance to agricultural development. The planned and practical activities that will spread into the villages will facilitate adopting this objective. However, to gain success in this purpose, a research-based agricultural policy should be formed and an agricultural regime that every villager and citizen can easily understand and practice should be established" [7]. Ataturk reveals the notion behind the establishment of Ataturk Forest Farm with this statement.

Ataturk formed a group of Turkish agricultural experts and told them that *he wanted to establish a large farm in Ankara*, asking them to find a suitable location. One of the experts in the group stated: "We did not need to search through the entire city of Ankara and look for different natural characteristics. The reason was simple. A medieval city in the middle of an arid steppe… No trees, water, nothing… While looking for a place to establish a farm around Ankara, we focused on the current location of the farm the least. This place was not treated kindly by nature; it looked neglected and pale and made you feel pessimistic when you looked at it. The reeds in the swamp of the land through which trains pass poisoned the urban life made the people living around appear as pale and sick and acting as a source of malaria. Eagles and vultures made nests around the land, which had nothing but an adobe house back then. There was only a thin railroad as the sign of civilization and humanity on this land. When our assessments were complete, we submitted the result to the Great Chief. Ataturk showed the current location of the farm and asked: Did you visit there? We extended our common thought that the location he pointed out had none of the qualities needed for a farm and that it was only a swamp with arid sections. He replied as follows: This is what we want. A swamp, arid, poor location around Ankara. It is up to us, rather than anybody else, to physically improve that location" [5].

Ataturk's own words show that he targeted agricultural amendment before agricultural activities. Agricultural undertakings are accomplished by finding a suitable location for the pre-determined agricultural product and conducting agricultural activities in that location. However, as noted in the expert's report on Ataturk Forest Farm, Ataturk wanted to correct the land for agriculture first and only later to conduct agricultural activities.

The assessments of agriculture experts were based on the probability of performing agricultural activities on the farm. After determining the location of the modern farm land, some experts claimed it was unsuitable for any agricultural activity, but some admitted that, with stringent efforts, the land could be made suitable. For instance, Schmid, an expert from the Ministry of Agriculture, stated: "This is such a plan that either patience or money will be consumed on these arid lands and in these climate conditions" [5]. His statement is supported by the detailed soil analyses provided below, which were performed in 1926. These soil analyses indicate that the activities performed at Ataturk Forest Farm were conducted in the light of the most up-to-date data at the time.

ARID SOIL (Source [8]:) Chemistry Laboratory Number 32/42

1926 ANALYSIS REPORT Sent by: Directorate of Forest Farm. Analyzed: Soil sample. Amount: Approximately 10 kilograms. Result of mechanical analysis:

Different experts analyzed the lands of Ataturk Forest Farm at different times, and the analyses revealed that the soil structure of the farmland was not homogeneous. Therefore, different agricultural activities were performed on the different soil types.

Agriculture is affected by all climate parameters but the amount and timing of rainfall is the most important parameter. Water should be supplied, or water resources should be accessed to supplement rainfall and sustain agricultural activities.

Accordingly, Atatürk Forest Farm interventions to access water included the following:

*Introductory Chapter: Understanding the Ataturk Forest Farm from an Ecological Perspective DOI: http://dx.doi.org/10.5772/intechopen.102880*

Using irrigation increased productivity on Ataturk Forest Farm, and agricultural activities that suited the climate and land conditions were performed in certain parts of the Farm, although the infrastructure activities mentioned before were too expensive [5]. Ankara has been a model for agricultural activities. Due to the nature of agriculture, fruit productivity at Ataturk Forest Farm falls to minimum levels and significantly varies, year by year, due to climate issues, even though conservation actions are taken" [5].

### **3. Forestry and Ataturk Forest Farm**

Ataturk Forest Farm did not focus solely on agricultural activities; it also played a key role in growing trees that were conserved under the conditions of that era. By building a forest, *a unity of life collectively formed* by trees, shrubs, bushes, and herbaceous plants of a certain height, characteristics and density, kelp, Boston ferns and fungi, microorganisms living under and over the ground, and various bugs and animals, all of which can create a specific climate in the borders of the forest, is created [9].

The afforestation of Ataturk Forest Farm was greatly assisted by the *Forestry School* [10] opened in İstanbul in 1857. The first afforestation activity conducted in Turkey dates back to 1892. Aleppo pine, cedar, black pine, gallnut, and ash trees were planted in a 20–25 decare area. The fatigue arising from World War I and after the Turkish war of independence, the rapid occurrence of reforms, and limited financial resources, all meant little importance was attributed to forestry and afforestation till 1937. Afforestation of Ankara's Ataturk Forest Farm was renewed after World War II when the Yalova-Termal afforestation and Tarsus-Karabucak eucalyptus forest were among the important afforestation activities [11]. Various laws enacted between 1923 and 1937 enabled performing afforestation at Ataturk Forest Farm, but no legal actions regarding the control of flooding were taken during those years [12]. The micro-climatic impact expected from the afforestation activities performed at Ataturk Forest Farm has finally been realized today, although conditions have changed since those early days. The necessity of a challenging study can still be mentioned if the soil analyses provided above are to be assessed by modern forestry and agriculture experts.

#### **3.1 Ataturk Forest Farm being gifted to the treasury**

Mustafa Kemal Ataturk and his farmworkers made great efforts for 13 challenging years. At the end of this period, Ataturk thought about granting his properties and

goods to the Republican People's Party, which he founded, and he mentioned this idea during the congress of 1927 [5]. In 1937, Ataturk submitted a letter to the Speaker's Office at the Grand National Assembly of Turkey indicating his wish to donate the Ataturk Forest Farm, and he stated [5]: "As you know, I founded numerous farms in different parts of the country at different times to gain experiences in terms of agriculture. I give all these farms which hosted various agricultural arts as well as all sorts of agricultural products produced under relevant climates *during the challenging efforts lasting 13 years along with all equipment, animals, and fixtures to the treasury*. A brief list indicating the lands of farms, equipment, and fixtures is attached" [7].

Ataturk mentioned the 13 years of challenging effort spent building up the farm in the letter in which he donated Ataturk Forest Farm to the treasury—and the challenge and the effort remain even under modern conditions. Humanity's interest in (and opposition to, in certain cases) soil and climate continue to be manifested by forestry and agricultural activities. Nevertheless, it was the dedicated efforts of Ataturk and his farmworkers that turned this challenging process into a success story.

After 1937, the farmlands were managed by the government. In 1950, when Şemsettin Günaltay was the Prime Minister of Turkey, "The Law on the Foundation of Ataturk Forest Farm" was enacted. The 10th Article of the law states:

*The transfer and handover of Ataturk Forest Farm as well as the real estates within the border of the farm during the time this law was enacted to natural and legal persons and expropriation of the aforenoted are subject to receiving permission through a special law. Prior to the enactment of this law, the aforenoted shall not be applied to the real estates that were sold to the official offices and organizations through the approval of the Administrative Board of State Agricultural Business Department and Ministry of Agriculture [13].*

Accordingly, some sections of Ataturk Forest Farm were sold, and thus the surface area of the farmland shrank. The farm has continued shrinking physically because land from Ataturk Forest Farm was generally transferred in four ways: through a special law, through a protocol, through renting, and without law or protocol. The percentage distribution of the transferred land is as follows [14]:

Parts of Ataturk Forest Farm continued to be transferred for approximately 100 years. This was because a population of 250–300,000 people was estimated when planning the development of Ankara in the 1930s, but the actual figures were much higher. The most unexpected result of this process is that Ankara now has a population of more than 5.5 million people. This unexpected population increase put pressure not only on Ataturk Forest Farm but also on other natural resources. What would Hermann Jansen, the person who created the development plan of Ankara in the 1930s, say when he was told that the population of Berlin where he resided would remain at 3.5 million even after a century but that Ankara, the arid city in the middle of Anatolia, would have 5.5 million people 100 years later?

*Introductory Chapter: Understanding the Ataturk Forest Farm from an Ecological Perspective DOI: http://dx.doi.org/10.5772/intechopen.102880*

Considering the conditions of the era, neither Jansen nor anybody else would believe such a claim. While this unexpected population growth expanded Ankara, it shrank Ataturk Forest Farm. The process started during the era of Ataturk's contemporaries after Ataturk passed on. This transformation was not directed against Ataturk Forest Farm; instead, it was conducted in conjunction with efforts to maintain and improve the Republic of Turkey.

#### **3.2 Ecological impacts of Ataturk Forest Farm**

The transformation of urban spaces undoubtedly causes changes in environmental processes. In a 2009 study by Bilgili, the temperatures at different areal and green locations of Ankara were measured by fixed and mobile climate stations. Ataturk Forest Farm was statistically shown to be cooler than the built-up areas around Altınpark, Gençlik Parkı and Kurtuluş Parkı and was also cooler than the other green areas. The scientific findings varied according to the green areas, the micro-scale climate change caused by tall trees with large petal leaves in and around Ataturk Forest Farm, and the spatial (size), structural (pattern and plant cover), and temporal (phenological periods) traits of green areas. Therefore, the contribution of green areas to an urban ecosystem is a dynamic process caused by the collective impact of these areas' spatial, structural and temporal traits, and it changes over time. According to the Normalized Difference Vegetation Index, the plant cover of Ataturk Forest Farm, and of the forests surrounding the agricultural areas there, have a linear form, distinctively separate from the agricultural areas [15].

The trees planted approximately a century ago still have an impact on the climate parameters of Ankara in current times. Because it is in the center of the city, Ataturk Forest Farm's spatial traits that will create Ankara's green area system for the next century should be reassessed at this point.

## **Conflict of interest**

The authors declare no conflict of interest.


**9. The significance of family involvement in preschool education and care**

Family involvement is a critical component of preschool education and care. Research has consistently shown that when families are engaged in their children's learning, children are more likely to succeed academically, develop positive socialemotional skills and have better long-term outcomes. This is why it is vital for teachers and caregivers to actively seek out ways to involve families in the learning process [39]. One of the essential strategies for engaging families is regular communication. Teachers and caregivers should establish open lines of communication with families and share information about the child's learning and development. This can include daily updates, weekly newsletters, parent-teacher conferences and progress reports. It is also essential to be responsive to families' questions and concerns and to provide opportunities for families to share their insights and experiences.

Kuttner et al. [40] identified another effective way to involve families: inviting them to participate in classroom activities and events. This can include volunteer opportunities, parent workshops and family celebrations. Teachers and caregivers can create a sense of community and support by applying families in these activities and showing families they are valued partners in their child's education.

Finally, it is essential to recognise and respect families' diverse cultural backgrounds and perspectives. Teachers and caregivers should strive to create a culturally responsive learning environment that reflects and celebrates the diversity of the children and families they serve. This can include incorporating multicultural books and materials, inviting families to share their cultural traditions and practices and adapting curriculum and teaching strategies to meet each child's unique needs and interests [41].

In conclusion, family involvement is critical to preschool education and care. Teachers and caregivers can create a strong partnership that supports children's overall development and success by actively involving families in the learning process.

### **10. Quality standards for early childhood education and care programmes**

Quality standards are guidelines and criteria used to measure the quality of early childhood education and care programmes [42]. These standards ensure that children receive high-quality care and education that support their learning and development. Several quality standards are used in early childhood education and care programmes, including licencing, accreditation and programme standards.

Licencing standards are the minimum requirements a programme must meet to be licenced by the state or local government. These standards typically include provisions related to health and safety, such as staff-to-child ratios, background checks for staff and the cleanliness and security of the physical environment [43]. Accreditation standards are voluntary standards that programmes can meet to demonstrate their commitment to high-quality care and education. Accreditation standards are typically more rigorous than licencing standards and often include curriculum, teacher qualifications, family involvement and program administration requirements.

Programme standards are guidelines and criteria used to assess the quality of a programme's curriculum, teaching practices and overall program structure [44]. Programme standards may be developed by state or local government agencies, professional organisations, or advocacy groups. Some examples of quality standards in early childhood education and care programs include the National Association for the Education of Young Children's (NAEYC) accreditation standards, the Head Start Programme Performance Standards and the Quality Rating and Improvement Systems (QRIS) [45] used by many states. In addition to promoting high-quality care and education for children, quality standards provide a framework for continuous improvement. Programs that meet or exceed quality standards are more likely to provide positive outcomes for children, including improved academic and social-emotional development, and are better positioned to support the diverse needs of families and communities.

In conclusion, quality standards are essential to early childhood education and care programs. They help ensure that programs provide high-quality care and education supporting children's development and success. By meeting or exceeding quality standards, programs can demonstrate their commitment to continuous improvement and provide families with the confidence and reassurance that their children receive the best possible care and education.


**1. Introduction**

392 MATLAB – A Fundamental Tool for Scientific Computing and Engineering Applications – Volume 1

*Man and Cybernetics*, Vol. 31, No.3, pp:353-361.

*and Cybernetics—Part A*, Vol. 31, No. 1, pp: 59-66.

*Engineering*, Vol. 15, No. 4, pp:535-542.

129.

*Biomed. Eng*., pp. 1-22.

pp:71-80.

Huang, H.; Liu, Y. & Wong, C.(2003).Automatic EMG Feature Evaluation for Controlling a Prosthetic Hand Using a Supervised Feature Mining Method :An Intelligent Approach,

Momen K.; Krishnan, S*.* & Chau, T. (2007). Real-Time Classification of Forearm Electromyographic Signals Corresponding to User-Selected Intentional Movements for Multifunction Prosthesis Control, *IEEE Trans on Neural System and Rehabilitation*

Moradian, M. & Baraani, A.(2009). KNNBA: K-Nearest-Neighbor-Based-Association Algorithm. *Journal of Theoretical and Applied Information Technology*, Vol.6. No. 1, pp 123 –

Pal, N. & Ghosh, S. (2001).Some Classification Algorithms Integrating Dempster–Shafer Theory of Evidence with the Rank Nearest Neighbor Rules*, IEEE Trans on Systems, Man,*

Park, S. & Lee, S. (1998). EMG Pattern Recognition Based on Artificial Intelligence Techniques, *IEEE Trans on Rehabilitation Engineering*, Vol. 6, No. 4, pp:400-405. Parvin, H; Alizadeh, H. & Bidgoli, B. (2008). MKNN: Modified K-Nearest Neighbor, *Proceedings of the World Congress on Engineering and Computer Science*, pp:831-834. Perry, J.; Bekey, G. (1981).EMG-force relationships in skeletal muscle." *CRC Critical Rev. in*

Phinyomark, A.; Limsakul, C. & Phukpattaranont, P.(2009). A Novel Feature Extraction for Robust EMG Pattern Recognition, *Journal of Computing*, ISSN: 2151-9617, Vol.1, No. 1,

*International Conference on Robotics & Automation, Taiwan, IEEE*, pp:220-225, 2003. Kiguchi*,* K.; Watanabe*,* S.; Izumi, K. & Fukuda, T. (2001).An Exoskeletal Robot for Human Elbow Motion Support—Sensor Fusion, Adaptation, and Control, *IEEE Trans on System,*

> The quest for higher data rates in state-of-the-art wireless standards and services calls for wideband and high-resolution data-converters in wireless transceivers. While modern integrated circuits (IC) technologies provide high cut-off frequencies (்݂) for transistors and hence allow the operation at higher speeds, the main limitation against increasing speed of operation of data-converters is the problem of clock-jitter. Clock-jitter is a common problem associated with clock generators due to uncertainty in the timing of the clock edges caused by the finite phase-noise (PN) in the generated clock waveform. Particularly, noise components induced by several noise sources in the system providing the clock (e.g. phaselocked loop, PLL) add to the clock waveform and cause uncertainty in the timing of the zero-crossing instants from cycle to cycle. Figure 1 shows a survey chart of the analog-todigital converter (ADC) implementations reported in IEEE International solid-state circuits conference (ISSCC) and VLSI Symposium since 1997 [1]. The straight lines show the limitation on the achievable signal-to-noise ratio (SNR) by clock-jitter for jitter root-mean square (rms) values of 1ps and 0.1ps. As can be seen from the chart, the performance of most ADCs falls below the line corresponding to 1ps rms jitter, few ADCs reside in the range between 1ps and 0.1ps, and almost all ADC implementations reported so far are beyond the 0.1ps rms jitter line. This means that the main limitation on increasing the ADC performance in terms of SNR and speed is the specification on the clock-jitter of 0.1ps.

> Delta-sigma (ΔƩ) ADCs are the convenient choice in low power and state-of-the-art multistandard wireless receivers for two main reasons. First, they trade DSP for relaxed analog circuit complexity. Particularly, ΔƩ ADC implementations span analog and digital domains (ΔƩ pulse density modulation + digital decimation and filtering, as shown in Figure 2) and hence exploit DSP to relax hardware requirements on analog blocks. Thus, the simplified analog part (ΔƩ modulator) and the digital filtering can be efficiently reconfigured to fulfill

© 2012 Saad et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 Saad et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

performance requirements of different standards at minimum power consumption. Second, ΔƩ modulators use oversampling and hence trade speed for resolution. Specifically, for a given ΔƩ modulator and channel bandwidth (BW), higher effective number of bits (ENOB) can be achieved by increasing the oversampling ratio (OSR). This qualifies ΔƩ ADCs to benefit from increasing speeds of operation offered by advanced deep submicron CMOS technologies (maximum cutoff-frequency ்݂ ͵ͲͲݖܪܩ in 45nm [2]) to meet higher resolution requirements for modern and future wireless services at minimum power overhead.

**Figure 1.** ADC Performance Survey 1997-2012 [1]

Continuous-time (CT) ΔƩ ADCs are widely used in wideband low power wireless receivers [3, 4]. The CT operation of the loop filter relaxes the requirements on the gain-bandwidth product (GBW) of the adopted amplifiers and hence allows the operation at higher speeds or lower power consumption compared to discrete-time (DT) implementations. Also, CT loop filters offer inherent anti-aliasing and thus save the need for explicit anti-aliasing filter before the ADC. The requirements on the sample-and-hold (S/H) circuitry are also relaxed because the sampling is performed after the loop filter and hence the sampling errors experience the maximum attenuation offered by the loop (similar to quantization noise). However, CT ΔƩ modulators suffer from high sensitivity to clock-jitter in the sampling-clock of the digital-to-analog converters (DACs) in the feedback.

In this context, this chapter is intended to provide a comprehensive background and study for the effects of clock-jitter in the sampling-clocks of ΔƩ modulators. Also, Matlab/Simulink models for additive errors induced by clock-jitter in ΔƩ modulators are given so that to help designers characterize the sensitivities of various types of ΔƩ architectures to clock-jitter. The material in this chapter is organized as follows. Section 2 gives a general background about the types of errors caused by clock-jitter in different classes of switched circuits and signal waveforms. The critical sources of jitter induced errors in a ΔƩ loop are identified for DT and CT ΔƩ modulators and a comparison between the two types, in terms of sensitivity to clock-jitter, is done in Section 3. Section 4 provides detailed sensitivity analysis for CT ΔƩ modulators to clock-jitter in the feedback DAC sampling-clock. In Section 5, Simulink models, based on the analysis of Section 4, for the additive errors generated by clock-jitter in CT ΔƩ modulators are shown and the robustness of these models is verified by CT simulations in Matlab/Simulink. Simulations results show good agreement with the theoretical expectations. Finally, conclusions are drawn in Section 6.

**Figure 2.** ΔƩ ADC (ΔƩ pulse density modulation + digital decimation and filtering).

### **2. Jitter problems: Background**

394 MATLAB – A Fundamental Tool for Scientific Computing and Engineering Applications – Volume 1

overhead.

**Figure 1.** ADC Performance Survey 1997-2012 [1]

of the digital-to-analog converters (DACs) in the feedback.

performance requirements of different standards at minimum power consumption. Second, ΔƩ modulators use oversampling and hence trade speed for resolution. Specifically, for a given ΔƩ modulator and channel bandwidth (BW), higher effective number of bits (ENOB) can be achieved by increasing the oversampling ratio (OSR). This qualifies ΔƩ ADCs to benefit from increasing speeds of operation offered by advanced deep submicron CMOS technologies (maximum cutoff-frequency ்݂ ͵ͲͲݖܪܩ in 45nm [2]) to meet higher resolution requirements for modern and future wireless services at minimum power

Continuous-time (CT) ΔƩ ADCs are widely used in wideband low power wireless receivers [3, 4]. The CT operation of the loop filter relaxes the requirements on the gain-bandwidth product (GBW) of the adopted amplifiers and hence allows the operation at higher speeds or lower power consumption compared to discrete-time (DT) implementations. Also, CT loop filters offer inherent anti-aliasing and thus save the need for explicit anti-aliasing filter before the ADC. The requirements on the sample-and-hold (S/H) circuitry are also relaxed because the sampling is performed after the loop filter and hence the sampling errors experience the maximum attenuation offered by the loop (similar to quantization noise). However, CT ΔƩ modulators suffer from high sensitivity to clock-jitter in the sampling-clock

In this context, this chapter is intended to provide a comprehensive background and study for the effects of clock-jitter in the sampling-clocks of ΔƩ modulators. Also, Matlab/Simulink Since digital data is always available in DT form, then any process of converting information from analog form to digital bit-stream or vice versa entails sampling. However, the clock signals driving sampling switches suffers clock-jitter due to the noise components that accompany the clock waveform. Figure 3 shows the PN density in a typical voltagecontrolled oscillator (VCO)1. In the time-domain, the integrated effect of these noise components results in random variations in the phase of the generated clock signal. In dataconverters, the problem of clock-jitter is a very critical issue and can significantly deteriorate the achievable SNR by several dBs. The problems resulting from clock-jitter are classified as follows:

**1** The design of clock generators and the mechanisms of PN generation in PLLs are not within the scope of the material given in this chapter.

$$\begin{aligned} \text{le} \{ \text{n } T\_{\sf s} \} &= A \left\{ \sin \left[ \omega (\!(\!n \, T\_{\sf s} + \!\!n \, t \!\!(\!n \,) \) \right] - \sin \left( \!(\!\omega \, n \, T\_{\sf s}) \right) \right\} \\ &\approx A \, \omega \, \Delta t \text{(n) } \cos \{ \!\!n \, T\_{\sf s} \}. \end{aligned} \tag{1}$$

$$
\sigma\_e^2 = E\left(e^2\right) = \frac{\left(\mathbb{A} \text{ or } \sigma\_l\right)^2}{2} \tag{2}
$$

$$\lceil SNR \rceil\_{\text{Due to aperture filter}} = 10 \log \left( \frac{1}{\omega^2 \sigma\_j^2} \right) = 10 \log \left( \frac{1}{4 \, n^2 \, f^2 \sigma\_j^2} \right) \tag{3}$$

$$I\_n(t) = \begin{cases} I\_p \ e^{-(t-a)T\_s} & , \quad aT\_s < t < \beta T\_s, 0 \le a \le \beta \le 1 \\\\ 0 & , \quad \
otherwise \end{cases},\tag{4}$$

$$Q\_n = \int\_{aT\_\delta}^{\beta T\_\delta} I\_n(t) \, dt = \int\_{aT\_\delta}^{\beta T\_\delta} I\_P \, e^{\frac{-(t-a)T\_\delta}{\tau}} \, dt = -\tau A \, e^{\frac{-(t-a)T\_\delta}{\tau}} \Big|\_{aT\_\delta}^{\beta T\_\delta} = \tau A \left(1 - e^{\frac{-(\beta-a)T\_\delta}{\tau}}\right) \tag{5}$$

$$I\_P = \frac{q\_n}{r\left(1 - e^{\frac{-(\beta - a)T\_S}{t}}\right)}.\tag{6}$$

$$I\_n(t) = \begin{cases} \frac{q\_n}{t\left(1 - e^{\frac{-(\beta - a)T\_s}{t}}\right)} e^{\frac{-(t-a)T\_s}{t}}, aT\_s < t < \beta T\_s, 0 \le a \le \beta \le 1. \\\\ 0, otherwise. \end{cases} \tag{7}$$

$$e\_j(n) = \frac{Q\_n}{\tau \left(1 - e^{\frac{-(\beta - a)T\_s}{\tau}}\right)} \int\_{\beta T\_s}^{\beta T\_s + \Delta t(n)} e^{-\frac{(t-a)T\_s}{\tau}} dt = \frac{-Q\_n}{\left(1 - e^{\frac{-(\beta - a)T\_s}{\tau}}\right)} e^{\frac{-(t-a)T\_s}{\tau}} \Bigg|\_{\beta T\_s}^{\beta T\_s + \Delta t(n)}$$

$$= \frac{Q\_n}{\left(1 - e^{\frac{-(\beta - a)T\_s}{\tau}}\right)} \left[ e^{\frac{-(\beta - a)T\_s}{\tau}} - e^{\frac{-(\beta - a)T\_s - \Delta t(n)}{\tau}} \right]$$

$$= \frac{Q\_n}{\left(1 - e^{\frac{-(\beta - a)T\_s}{\tau}}\right)} e^{\frac{-(\beta - a)T\_s}{\tau}} \Bigg[1 - e^{-\frac{\Delta t(n)}{\tau}}\right]. \tag{8.7}$$

$$e\_f(n) \approx \frac{q\_n}{\pi \left(1 - e^{\frac{-(\beta - a)T\_\xi}{\pi}}\right)} e^{\frac{-(\beta - a)T\_\xi}{\pi}} \Delta t(n). \tag{9}$$

$$\sigma\_e^2 = E\{e^2\} = \left(\frac{q\_{rms}}{\tau \left(1 - e^{\frac{-(\beta - a)T\_s}{\tau}}\right)} e^{\frac{-(\beta - a)T\_s}{\tau}}\right)^2 \sigma\_f^2,\tag{10}$$

$$SNR|\_{Due to charge transfer \, jitter} = 10 \log \left( \frac{Q\_{rms}^2}{\left( \frac{Q\_{rms}}{\tau \left( 1 - e^{\frac{-(\beta - a)T\_2}{\tau}} \right)^2} e^{\frac{-(\beta - a)T\_2}{\tau}} \right)^2} \right)$$

$$= 10 \log \left( \frac{r^2 \left( 1 - e^{\frac{-(\beta - a)T\_2}{\tau}} \right)^2}{\left( e^{\frac{-(\beta - a)T\_2}{\tau}} \right)^2} \frac{1}{\sigma\_f^2} \right). \tag{11}$$

**Figure 7.** SNR variation with the input frequency due to charge transfer jitter for different rms jitter values. (a) � � �� �����. (b) � � �� ����. (c) � � �� ����.

The plots in Figure 7 show the limitation on the achievable SNR vs. the signal frequency due to charge transfer jitter for different values of the rms jitter ��. Typical values of � � ��� and ��� have been considered. The results are provided for � � �������, ������, and ������. As can be seen from the plots in Figure 7, for a given clock frequency, the SNR limitation due to charge transfer jitter is much more relaxed compared to the aperture jitter error (Figure 5). This result was expected because from equation (11), the effect of the jitter induced noise is reduced by an exponential factor indicating that charge transfer error in SC circuits should be less critical. This also can also be explained intuitively by noting that for the exponentially-decaying waveform in Figure 8, the amplitude of the pulse is rather low at the end of the clock-cycle and hence the amount of charge that varies over one clock period due to jitter is significantly reduced. However, for a given rms jitter and sampling frequency, the SNR limitation due to charge transfer jitter degrades as the discharging time-constant � increases. This is because the value of the charge transfer current at the end of the clockcycle (discharge phase) is varying exponentially with �, thus for a given timing error ��, the error in the amount of charge transferred is higher.

$$
\sigma\_e^2 = \sigma\_l^2 \ I\_\mathbb{S} \ ^2. \tag{12}
$$

$$
\sigma\_{signal}^2 = \frac{I\_S^{\ast^2} T\_s^{\ast^2}}{2}.\tag{13}
$$

$$SNR|\_{Due\ to\ pulse-worldth\ filter} = 10\ Log\ \left(\frac{\tau\_s}{2\sigma\_f^2}\right),\tag{14}$$

**(b)**

**(a)**

**(b)**

**Figure 9.** Pulse-width jitter in switched-current circuits.

**Figure 10.** SNR variation with the input frequency due to pulse-width jitter for different rms jitter values.

**Figure 11.** SNR variation with the sampling frequency due to different types of jitter induced errors for a rms jitter of 10 ps.

Thus, the SNR degradation by PWJ is less than that of the aperture jitter by a factor of ���. The plots in Figure 10 show the limitation on the achievable SNR vs. the signal frequency due to PWJ problem for different values of the rms jitter ��.

Figure 11 provides a comparative insight about the SNR limitation imposed by each one of the clock-jitter induced problems discussed above. It is worth noting that these plots are for Nyquist-rate sampling; however the foregoing analysis and results can be easily extended to include the effect of oversampling in oversampled circuits. As can be observed from the plots in Figure 11, for a given sampling frequency, the maximum limitation on the achievable SNR is caused by aperture jitter. However, the charge transfer jitter limits the SNR at very high frequencies; for example for an SNR of 80 dB, the charge transfer jitter starts to limit the achievable SNR at sampling frequency �� ≥ ����� for � � ������, and as mentioned before more robustness to charge transfer jitter at high frequencies can be obtained by reducing the discharging time-constant �.


*3.2. Analysis of the SSR Repeat Motif Types and Frequencies*

As shown in Figure 1E, the distributions of the SSRs were also examined from the aspect of the number of repeat units. It was observed that for all the SSR types, the SSR frequencies decreased as the number of repeat units increased. Meanwhile, the change rates became more gradual for the dinucleotides when compared with the longer repeat motif types. The dominant numbers of repeats in the pepper SSR loci ranged between 4 and 10, with the exception of a few (more than 10). At the same time, the majority of the observed repeat times were 6, accounting for 26.20% (45,076). The dinucleotides were found to be the most abundant number of repeats, accounting for 55.16% (94,916), whereas the number of repeats for remaining trinucleotide, tetranucleotide, pentanucleotide, and hexanucleotide were determined to be 30.58% (52,614), 9.50% (16,351), 2.63% (4531), and 2.13% (3660), respectively, as shown in Figure 1E.

**Figure 1.** Total number, distribution, frequency, and distinct motif types of SSRs in the entire genome of *C. frutescens*: (**A**) Distribution of the SSR in the 'Xiaomijiao' genome; (**B**) Number of dinucleotide and trinucleotide repeat motifs in the genome; (**C**) Number of tetranucleotide repeat motifs in the genome; (**D**) Distribution and frequency of the SSR loci in the genome; (**E**) Relative frequency (%) of the SSR types with different repeat numbers in the 'Xiaomijiao' genome.

*3.3. Primer Design of the Pepper Plant Genomic SSR Markers*

For the designing of the primers in the current study, a total of 240 SSRs, which were distributed on different chromosomes, were selected. Then, the reliability was evaluated on eight pepper cultivars. It was determined that out of the total primer sets tested, 41 were successfully amplified showing full length polymorphisms. The remaining 199 primers were found to be either non-polymorphic, non-specific amplification with ambiguous bands, or not amplified, as evidenced from the gel results. Of the 41 amplified primer sets, only 33 (13.75%) were found to have generated both polymorphic and unambiguous bands on the gel. Therefore, those primer sets were selected for further analysis, as detailed in Table 1. Among the 33 SSR loci, 1 was observed to be dinucleotides, 11 were trinucleotides, 4 were tetranucleotides, 4 were pentanucleotides, and 13 were hexanucleotides, respectively.


*Article* **Factors Affecting the Outcome of Periapical Surgery; a Prospective Longitudinal Clinical Study**

**Muhammad Adeel Ahmed 1,2,\* , Nouman Mughal 3,4, Syed Hani Abidi 4 , Muhammad Furqan Bari 5, Mohammed Mustafa 6, Fahim Vohra 7,8 and Ali Alrahlah 8,9**

**Abstract:** Wound healing following periapical surgery is influenced by age, gender, smoking, periapical lesion size, type of root-end filling, method of root-end cavity preparation and the use of microsurgical or conventional technique. This study aimed to evaluate the influence of various preoperative factors such as age, gender, smoking, preoperative pain, and preoperative periapical lesion size on the outcome of surgical endodontic treatment. A thorough history, examination, and investigation were performed to establish patient age, gender, smoking status, periapical lesion size, and the presence of preoperative pain. Forty patients aged between 15–57 years presented with persistent chronic apical periodontitis of single-rooted anterior teeth after conventional re-root canal treatment were enrolled for periapical surgery. Following periapical surgery, all patients were recalled for evaluation of periapical healing after 12 months based on clinical and periapical X-ray examination due to inaccessibility of an advanced imaging system (CBCT). Chi-squared and Fisher's exact test were applied, which revealed a statistically significant association of periapical healing with age (*p* = 0.025), smoking (*p* = 0.029), and lesion size (*p* < 0.001). Although, the success of periapical healing was higher in males 78.6% (22/28) compared to females 58.3% (7/12) however, no statistically significant relationship was found between gender and healing (*p* = 0.254). Patient age, smoking status, and size of the preoperative lesion had a strong influence on periapical healing after surgical endodontic treatment.

**Keywords:** periapical surgery; surgical endodontic treatment; preoperative factors; prospective study


*Communication* **A Fiber-Integrated CRDS Sensor for In-Situ Measurement of Dissolved Carbon Dioxide in Seawater**

**Mai Hu 1,2, Bing Chen 1 , Lu Yao 1 , Chenguang Yang 3 , Xiang Chen 1 and Ruifeng Kan 1,\***

**Abstract:** Research on carbon dioxide (CO2 ) geological and biogeochemical cycles in the ocean is important to support the geoscience study. Continuous in-situ measurement of dissolved CO2 is critically needed. However, the time and spatial resolution are being restricted due to the challenges of very high submarine pressure and quite low efficiency in water-gas separation, which, therefore, are emerging the main barriers to deep sea investigation. We develop a fiber-integrated sensor based on cavity ring-down spectroscopy for in-situ CO2 measurement. Furthermore, a fast concentration retrieval model using exponential fit is proposed at non-equilibrium condition. The in-situ dissolved CO2 measurement achieves 10 times faster than conventional methods, where an equilibrium condition is needed. As a proof of principle, near-coast in-situ CO2 measurement was implemented in Sanya City, Haina, China, obtaining an effective dissolved CO2 concentration of ~950 ppm. The experimental results prove the feasibly for fast dissolved gas measurement, which would benefit the ocean investigation with more detailed scientific data.

**Keywords:** seawater dissolved gas; carbon dioxide; optical cavity ring-down spectroscopy; in-situ measurement

#### **1. Introduction**

Marine carbon cycling is the result of a series of physical, geological and biological processes on a spatiotemporal scale [1,2]. The ocean absorbs one-third of the anthropogenic carbon emission, about 2 billion tons per year. As such, the ocean becomes one important place for carbon sequestration [3]. Furthermore, CO2 is the main greenhouse gas, the main dissolved gas of seawater and the main fluid component of the deep-sea extreme window of cold spring and hydrothermal solution. Precise measurement on its spatiotemporal distribution is significant to investigate the biogeochemical material cycle and global climate change [4,5]. However, common methods based on sampling-laboratory analysis are not enough to support modern marine science. In-situ CO2 sensors with high sensitivity, high fidelity, large dynamic range and fast response are highly needed in many cutting-edge research topics, such as the sea-air exchange flux of CO2 [6], CO2 concentration of deep-sea cold spring and hydrothermal fluid components [7–9], and isotope measurement [7,8,10,11].

Currently, optical technology, semiconductor gas sensing and mass spectrometry [8,9,11–13] are common methods in in-situ measurement of dissolved gas in seawater. Among them, laser-based in-situ optical spectrometer is suitable for greenhouse gas sensing in seawater due to its unique selectivity and sensitivity [12,14,15]. Using infrared spectroscopy technology, the German Hydro C company demonstrated dissolved CH4 measurement in seawater [16]. Using the off-axis integrated cavity output spectroscopy (OA-ICOS), the LGR Company in the United States measured the dissolved CH4/CO2 and

**Citation:** Hu, M.; Chen, B.; Yao, L.; Yang, C.; Chen, X.; Kan, R. A Fiber-Integrated CRDS Sensor for In-Situ Measurement of Dissolved Carbon Dioxide in Seawater. *Sensors* **2021**, *21*, 6436. https://doi.org/10.3390/ s21196436

Academic Editor: Jin Li

Received: 21 August 2021 Accepted: 24 September 2021 Published: 27 September 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/).

its isotope δ 13CH4 in seawater [11]. Using mid-infrared absorption spectroscopy technology, Zheng Chuantao et al. achieved the measurement of dissolved CO2 and its isotope δ 13CO2 [12]. In addition, cavity ring-down spectroscopy (CRDS), proposed by O, Keefe and Deacon in 1988 [17], has ultra-high detection sensitivity, light intensity jitter immunity and free instrument calibration, making CRDS one of the best candidates for gas detection. The past years have witnessed its remarkable progresses in precision spectroscopy [18–20] and important applications in atmospheric trace gas measurement [21–23]. However, the application of this technology in the marine field for in-situ measurement remains unresolved due to the challenges of high stability resonant cavity, high precision/low power circuit and time-consuming dissolved gas concentration retrieval.

In this paper, we report the development of an in-situ CRDS based dissolved CO2 sensor. A fast exponential regression model is proposed to retrieve the concentration of dissolved gas in seawater. In the implementation, we design high-pressure assembling and use polydimethylsiloxane (PDMS) membrane for water/gas separation and enrichment [24,25]. A long-time in-situ observation near the coast is carried out to prove the feasibility of in-situ separation, enrichment and measurement of dissolved CO2 in seawater.

#### **2. Principle of CRDS-Based Seawater Dissolved Gas Measurement**

#### *2.1. Water/Gas Separation and Enrichment, and Dissolved Gas Retrieval*

A PDMS membrane is one common tool, as the gas-liquid interface with a thickness of l, to separate and enrich seawater dissolved gases [26,27]. The concentration difference between both sides enables the dissolved gas pass through the membrane and blocks liquid water molecule (H2O)n. Thus, small gas molecules, such as CH4, CO2 and O2 can be separated from seawater. The water/gas separation of PDMS membrane is typically described by the "dissolution-diffusion" model [24,26]. When concentration difference between both sides exists, gas molecules diffuse into the membrane and realize gas exchange.

In the case of a stable situation, the dissolved gas concentration remains stable along the direction of film thickness. The diffusion flux on the side of the gas chamber can be expressed by Fick's first law [24,26,27]:

$$F\_G = \frac{D\_G S\_G A (P\_{G1} - P\_{G2})}{l} \,\tag{1}$$

where *FG* (cm3 ·cm2 (cm2polymer)−1 ·s −1 ) is the diffusion flux of gas component *G* per unit time, *DG* (cm2 ·s −1 ) is the diffusion coefficient of gas component *G* in the membrane, *SG* (cm3 ·(cm2polymer)−1 ·Pa−1 ) is the solubility coefficient of gas component *G* in the membrane, *PG*1 (Pa) is the partial pressure of seawater dissolved gas *G*, *PG*2 (Pa) is the partial pressure of gas component *G* in the gas chamber, *A* (cm2 ) is the film area, *l* (cm) is the film thickness. While gas diffusion flux can be expressed by Fick's second law in the case of unstable situation [25,26]:

$$F\_{\rm G,f} = F\_{\rm G,ss} \left( 1 + 2 \sum\_{n=1}^{\infty} (-1)^n \exp\left\{ \frac{-n^2 \pi^2 D\_{\rm G} t}{l^2} \right\} \right), \tag{2}$$

where, *FG*,*t* is the gas flux at time *t*, *FG*,*ss* is the gas flux in the stable situation.

The diffusion coefficient of CO2 in PDMS membrane is about 1.5 × 10−5 cm2/s, *l* 1 cm, *t* = 1 s, then expn −*n* 2*π* 2*DGt l* 2 o is almost zero and *FG*,*t* ≈ *FG*,*ss*. Thus, the diffusion flux in this case can also be described by Fick's first law. Therefore, the concentration change of the gas component *G* is as follows:

$$P\_{\rm G2} \* V = \int \frac{D\_{\rm G} S\_{\rm G} A (P\_{\rm G1} - P\_{\rm G2})}{l} d\_{t\nu} \tag{3}$$

where *V* is the volume of the gas chamber. From Equation (3), we can obtain: CH4, the value of − ಸௌಸ is independent from partial pressure.

where *V* is the volume of the gas chamber. From Equation (3), we can obtain:

ௗುಸమ ௗ

*Sensors* **2021**, *21*, x FOR PEER REVIEW 3 of 12

$$\frac{d\_{\rm F\_{G2}}}{d\_t} \* V = \frac{D\_{\rm G} S\_{\rm G} A (P\_{\rm G1} - P\_{\rm G2})}{l} \tag{4}$$

With the boundary condition, *t* → ∞, *PG*2 = *PG*1 , it will be extrapolated that:

ீଶ ∗= ಸௌಸ(ಸభିಸమ)

With the boundary condition, → ∞, ீଶ = ீଵ, it will be extrapolated that:

where *K* is related to the initial pressure. For non-condensable gases, such as CO2 and

ீଶ = ∗ exp ቀ− ಸௌಸ

∗= ಸௌಸ(ಸభିಸమ)

௧, (3)

, (4)

ቁ + ீଵ, (5)

$$P\_{G2} = K \* \exp\left(-\frac{D\_G S\_G A}{lV}t\right) + P\_{G1} \tag{5}$$

where *K* is related to the initial pressure. For non-condensable gases, such as CO2 and CH4, the value of − *DGSG A lV* is independent from partial pressure. decays to *Iout* due to gas absorption, which can be described by the Beer-Lamber law [10,17]:

After measuring the value of *PG*2 over time in an unbalanced situation, the exponential regression is used to retrieve *PG*1 . *PG*1 equals to *PG*2 in balanced situation. ௨௧ = ∗ exp(− ∗ ), (6)

#### *2.2. Optical Measurement Using CRDS* where, *α* (cm−1) is the spectral absorption coefficient, *L* (cm) is the interaction distance.

When laser with an intensity of *Iin* passes through uniform gas substance, the laser decays to *Iout* due to gas absorption, which can be described by the Beer-Lamber law [10,17]: In the configuration of CRDS, a couple of high reflectivity mirrors, usually higher than 99.99%, enable a significant interaction length extension inside a limited physical

$$I\_{\rm out} = I\_{\rm in} \* \exp(-\mathfrak{a} \* L),\tag{6}$$

where, *α* (cm−1 ) is the spectral absorption coefficient, *L* (cm) is the interaction distance. Its working principle is shown in Figure 1. When laser beam resonates with one resonant

In the configuration of CRDS, a couple of high reflectivity mirrors, usually higher than 99.99%, enable a significant interaction length extension inside a limited physical space [28], and become capable of detecting minor absorption of trace gas concentration. Its working principle is shown in Figure 1. When laser beam resonates with one resonant cavity mode, the laser power inside the cavity will be rapidly built up. With a trigger that the inside laser power reaches a certain threshold, the incident laser is quickly cut off to generate a free intracavity ring down. The leaked laser intensity *Iν*(*t*) after each ring down is successively recorded at the exit to obtain optical intensity that decays with time as follows, cavity mode, the laser power inside the cavity will be rapidly built up. With a trigger that the inside laser power reaches a certain threshold, the incident laser is quickly cut off to generate a free intracavity ring down. The leaked laser intensity ఔ() after each ring down is successively recorded at the exit to obtain optical intensity that decays with time as follows, ఔ(t) = ఔ ∗ exp ቆ− ௧ (1−+ఔ௦)ቇ, (7)

$$I\_{\nu}(\mathbf{t}) = I\_{0\nu} \* \exp\left(-\frac{\text{tc}}{L\_{\text{mirror}}}(\mathbf{1} - \mathbf{R} + a\_{\nu}L\_{\text{mirror}})\right),\tag{7}$$

where, *c* is the speed of light, *t* (µs) is the time, *Lmirrors* (cm) is the physical distance between the two mirrors, *R* is the reflectivity of the optical cavity mirror, *αν* (cm−1 ) is the spectral absorption coefficient of the specific wavelength; *I*0*ν* is the initial laser intensity when the laser is cut off. the two mirrors, *R* is the reflectivity of the optical cavity mirror, ఔ (cm−1) is the spectral absorption coefficient of the specific wavelength; ఔ is the initial laser intensity when the laser is cut off.

**Figure 1.** Schematic of the cavity ringdown technique. M1 and M2: cavity mirrors with high reflectivity (>99.99%). **Figure 1.** Schematic of the cavity ringdown technique. M1 and M2 : cavity mirrors with high reflectivity (>99.99%).

The time for the initial laser intensity reduces to 1/e in the measurement is defined as the ring-down time [17]. According to Equation (7), in the case of no gas absorption, the empty cavity ring-down time *τ*0 is:

$$\pi\_0 = \frac{L\_{\text{mirror}}}{\mathbb{C}(1-R)'} \tag{8}$$

In the presence of gas absorption, the ring-down time *τν* is:

$$\pi\_{\nu} = \frac{L\_{\text{mirror}}}{\mathcal{C}(1 - R + \alpha\_{\nu} L\_{\text{mirror}})} \,\text{}\,\text{}\tag{9}$$

Combining (8) and (9), we obtain the intracavity spectral absorption coefficient as:

$$\mathfrak{a}\_{\nu} = \frac{1}{\mathbb{C}\mathfrak{r}\_{\nu}} - \frac{1}{\mathbb{C}\mathfrak{r}\_{0}},\tag{10}$$

In the absorption spectrum [29], the absorption coefficient can be expressed as:

$$\alpha\_{\boldsymbol{\nu}} = \mathcal{S}(\boldsymbol{T}) \* P\_{\text{total}} \* X \* \boldsymbol{\psi}(\boldsymbol{\nu}), \tag{11}$$

where *S*(*T*) (cm−2 ·atm−1 ) is the intensity of the absorption line, *Ptotal* (atm) is the total pressure of the gas, *X* is the molecular concentration, *ψ*(*ν*) (cm) is the absorption line shape function.

Since integral of *ψ*(*ν*) over *ν* is equals 1, i.e., R +∞ −∞ *ψ*(*ν*)*dν* = 1, *S*(*T*) relates to the temperature for a specific absorption line, and *Ptotal* can be measured using a commercial pressure sensor. Therefore, after fitting the absorption coefficient curve to obtain the integrated area *A*, the partial pressure of the gas can be calculated by:

$$P = XP\_{total} = \frac{A}{S(T)}.\tag{12}$$

Thus, the partial pressure of dissolved gas (*PG*1 ) can be retrieved by measuring the partial pressure of gas (*PG*2 ) in the cavity and fitting formula (5).

#### **3. In-Situ Dissolved CO2 Sensor Configuration**

Figure 2 depicts the schematic diagram of the optical dissolved-CO2 sensor, which comprises three parts, one pressured chamber, one water/gas separation and enrichment unit and one gas measurement unit. The first part, i.e., the pressure chamber, is a dry titanium alloy chamber with an inner diameter of 128 mm, a length of 750 mm and a wall thickness of 10 mm. It can withstand a pressure as high as 57 MPa, which is suitable for experiments at about 4500 m under water. The second part, i.e., the water gas separation and enrichment unit consists of a water pump (SEA-BIRD SBE-5T), a PDMS membrane module (membrane thickness 50 µm, diameter 5 cm, gas separation efficiency 0.034 mL/min at 296 K and 1 atm pressure difference), a drying box, an air pump (KNF NMP05), a filter (Swagelok, SS-2F-05), two needle valves (Swagelok, SS-ORS2) and the gas chamber (optical resonant cavity). With the water pump, the seawater continuously flows through the surface of PDMS membrane at a flow rate of 0.8 L/min, forming a stable dissolved gas concentration field on the surface of the membrane. The dissolved gas permeates into the PDMS membrane. On the other side of the membrane, the permeated gas is desiccated by the drying box, then enters the gas chamber through the needle valve 1 and the filter, and finally returns to the PDMS membrane module through the filter, the needle valve 2 and the gas pump. Thus, water/gas separation and enrichment can be completed. The needle valve permits a flow rate of approximately 50 mL/min.

The third part, i.e., the gas detection unit, includes a control circuit (DSP, TMS320C6748, Texas Instruments, Dallas, TX, USA), a DFB laser (NLK1L5GAAA, NEL, Yokohama, Japan), a semiconductor optical amplifier (SOA, BOA1080P, Throlabs, USA), an isolator (PIISO-

1600-D-L-05-FA, Qinghe Photonics, Shenzhen, China), an optical resonator and a photoelectric detector (GPD, GAP1000FC, GPD Optoelectronics, Salem, MA, USA). Sawtooth signal and step signal generated by the control circuit are combined to drive the laser to achieve laser beam resonance enhancement inside the optical resonator. When the enhanced intracavity laser power, monitored by the detector, exceeds a certain threshold, the incident laser is rapidly cut off using the TTL driven SOA to generate ring-down signal. With the ring down signal recorded, a fast single exponential fitting is performed to calculate the ring down time. After 20 recordings of ring-down time for a single longitudinal mode, the step voltage is reset to match the laser to next longitudinal mode until the whole absorption line of CO2 is covered. With the averaged absorption spectra, the CO2 absorbance and concentration are calculated by spectral fitting algorithm. Finally, the concentration of the seawater side gas is retrieved according to the exponential fitting in the unbalanced situation. *Sensors* **2021**, *21*, x FOR PEER REVIEW 5 of 12

**Figure 2.** Framework of in-situ underwater instrument for dissolved gas measurement. **Figure 2.** Framework of in-situ underwater instrument for dissolved gas measurement.

#### *3.1. Absorption Line Selection*

The third part, i.e., the gas detection unit, includes a control circuit (DSP, TMS320C6748, Texas Instruments, Dallas, TX, USA), a DFB laser (NLK1L5GAAA, NEL, Yokohama, Japan), a semiconductor optical amplifier (SOA, BOA1080P, Throlabs, USA), an isolator (PIISO-1600-D-L-05-FA, Qinghe Photonics, Shenzhen, China), an optical resonator and a photoelectric detector (GPD, GAP1000FC, GPD Optoelectronics, Salem, MA, USA). Sawtooth signal and step signal generated by the control circuit are combined to drive the laser to achieve laser beam resonance enhancement inside the optical reso-Appropriate absorption line with high absorption intensity and less background interference can benefit the dissolved CO2 measurement with high signal-to-noise ratio. Considering the application condition in seawater dissolved gas measurement, absorption spectra of CO2, 13CO2, H2O and CH4 within 1599.4–1599.7 nm is simulated based on the HITRAN database [30] (temperature: 296 K, gas pressure: 1.01 × 105 Pa, CO2 = 400 ppm, 13CO2 =, H2O = 2% and CH4 = 2 ppm). The simulation results, shown in Figure 3, illustrate that CO2 absorption coefficient is 2.5 ×10−7 cm−1 with negligible interference. Thus absorption transition R(36) at 6251.761 cm−1 is selected for the following CO2 measurement.

#### nator. When the enhanced intracavity laser power, monitored by the detector, exceeds a certain threshold, the incident laser is rapidly cut off using the TTL driven SOA to gen-*3.2. Optical Resonator Design*

exponential fitting in the unbalanced situation.

*3.1. Absorption Line Selection*

ment.

erate ring-down signal. With the ring down signal recorded, a fast single exponential fitting is performed to calculate the ring down time. After 20 recordings of ring-down time for a single longitudinal mode, the step voltage is reset to match the laser to next longi-The optical resonator is shown in Figure 4. Two identical cavity mirrors M1 and M2 (Layertec) have a diameter of 12.7 mm, a radius of curvature of 1000 mm and a reflectivity of higher than 99.99% (@1500~1700 nm). Lens1 and Lens2 are adjustable focus aspherical collimators (CFC-8X-C, Throlabs, Newton, NJ, USA). To suppress the high-order modes,

tudinal mode until the whole absorption line of CO2 is covered. With the averaged ab-

Appropriate absorption line with high absorption intensity and less background interference can benefit the dissolved CO2 measurement with high signal-to-noise ratio. Considering the application condition in seawater dissolved gas measurement, absorption spectra of CO2, 13CO2, H2O and CH4 within 1599.4–1599.7 nm is simulated based on the HITRAN database [30] (temperature: 296 K, gas pressure: 1.01 × 105 Pa, CO2 = 400 ppm, 13CO2=, H2O=2% and CH4 = 2 ppm). The simulation results, shown in Figure 3, illustrate that CO2 absorption coefficient is 2.5 ×10−7 cm−1 with negligible interference. Thus absorption transition R(36) at 6251.761 cm−1 is selected for the following CO2 measurevapor and CH4.

the laser beam is spatially filtered using a single-mode fiber before illuminating on the photodetector, achieving a high-order modes suppression ratio of better than 100:1. It should be noted that, most of the optical and mechanical components of the optical cavity are fixed with structural adhesive to adapt to the extreme marine environment and improve the system stability. *Sensors* **2021**, *21*, x FOR PEER REVIEW 6 of 12

*Sensors* **2021**, *21*, x FOR PEER REVIEW 6 of 12

**Figure 3.** Simulation of CO2 absorption feature and background interference from of 13CO2, H2O vapor and CH4. **Figure 3.** Simulation of CO2 absorption feature and background interference from of 13CO2 , H2O vapor and CH4 . ment and improve the system stability.

modes, the laser beam is spatially filtered using a single-mode fiber before illuminating on the photodetector, achieving a high-order modes suppression ratio of better than **Figure 4.** Framework of the optical resonant cavity, SMF: Single mode fiber; M1 and M2: cavity mirrors. Lens1 is a resonant cavity mode matching lens for mode matching of a laser-beam to a **Figure 4.** Framework of the optical resonant cavity, SMF: Single mode fiber; M1 and M2: cavity mirrors. Lens1 is a resonant cavity mode matching lens for mode matching of a laser-beam to a high finesse optical cavity. Lens2 is the light converging lens for coupling light into fiber.

#### 100:1. It should be noted that, most of the optical and mechanical components of the optical cavity are fixed with structural adhesive to adapt to the extreme marine environhigh finesse optical cavity. Lens2 is the light converging lens for coupling light into fiber. *3.3. Longitudinal Mode Matching and Spectral Scanning*

ment and improve the system stability. **Laser Source SMF Optical cavity M1 M2 SMF Photodetector Lens1 Lens2 Figure 4.** Framework of the optical resonant cavity, SMF: Single mode fiber; M1 and M2: cavity mirrors. Lens1 is a resonant cavity mode matching lens for mode matching of a laser-beam to a *3.3. Longitudinal Mode Matching and Spectral Scanning*  The physical distance between the two cavity mirrors is 420 mm, corresponding to a free spectral range (FSR) of 0.0119 cm−1. For each measurement using the continuous-wave CRDS system, the laser frequency is adjusted to resonate with one cavity mode. Since the cavity length remains stable during the measurement, the longitudinal cavity modes are used as the frequency reference to depict spectral absorption curve. To avoid potential spectral distortion from the longitudinal mode leakage during the spectral scanning process, we propose a strategy of longitudinal mode matching in a stepwise manner shown in Figure 5, and the step size is set as 1/5 FSR. Ideally, when the laser resonates with the longitudinal cavity mode q, 5 steps can rematch laser and the resonant cavity. However, it can be hardly realized due to the laser wavelength drift and cavity The physical distance between the two cavity mirrors is 420 mm, corresponding to a free spectral range (FSR) of 0.0119 cm−1 . For each measurement using the continuous-wave CRDS system, the laser frequency is adjusted to resonate with one cavity mode. Since the cavity length remains stable during the measurement, the longitudinal cavity modes are used as the frequency reference to depict spectral absorption curve. To avoid potential spectral distortion from the longitudinal mode leakage during the spectral scanning process, we propose a strategy of longitudinal mode matching in a stepwise manner shown in Figure 5, and the step size is set as 1/5 FSR. Ideally, when the laser resonates with the longitudinal cavity mode q, 5 steps can rematch laser and the resonant cavity. However, it can be hardly realized due to the laser wavelength drift and cavity vibration. Differently, a high frequency sawtooth modulation (amplitude, 1/4FSR ≤ M ≤ 2/5FSR) is simultaneously superimposed on the step signal to ensure the resonance of each cavity mode with the laser. In the implementation of this strategy, each longitudinal mode resonates at least once in 5-step scanning, and at least one step does not resonate. The discrete spectrum can be obtained by taking the non-resonant step as a marker.

high finesse optical cavity. Lens2 is the light converging lens for coupling light into fiber.

free spectral range (FSR) of 0.0119 cm−1. For each measurement using the continuous-wave CRDS system, the laser frequency is adjusted to resonate with one cavity mode. Since the cavity length remains stable during the measurement, the longitudinal cavity modes are used as the frequency reference to depict spectral absorption curve. To avoid potential spectral distortion from the longitudinal mode leakage during the spectral scanning process, we propose a strategy of longitudinal mode matching in a stepwise manner shown in Figure 5, and the step size is set as 1/5 FSR. Ideally, when the laser resonates with the longitudinal cavity mode q, 5 steps can rematch laser and the resonant cavity. However, it can be hardly realized due to the laser wavelength drift and cavity vibration. Differently, a high frequency sawtooth modulation (amplitude, 1/4FSR ≤ M ≤ 2/5FSR) is simultaneously superimposed on the step signal to ensure the resonance of each cavity mode with the laser. In the implementation of this strategy, each longitudinal mode resonates at least once in 5-step scanning, and at least one step does not resonate.

The discrete spectrum can be obtained by taking the non-resonant step as a marker.

*3.3. Longitudinal Mode Matching and Spectral Scanning*

The discrete spectrum can be obtained by taking the non-resonant step as a marker.

vibration. Differently, a high frequency sawtooth modulation (amplitude, 1/4FSR ≤ M ≤ 2/5FSR) is simultaneously superimposed on the step signal to ensure the resonance of

mode resonates at least once in 5-step scanning, and at least one step does not resonate.

*Sensors* **2021**, *21*, x FOR PEER REVIEW 7 of 12

**1/4FSR<M<2/5FSR**

**Figure 5.** Schematic of the stepwise spectral scanning. **Figure 5.** Schematic of the stepwise spectral scanning. **4. Experimental Results**

#### **4. Experimental Results** *4.1. Sensor Performance*

#### **4. Experimental Results**  *4.1. Sensor Performance* After assembling the sensor, calibrated CO2 sample with a certain concentration of

*4.1. Sensor Performance*  After assembling the sensor, calibrated CO2 sample with a certain concentration of 885 ppm (uncertainty, 1%) was sealed inside the gas chamber. The absorption spectrum of CO2 is obtained using the method described in Section 3.3. Voigt spectrum fitting was performed using a Python LMFIT-based program. Figure 6 presents a direct comparison After assembling the sensor, calibrated CO2 sample with a certain concentration of 885 ppm (uncertainty, 1%) was sealed inside the gas chamber. The absorption spectrum of CO2 is obtained using the method described in Section 3.3. Voigt spectrum fitting was performed using a Python LMFIT-based program. Figure 6 presents a direct comparison between raw spectrum data of and the fitting curve, and a residual error of 1.5 × 10−9 cm−1 . The SNR is calculated to be 500, corresponding to a detection limit of 1.8 ppm. 885 ppm (uncertainty, 1%) was sealed inside the gas chamber. The absorption spectrum of CO2 is obtained using the method described in Section 3.3. Voigt spectrum fitting was performed using a Python LMFIT-based program. Figure 6 presents a direct comparison between raw spectrum data of and the fitting curve, and a residual error of 1.5 × 10−9 cm−1. The SNR is calculated to be 500, corresponding to a detection limit of 1.8 ppm.

**Figure 6.** Comparison of the measured CO2 spectral data and Voigt fitting curve. **Figure 6.** Comparison of the measured CO2 spectral data and Voigt fitting curve.

**Figure 6.** Comparison of the measured CO2 spectral data and Voigt fitting curve. CO2 samples with different concentration of 2000 ppm, 1600 ppm, 1000 ppm, 500 ppm and 250 ppm were generated by diluting the calibrated 10000 ppm CO2 with pure CO2 samples with different concentration of 2000 ppm, 1600 ppm, 1000 ppm, 500 ppm and 250 ppm were generated by diluting the calibrated 10000 ppm CO2 with pure N2. The uncertainty for above samples is 1%, mainly introduced by the dilution system. The mass flow meter was used to control the inlet flow rate at 50 mL/min. A continuous CO2 samples with different concentration of 2000 ppm, 1600 ppm, 1000 ppm, 500 ppm and 250 ppm were generated by diluting the calibrated 10000 ppm CO2 with pure N2. The uncertainty for above samples is 1%, mainly introduced by the dilution system. The mass flow meter was used to control the inlet flow rate at 50 mL/min. A continuous measurement of each CO2 sample was performed over 45 min and the experimental results are shown in Figure 7a.

N2. The uncertainty for above samples is 1%, mainly introduced by the dilution system. The mass flow meter was used to control the inlet flow rate at 50 mL/min. A continuous

measurement of each CO2 sample was performed over 45 min and the experimental re-

sults are shown in Figure 7a.

sults are shown in Figure 7a.

data.

data.

**2000×10–6**

*Sensors* **2021**, *21*, x FOR PEER REVIEW 8 of 12

**Figure 7.** (**a**) Measured results of CO2 samples ranging from 250 ppm to 2000 ppm and (**b**) a linear fit to the measured **Figure 7.** (**a**) Measured results of CO2 samples ranging from 250 ppm to 2000 ppm and (**b**) a linear fit to the measured data. A linear fit, shown in Figure 7b, was thus performed to the measured data and the

A linear fit, shown in Figure 7b, was thus performed to the measured data and the R-square value was calculated to be 0.9999, illustrating a good linear response to the CO2 concentration from 250 ppm to 2000 ppm. The relative error of the measured values of the five groups of standard gases is less than 2.66%, which is consistent with the uncertainty of the standard gases. A linear fit, shown in Figure 7b, was thus performed to the measured data and the R-square value was calculated to be 0.9999, illustrating a good linear response to the CO2 concentration from 250 ppm to 2000 ppm. The relative error of the measured values of the five groups of standard gases is less than 2.66%, which is consistent with the uncertainty of the standard gases. R-square value was calculated to be 0.9999, illustrating a good linear response to the CO2 concentration from 250 ppm to 2000 ppm. The relative error of the measured values of the five groups of standard gases is less than 2.66%, which is consistent with the uncertainty of the standard gases. To prove the feasibility, a long-term (8 h) comparison experiment was carried out

To prove the feasibility, a long-term (8 h) comparison experiment was carried out between this sensor and one commercial land-based instrument CRDS instrument (G2201-i, Picarro). The gas pipelines of G2201-i and this sensor were connected together to guarantee the same the analyte was simultaneously and separately measured. The comparison result, shown in Figure 8, demonstrates that the measured CO2 concentration and its variation trend are consistent with each other and the maximum relative differ-To prove the feasibility, a long-term (8 h) comparison experiment was carried out between this sensor and one commercial land-based instrument CRDS instrument (G2201-i, Picarro). The gas pipelines of G2201-i and this sensor were connected together to guarantee the same the analyte was simultaneously and separately measured. The comparison result, shown in Figure 8, demonstrates that the measured CO2 concentration and its variation trend are consistent with each other and the maximum relative difference is within 1.3%. between this sensor and one commercial land-based instrument CRDS instrument (G2201-i, Picarro). The gas pipelines of G2201-i and this sensor were connected together to guarantee the same the analyte was simultaneously and separately measured. The comparison result, shown in Figure 8, demonstrates that the measured CO2 concentration and its variation trend are consistent with each other and the maximum relative difference is within 1.3%.

**Figure 8.** Comparison of this sensor to one commercial CRDS instrument (G2201-i, Picarro, Santa **Figure 8.** Comparison of this sensor to one commercial CRDS instrument (G2201-i, Picarro, Santa Clara, CA, USA). **Figure 8.** Comparison of this sensor to one commercial CRDS instrument (G2201-i, Picarro, Santa Clara, CA, USA).

Clara, CA, USA).

#### *4.2. Verification of Exponential Fit Retrieval Method for Dissolved CO2 Concentration* vue, WA, USA) is used to continuously cycle the solution through the PDMS module to

*4.2. Verification of Exponential Fit Retrieval Method for Dissolved CO2 Concentration*

Sample solutions are prepared by mixing 2000 ppm CO2 and pure N2 (purity:

99.99%) using two mass flow meters (AST10-DLCMX-100C-025-A2B2-4VE, Asert, Franklin, MA, USA), as shown in Figure 9a. A submersible pump (SBE-5T, SEA-BIRD, Belle-

*Sensors* **2021**, *21*, x FOR PEER REVIEW 9 of 12

Sample solutions are prepared by mixing 2000 ppm CO2 and pure N2 (purity: 99.99%) using two mass flow meters (AST10-DLCMX-100C-025-A2B2-4VE, Asert, Franklin, MA, USA), as shown in Figure 9a. A submersible pump (SBE-5T, SEA-BIRD, Bellevue, WA, USA) is used to continuously cycle the solution through the PDMS module to separate dissolved CO2 to be measured. PDMS module is connected to the chamber of in-situ measurement system via a stainless tube. After 12-h measurement, the exponential model combined with Levenberg Marquardt (LM) algorithm is performed on the measured data. Figure 9b depicts the results with a R-square of 99.84%, when gas-phase CO2 concentration in the chamber is lower than the dissolved CO2. Figure 9c depicts the results with a R-square of 98.71% when gas-phase CO2 concentration in the chamber is higher than the dissolved CO2. The coefficient *DGSG A lV* , independent from the gas concentration, inside and outside the membrane are 0.1389 and 0.1444, respectively. The coefficients of two different situations are consistent with each other with a small difference of 3.8%. Thus, the method of exponential fit for dissolved gas concentration proves to be validated. separate dissolved CO2 to be measured. PDMS module is connected to the chamber of in-situ measurement system via a stainless tube. After 12-h measurement, the exponential model combined with Levenberg Marquardt (LM) algorithm is performed on the measured data. Figure 9b depicts the results with a R-square of 99.84%, when gas-phase CO2 concentration in the chamber is lower than the dissolved CO2. Figure 9c depicts the results with a R-square of 98.71% when gas-phase CO2 concentration in the chamber is higher than the dissolved CO2. The coefficient ಸௌಸ , independent from the gas concentration, inside and outside the membrane are 0.1389 and 0.1444, respectively. The coefficients of two different situations are consistent with each other with a small difference of 3.8%. Thus, the method of exponential fit for dissolved gas concentration proves to be validated.

**Figure 9.** (**a**) Schematic of sample solution preparation and dissolved CO2 measurement system. Real-time concentration measurements when CO2 concentration in the cavity is (**b**) lower and (**c**) higher than the dissolved CO2, respectively. **Figure 9.** (**a**) Schematic of sample solution preparation and dissolved CO2 measurement system. Real-time concentration measurements when CO2 concentration in the cavity is (**b**) lower and (**c**) higher than the dissolved CO2 , respectively.

#### *4.3. In-Situ Detection of Dissolved CO2 in Seawater*

*4.3. In-Situ Detection of Dissolved CO2 in Seawater*  From 6 to 7 March 2021, an in-situ observation was carried out near the coast of Sanya Institute of Deep Sea, Chinese Academy of Sciences, Sanya, Hainan Province. From 6 to 7 March 2021, an in-situ observation was carried out near the coast of Sanya Institute of Deep Sea, Chinese Academy of Sciences, Sanya, Hainan Province. Figure 10 shows the observation location.

Figure 10 shows the observation location. At the beginning of measurement, about 1100 ppm CO2 was filled in the cavity to quickly balance the concentration inside and outside the membrane. Figure 11 shows the observation result, the whole measurement is divided into two unstable parts and one stable part. In the first unstable part (3 h), the measured concentration was higher than that of seawater dissolved CO2 due to the presence of 1100 ppm CO2 in the measurement chamber, and the CO2 diffused from the measurement chamber into seawater. The concentration of seawater dissolved CO2 is calculated to be 850 ppm. In the second stable part (9 h), the

gas concentration inside and outside the membrane remained equal, and the dissolved gas concentration fluctuated between 950 ppm to 980 ppm. In the third unstable part (8 h), the concentration of dissolved CO2 decreased and the CO2 in the cavity continued to diffuse into seawater. The concentration of dissolved CO2 in the water is calculated to be 808 ppm. Interestingly, these measured dissolved CO2 concentrations are much higher than the atmospheric CO2 concentration, about 400 ppm, mainly because of a number of yachts cruising near the coast and the tide phenomenon. *Sensors* **2021**, *21*, x FOR PEER REVIEW 10 of 12 Latitude : 18.217° Longitude: 109.487°

*Sensors* **2021**, *21*, x FOR PEER REVIEW 10 of 12

**Figure 10.** The in-situ test site is located at 18.217° north latitude and 109.487° east longitude. **Figure 10.** The in-situ test site is located at 18.217◦ north latitude and 109.487◦ east longitude. a number of yachts cruising near the coast and the tide phenomenon.

a number of yachts cruising near the coast and the tide phenomenon. **Figure 11.** The result of In-situ measurements near coast. **Figure 11.** The result of In-situ measurements near coast.

#### **5. Conclusions**

**High tide Low tide –192.559× Exp(–0.1421t)+850 Data1 978× Exp(–0.13976t)+808** We develop a fiber-integrated in-situ dissolved CO2 sensor using CRDS, in which a PDMS membrane is employed for water/gas separation and enrichment, and an exponential regression model is proposed for fast dissolve CO2 retrieval. The model feasibility is verified by performing a comparison test under two different situations, i.e., measurement

00:00 05:00 10:00 15:00 20:00

**Figure 11.** The result of In-situ measurements near coast.

chamber CO2 concentration is higher and lower than the dissolved CO2 concentration. Both their R-square of fitting are better than 98.5% and the difference of the two individually measured PDMS membrane coefficient *DGSG A lV* is only 3.8%. The whole absorption spectrum of CO2 can be obtained within 90 s and a detection sensitivity of 1.8 ppm has been achieved. A near coast in-situ measurement has been implemented over 24 h and provided regular fluctuation of dissolved CO2 concentrations, which is due to the tide phenomenon. Future efforts will be made to improve the corrosion resistance ability by using titanium alloy stainless steel as the pressured chamber material and to improve the gas separation efficiency by increasing the membrane surface area. Therefore, the developed senor could act as a promising tool to achieve high-precision detection of dissolved gas in seawater and then support the investigation on the ocean, such as vertical dissolved CO2 profile as deep as 4500 m and long-term dissolved CO2 monitoring under deep-sea extreme environment window, e.g., hydrothermal and cold spring.

**Author Contributions:** Conceptualization, M.H. and B.C.; methodology, M.H. and R.K.; software, M.H. and L.Y.; validation, M.H. and C.Y.; formal analysis, X.C.; investigation, X.C.; resources, C.Y.; writing—original draft preparation, M.H.; writing—review and editing, R.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the Scientific Instrument Developing Project of the Chinese Academy of Sciences (YJKYYQ20190037), the Second Comprehensive Scientific Investigation of the Qinghai-Tibet Plateau(2019QZKK020802), the National Key Research and Development Project (2019YFB2006003) and the National Natural Science Foundation of China (61805286).

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data that support the plots within this paper are available from the corresponding author on request basis.

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

#### **References**


**Fast Active Merging of Microdroplets in Microfluidic Chambers Driven by Photo-Isomerisation of Azobenzene Based Surfactants**

**Zain Hayat 1,†, Nizar Bchellaoui 1,‡, Claire Deo 2,§, Rémi Métivier 2, Nicolas Bogliotti 2, Juan Xie 2, Malcolm Buckle 3 and Abdel I. El Abed 1,\***

Received: 7 October 2019; Accepted: 28 October 2019; Published: 1 November 2019

**Abstract:** In this work, we report on the development of a newly synthesized photoactive reversible azobenzene derived surfactant polymer, which enables active and fast control of the merging of microdroplets in microfluidic chambers, driven by a pulsed UV laser optical stimulus and the well known *cis*-*trans* photo-isomerisation of azobenzene groups. We show for the first time that merging of microdroplets can be achieved optically based on a photo-isomerization process with a high spatio-temporal resolution. Our results show that the physical process lying behind the merging of microdroplets is not driven by a change in surface activity of the droplet stabilizing surfactant under UV illumination (as originally expected), and they suggest an original mechanism for the merging of droplets based on the well-known opto-mechanical motion of azobenzene molecules triggered by light irradiation.

**Keywords:** microdroplets; photo-isomerisation; photokinetics; opto-mechanics; conformational states